Reactive vs. Proactive Behavior: Examples, Benefits, and Tips

By Editorial Team on February 28, 2024 — 9 minutes to read

In our daily lives, we often encounter situations that require us to either react or be proactive. These two types of behavior can significantly impact our personal and professional success. This article will discuss reactive vs. proactive behavior, provide examples of each, explore their benefits, and offer tips on how to develop a more proactive approach to life.

  • Reactive behavior means responding to events, situations, or people after they have occurred or taken action. For example, you might wait for a problem to arise before addressing it, or respond to an email only when someone reminds you about it. While this approach can sometimes work, it frequently leaves you scrambling to mitigate the consequences or catch up on missed opportunities.
  • Proactive behavior refers to anticipating possible scenarios and taking action to prevent or handle them before they happen. For instance, regularly maintaining your car to avoid breakdowns, or setting weekly goals to ensure you stay on track with your projects. Proactive behavior allows you to be better prepared for challenges and take advantage of openings when they appear.

Defining Reactive and Proactive Behavior

Understanding reactive behavior.

Reactive behavior means that you’re responding to events and situations as they happen. It’s a style of behavior in which the individual reacts to their environment instead of creating the circumstances they desire.

For example, suppose you’re at work, and you suddenly receive an urgent email from your boss. In a reactive mode, you would immediately drop what you’re doing and respond to the email, even if it disrupts your day’s schedule. This type of approach can lead to increased stress and a feeling of always being on the defensive.

In a reactive mindset, you might also find yourself being more susceptible to external influences and opinions, making it harder for you to focus on your long-term goals. Because you’re responding to situations as they arise, you may end up spending more time fixing problems.

Understanding Proactive Behavior

Proactive behavior means taking intentional steps to control your life and reach your goals. Being proactive requires planning, preparation, and goal-setting, which in turn, enable you to anticipate and manage potential obstacles and challenges.

For example, if you know a deadline is approaching, you can get an early start on the project and tackle potential issues before they become major problems.

A proactive approach also includes developing your self-awareness , understanding your strengths and weaknesses , and identifying areas for growth . This helps you make better decisions, allocate resources effectively, and work on personal development.

Here are some examples of proactive behavior:

  • Setting and reviewing personal and professional goals regularly.
  • Seeking continuous learning and self-improvement opportunities.
  • Building strong relationships through networking and collaboration.
  • Creating a structured daily routine that promotes efficiency and productivity.
  • Accepting responsibility for your actions and learning from your mistakes.

Comparing Reactive and Proactive Approaches

Response to stimuli.

  • With reactive approach , you wait for events to unfold and then react to them. This means that you are constantly adapting to external factors, often without any control over the situation.
  • A proactive approach occurs when you anticipate potential situations and plan for them.

Impact on Decision-Making

  • The decision-making process is quite different in reactive and proactive approaches. With a reactive mindset, your decisions are often quick, short-term-oriented, and focused on solving the problem at hand. However, this can sometimes lead to less optimal long-term outcomes.
  • In contrast, proactive decision-making includes assessing a situation, evaluating potential outcomes, and preparing for the long term. By looking at the big picture, you are more likely to find effective solutions that address the core issue instead of just treating the symptoms.

Effects on Stress Management

How you manage stress heavily depends on whether you have a reactive or proactive mindset. With a reactive approach, you are more likely to feel stressed because you are constantly adapting to unforeseen circumstances. When you don’t have control over a situation, it can cause anxiety and negatively impact your overall well-being.

By adopting a proactive attitude, you can significantly reduce stress levels. This is because proactively addressing potential issues in advance provides a sense of control, order, and helps to prevent or minimize problems.

Examples of Proactive Behavior

In personal life.

In your personal life, being proactive means taking charge of your actions and planning for the future.

  • Setting long-term and short-term goals and actively working towards them helps you focus on what’s important and prioritize your time and energy.
  • Engaging in regular exercise and maintaining a healthy diet demonstrates your commitment to taking care of your body and well-being.
  • Developing a budget and saving money for emergencies or future expenses showcases that you’re prepared for unexpected situations and mindful of your financial goals.
  • Learning new skills or hobbies can help you grow as an individual, and it’s a great way to build resilience and adaptability.

In Professional Settings

In professional environments, practicing proactive behavior leads to efficiency, productivity, and the ability to face challenges head-on.

  • Taking initiative in completing tasks or proposing new projects shows you’re committed to contributing to the growth and success of the organization.
  • Actively seeking feedback from colleagues and supervisors demonstrates that you’re willing to learn from their insights and improve your performance.
  • Prioritizing tasks and managing your time effectively allows you to focus on what’s important, make better decisions, and be more productive.
  • Networking and building relationships with peers, supervisors, and clients shows that you’re proactive in maintaining and developing relationships crucial for your professional growth.

Benefits of Proactive Over Reactive

For individual growth.

When you adopt proactive behavior, it means you take charge of your life and the situations you encounter. By anticipating challenges and preparing for them, you can avoid getting caught off guard.

For example, imagine you are aware of an upcoming deadline. Instead of waiting until the last minute and reacting to the stress, you can plan your tasks and allocate time to complete the work ahead of the deadline. This approach helps you stay calm and focused, ultimately leading to personal growth.

One way to enhance your proactive behavior is by setting achievable goals. This way, you can track your progress and celebrate your successes.

For Team Dynamics

When working in a team, being proactive is an invaluable trait. It’s important because it promotes collaboration and harmony within the group. By identifying potential issues early and actively seeking solutions, you can prevent misunderstandings and conflicts that may disrupt team synergy.

For example, suppose you notice the group is falling behind on a project. Instead of waiting till someone points it out, you can initiate a team meeting to discuss strategies for getting back on track.

Strategies to Reduce Reactivity

Improving emotional intelligence.

One way to reduce reactivity is by improving your emotional intelligence . Emotional intelligence is the ability to recognize, understand, and manage your emotions and reactions. By doing so, it’s easier to respond to situations in a calm and controlled manner. For example, if someone criticizes your work, instead of reacting defensively or taking it personally, you might take a step back, evaluate the feedback, and decide how to move forward.

To improve your emotional intelligence, you can:

  • Practice self-awareness: Pay attention to your feelings and emotions, and try to identify what triggers them.
  • Cultivate empathy: Put yourself in others’ shoes and try to understand their perspective and feelings.
  • Regulate emotions: Practice techniques such as deep breathing, meditation, or mindfulness to help manage emotions in challenging situations.

Related: Emotional Intelligence (EQ) in Leadership [Examples, Tips]

Enhancing Time Management

Another strategy to reduce reactivity is by enhancing your time management skills . Poor time management can lead to last-minute decisions, increased stress, and reactive behavior. By effectively managing your time, you can plan your actions and avoid rushing into hasty decisions.

Some tips to improve time management include:

  • Prioritize tasks: Identify the most crucial tasks and complete them first.
  • Break tasks into smaller steps: Tackle larger tasks in more manageable chunks.
  • Limit distractions: Set specific times for checking email or browsing social media, and focus on tasks during your designated work time.
  • Use a planner or scheduling tool: Keep track of deadlines, appointments, and responsibilities in one place.

Strengthening Personal Discipline

Finally, strengthening your personal discipline is another key to reducing reactivity. When you’re disciplined, you’re less likely to make impulsive decisions and more likely to maintain focus on your goals. This means that even in emotionally charged situations, you can maintain self-control and make thoughtful choices.

To build personal discipline, consider the following suggestions:

  • Set clear goals: Identify what you want to achieve, both short and long term, and create a plan to work toward them.
  • Develop a routine: Establish daily habits that support your goals and help you stay focused.
  • Stay accountable: Share your goals with someone who can support and encourage you along the way.

Frequently Asked Questions

What are some key differences between proactive and reactive behaviors in personal development.

Proactive behavior means taking control of your actions and decisions, planning ahead, and being prepared for various situations. This approach helps you anticipate problems and come up with solutions before they occur. Reactive behavior, on the other hand, involves reacting to events as they happen. This can lead to a lack of preparation and feeling overwhelmed by situations. A key difference in personal development is that proactive individuals tend to have higher self-confidence, better time management, and increased adaptability to change.

Can you provide examples of how being proactive rather than reactive can benefit a business?

Being proactive in business means anticipating potential issues, making strategic decisions, and adapting to market changes. For example, a proactive business might allocate resources to research future market trends and stay ahead of competitors. This could involve developing new products or services, adapting to new technologies, or targeting emerging markets. Reactive businesses, on the other hand, might only react to market changes and trends after they have occurred, which can be less efficient and lead to missed opportunities.

How do proactive and reactive strategies vary in terms of outcomes in workplace settings?

Proactive strategies often lead to better outcomes in the workplace because they encourage planning, prevent surprises, and allow for more effective solutions. Employees who take a proactive approach might identify potential obstacles earlier and find ways to overcome them in advance. Reactive strategies, on the other hand, can sometimes result in missed opportunities and damage control, as employees respond to unexpected issues without adequate foresight. A proactive focus can lead to increased productivity, fewer mistakes, and a more efficient workflow.

What role do proactive and reactive behaviors play in psychological well-being?

Proactive behaviors can positively impact psychological well-being by promoting personal control and autonomy, reducing stress, and increasing feelings of competence and purpose. When you take a proactive approach, you’re empowered to make choices and take responsibility for your life, which contributes to a sense of agency and self-esteem. Reactive behaviors can have the opposite effect; constant reactions to situations may lead to feelings of helplessness, stress, and decreased self-worth.

In what ways can adopting a proactive approach influence long-term health maintenance compared to a reactive one?

Adopting a proactive approach to health maintenance means actively engaging in behaviors that promote well-being and prevent illness. This can include regular exercise, a healthy diet, routine medical checkups, and managing stress effectively. A proactive approach can lead to long-term benefits, such as a reduced risk of chronic illnesses, improved mental health, and a higher quality of life. Conversely, a reactive approach to health care might only address issues as they arise, potentially leading to a decreased ability to prevent and manage chronic conditions.

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Reactive vs Proactive Problem Management

proactive and reactive problem solving

Problem management in IT is rarely discussed, but it is certainly practiced daily—in a variety of ways, some of which are successful. More often, problem management looks like a group of sys admins arguing about who’s to blame for the latest episode of company-wide slow-down.

When done well, however, problem management has the potential to catapult the IT unit from a fire-fighting position to one that offers a clinical focus on improvement and innovation—precisely the value and ROI that your company expects from IT. In fact, the way IT goes about identifying, managing, and eliminating problems plays a major role in how the unit is viewed by other business units and the company at large. Atlassian reported that high-performing IT teams are nearly 2.5 times more likely to practice problem management proactively, instead of waiting to put out fires.

In this article, we’ll take a look at problem management and compare reactive and proactive approaches.

What is problem management in IT?

We already know from ITIL that any problem is an underlying cause of one or more incidents. Problem management, then, refers to how you manage the lifecycle of problems. IT can approach problem management in two ways: reactively or proactively.

  • Reactive problem management is concerned with solving problems in response to one or more incidents.
  • Proactive problem management is concerned with identifying and solving problems and known errors before further incidents related to them can occur again.

Both approaches are key to ensuring a holistic and comprehensive tackling of the underlying issues that negatively impact IT services, but it is the reactive approach that is usually the first port of call for most support teams. Balancing the two approaches must be ingrained throughout your organization and should be one of the leadership’s imperatives.

Defining reactive problem management

Reactive problem management is triggered directly after an incident that is deemed worthy for a root cause investigation, such as one major incident or a series of incidents which are significant in totality. It complements incident management by focusing on the underlying cause of an incident to prevent its recurrence and identifying workarounds when necessary. Reactive problem management considers all contributory causes, including causes that contributed to the duration and impact of incidents, as well as those that led to the incidents happening.

The swarming technique is a strong approach in reactive problem management: different units come together to examine an incident, then brainstorm and identify the source and the potential root causes. Take, for example, an application that has crashed. Incident management would restart services that have stopped or reload a recent version, while reactive problem management would investigate the source of the crash by analyzing logs or getting information from a developer or vendor. The problem would be logged as a direct reference to the incident and workarounds, as identified by incident resolution, would be documented alongside it. If the fix requires a patch, then change management process would be used to permanently resolve the problem.

Other techniques for reactive problem management include chronological analysis, Kepner and Tregoe, 5-Whys, and fault isolation.

One of the main drawbacks of reactive problem management is its defensive nature, not unlike closing the gate after the horse has bolted. Secondly, technical teams are usually under pressure to find the incident’s root cause instead of focusing on restoring service as quickly as possible. However, the benefits of reactive problem management are clearly visible to stakeholders once it is proven that a fix, whether permanent or temporary, will prevent recurrence or reduce impact should the incident resurface.

Understanding proactive problem management

Proactive problem management is driven from a continual improvement perspective. The trigger is not the result of an active incident, but rather the result of identified risks to service. These risks may include warnings, errors, or potential breaches to thresholds that indicate potential problem areas. As such, proactive problem management activities take place as ongoing activities targeted to improve the overall availability and end user satisfaction with IT services. The main techniques of proactive problem management include trend analysis, risk assessment , and affinity mapping.

Let’s use the same example as above to demonstrate proactive problem management. The monitoring unit detects errors in the application—they aren’t causing downtime, but they may indicate problem areas.

The sys admins take time to document the errors and research potential causes. This may indicate that that the errors occur whenever the application calls to a particular database, routed through certain interfaces. The sys admin can elevate this issue to the network admins and the database admins, who can then identify the exact issue and shut down the effected interfaces, ending the errors. Depending on the situation, the admins may opt to reconfigure the ports or replace the affected components in order to permanently eradicate the problem before it becomes serious.

The clearest benefit of proactive problem management is a significant decrease in the number of critical incidents. An IT team can never prevent all incidents, so reactive problem management is something all teams will have to deal with. However, proactive problem management is the mark of a truly mature IT unit.

Putting in place metrics that measure proactive problem management and placing a reward on the same from an innovation perspective will serve to motivate IT to focus on such opportunities. Interestingly, proactive problem management can have a negative side effect, at least from an IT marketing perspective: the company may not fully appreciate a problem that was addressed as it never caused an issue in the first place.

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Joseph Mathenge

Joseph is a global best practice trainer and consultant with over 14 years corporate experience. His passion is partnering with organizations around the world through training, development, adaptation, streamlining and benchmarking their strategic and operational policies and processes in line with best practice frameworks and international standards. His specialties are IT Service Management, Business Process Reengineering, Cyber Resilience and Project Management.

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  • Reactive vs Proactive

In this section, we will expand upon the two common approaches in practicing problem management and how your IT environment maturity decides which approach is more suitable for your organization.

Reactive problem management vs. proactive problem management

Reactive vs proactive problem management

What is reactive problem management?

Reactive problem management reacts to the incidents that show up, then proceeds with the problem management process. Essentially, a reactive problem management approach aims to find and eliminate the root causes of known errors, and deals with a problem only when it shows up as major or recurring incidents.

What is proactive problem management?

Proactive problem management seeks out issues, faults, and known errors in IT systems by going through past incidents, network monitor data logs, and other sources of information, then proceeds to solve them permanently before they arise as incidents. This process is a part of continuous service improvement. Proactive problem management also aims to solve all known errors under the KEDB if it is feasible to do so.

Both types of problem management follow the same phases of problem-solving once presented with a problem: problem identification, problem control, and error control. The only difference is the approach towards identifying the problem. Nonetheless, both processes offer distinct advantages to service management, and require unique resources to function.

Choosing between reactive and proactive problem management approaches

how to implement reactive and proactive problem management

Organizations that are new to problem management should focus their efforts on implementing a reactive problem management process. It's sensible to use the problem-solving talent of the existing service desk staff when they aren't occupied with daily incidents; in doing this, they gain valuable experience before implementing proactive problem management.

As an organization's service delivery matures, it should transition to a proactive problem management process. This transition should be carried out by a team with a good analytical skill set that's highly proficient in IT infrastructure and the tools and technology that support the organization.

However, many organizations don't undergo this transition since it's tricky to quantify the benefits of proactive problem management, which can be perceived as solving potential problems and not actual ones. Nevertheless, some of the world's most effective organizations practice proactive problem management and find tremendous benefit in it.

Despite reactive and proactive problem management following the same phases of problem-solving once presented with a problem, there are multiple techniques to get to the root cause of a problem. Let's move on to the various techniques used in problem management.

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A tale of two managers

Is it better to be proactive or reactive, what causes a reactive mindset, qualities of proactive leaders, how to be a more proactive manager.

Being a manager isn’t always putting out fires — but it can certainly feel that way. In a perfect world, you’d be able to anticipate every worst-case scenario. But life — and business — rarely work out that way. 

Every leader needs to have a mix of skills — the ability to be both proactive and reactive. Understand what proactive and reactive management styles are and how to best use them in this article.

Let's imagine that it's the holiday season. Every year, a retail store gets slammed with shoppers looking for the best bargain. The sales team counts on these last-minute shoppers. Their purchases help the store in order in their final push to meet sales goals for the fiscal year.

Manager A is dreading the upcoming season. Even though it's the busiest time of year, this is when they have the most call-outs and staffing issues. People are constantly shopping, but they’re all looking for sales and good deals. As a result, the store still has a hard time meeting its goals. 

By the time the end of the year rolls around, everyone is stressed, and half of the staff has quit.

Manager B knows that the holidays can be rough. They start hiring people for the holiday season early in the fall. By the time the holidays come around, the store is well-staffed and the employees are well-trained. To drive sales, they host a series of smaller sales events to learn which products are the most popular. Each staff member takes a workshop on sales training strategies . 

In the weeks leading up to the holiday, the store contacts regular customers and offers them a discount if they make a shopping appointment. By the time the holidays roll around, the store has already exceeded its goal. They also have fewer last-minute shoppers, so the store doesn’t need to be fully staffed. More employees get to spend time with their loved ones instead of working late. 

So what's the difference? One manager took a reactive approach, and the other manager took a proactive approach.

What is reactive vs proactive management?

Reactive management is when leaders respond to crises and issues as they arise. Proactive management means anticipating needs and challenges so that you and your team are prepared to overcome them.

It's impossible to anticipate every circumstance . No leader or organization can be proactive all the time. But if you're reactive all the time, you may spend a lot of time in “fire-fighting mode.” Your team may get frustrated that you didn't have contingency plans in place for foreseeable challenges.

In general, it’s better to be proactive. That means that you try to consider situations before they arise to make sure your team is prepared for them. As the saying goes, “An ounce of prevention is worth a pound of the cure.”

However, there are benefits and drawbacks to both leadership styles. Future-minded leaders need to be skilled at both proactive and reactive management styles. This is especially true as the world of work changes. The COVID-19 pandemic and the shift to remote work has increased the need for agile leaders who can adapt quickly to changing circumstances.

Let’s take a look at the pros and cons of reactive and proactive leadership.

Pros of proactive leadership:

Proactive leaders (and their teams) are confident in their ability to foresee and plan around potential challenges. 

When difficulties arise, the response time is often shortened by having a strong plan in place.

Proactive managers often allocate more time and resources to skills development, training, and mentoring their teams.

Cons of proactive leadership:

Leaders who prioritize a proactive approach need to be sure to leave time for day-to-day tasks and urgent matters.

Those who are too big picture-focused may leave their teams feeling like they’re out of touch.

It’s impossible to anticipate every situation. Trying to do so can lead to “analysis paralysis,” where people feel immobilized until they have all the facts.


Pros of reactive leadership:

Reactive teams can work very well under pressure. They have excellent problem-solving skills that are battle-tested.

Reactive people are often more comfortable with uncertainty and trying different approaches.

Individual contributors display high levels of ownership. They don't worry about planning everything out perfectly or running ideas by a chain of command before they act.

Cons of reactive leadership:

Always being in problem-solving mode isn’t good for morale. It can feel like there’s “always an emergency.”

When leaders are constantly putting out fires, they don’t have the time to devote to long-term planning.

It’s difficult to measure which methods were the most effective or predict success in the future.


No leader walks into work and says “I'm here just to deal with problems.” Leaders want to be able to set a vision for their team, achieve the goals they set, and help people develop in their careers. But sometimes, external factors affect leaders in ways that prevent them from being as forward-thinking as they would like.

Every leader and every workplace encounters challenges. But when circumstances feel out of control, it’s easy to start feeling a bit reactive.

These environments are overrun by the tyranny of urgency. There are a few factors that contribute to high stress and urgency in workplaces:

1. A culture of presenteeism

Employees benefit from taking time away from work . If they don’t feel like they can take time off, even when they’re sick, morale suffers. This culture of “show up no matter what” is detrimental to the workplace.

2. Poor priorities

Teams benefit from a clear set of priorities. When they fail to set a target that everyone can work towards, people often scatter their energy in different directions. This can make it feel like your team’s efforts are “all over the place” or that nothing’s ever finished.

3. Hypercompetitiveness 

Competition can be invigorating and motivating in an office. But when people feel that they need to fight to get ahead — or even keep their jobs — it saps energy. You can’t plan ahead if you’re always watching your back.

4. Lack of resources

Working on a shoestring budget or with a skeleton crew? If your team is stretched thin, proactive thinking will fall through the cracks. Companies in “survival mode” have a hard time thinking more than a step or two ahead. Even when they do, they often don’t have the resources to invest in anything that won’t have an immediate payoff.

Are your employees or managers burned out? Proactive thinking takes a certain amount of energy and optimism. Those can be in short supply when you’re feeling burned out . You’ll need to handle well-being before you can start thinking about anything else. 

The good news is that reprioritizing to be less reactive can actually help reduce burnout and boost employee well-being . 

The urgent/important matrix was first outlined by President Eisenhower and popularized by Stephen Covey. It helps people and organizations split tasks into four quadrants.


In today's hyper-connected world, we tend to focus on the tasks that are the most urgent. That includes ringing phones , pinging notifications, and the manager that’s threatening to quit if they don’t get a raise .

Often, the most important task that we have to do are not urgent at all. And many of our urgent tasks could have been handled before they became emergencies. That's where a proactive attitude is helpful. As leaders, if we can learn to handle day-to-day tasks while still making time to plan for potential future events, we become more effective. Prioritizing tasks and situations when they're important (but not urgent) prevents them from becoming problems.

What does a proactive leader look like? 

For starters, they are team-oriented. They know the importance of developing leaders and look for opportunities to invest time into their teams.

They try not to be a bottleneck for their teams. They encourage other people to talk through both short-term and long-term scenarios to look for potential problems. They empower their teams to take action and help them become self-reliant.

Even though these leaders are excellent problem solvers, they don’t spend all their time trying to fix problems. They split their time between working towards the team’s goals and trusting that they can handle unforeseen issues when they arise.

Proactive people don’t try to anticipate everything. They listen to their team’s input. They are as reflective as they are forward-thinking. They systematically look back on what worked and what didn’t work so they can increase their chances of success. 

These leaders are open to new approaches as long as they are aligned with the values of the organization.


Part of your responsibility as a leader is to handle challenges as they arise. In those cases, reactive strategies are helpful. The trick is to bring a proactive mindset even when you have to react to changing circumstances.

Here are some ways to be a more proactive manager:

1. Do some strategic planning

Where do you think your company going? What will you need — and who will you need to be — in order to get there? Both companies and individuals benefit from strategic planning . At BetterUp, we measure strategic planning as a skill with the Whole Person Model . People who grow this skill are better able to think proactively (both at work and in their own lives) to plan how to reach their goals.

2. Anticipate your growth

Imagine that you’ve accomplished your biggest goals. What problems would you have? Take steps to inhabit that solution in advance.

What does that mean? For example, you may want to increase your client base by 10x. What would that look like? What would you need to have in place? Can you start identifying and training new account executives? Can you upgrade to a new platform that can handle a larger bank of clients?

3. Track your time 

What do you spend a lot of time doing? What do you do that could be done smoother, easier, or by someone else? 

If you're struggling with this now, these pressure points will likely be the first areas to show strain when you grow. Can you streamline these tasks or delegate them? Do they need to be done at all?

4. Ask your team for feedback

Remember those Dilbert cartoons that made fun of “big picture thinking?” One of the drawbacks of proactive thinking is that leaders who only talk about “the big picture” can seem out of touch.

Touch base with your staff and the people who interact directly with your customers regularly. What questions are coming up consistently? What do they think you need to plan for? Make it easy for them to provide you with feedback , information, and ideas on a regular basis.

5. Develop self-awareness

Reactive thinking can leave you feeling as if you’re constantly under stress . This happens because you're always playing catch-up or “waiting for the other shoe to drop.” You don't really have time to plan for contingencies, and that can leave your bandwidth pretty stretched. 

Stressful situations come up, though — no matter how well you plan. Cultivating self-awareness can help you stay calm when you're under stress. You’ll be more effective in a crisis — and more reassuring to your team — when you don't lose your head every time something happens. 

6. Work with a coach

A mentor, senior leader, or coach is vital for managers and business owners. In fact, there are some very good reasons why every leader should work with a coach . 

As the saying goes, it's lonely at the top. Leaders need just as much support as their teams do, but they may not know where to go to get it. As people grow into leadership roles and the needs of the team get bigger, having a person who can step back and provide alternate perspectives is often invaluable. 

7. Stay focused on what matters

As a teenager, I worked as a barista in a local coffee shop. I loved my job and I was a pretty ambitious kid, so I quickly set my sights on getting promoted . I figured if I worked really hard and did everything perfectly, I’d be a shift supervisor in no time. 

One day, my supervisor saw me washing dishes. I had all my attention on getting that dish perfectly clean, so I was completely surprised to hear him greet a waiting customer.

After the customer had her coffee and was on her way, my supervisor gently reminded me that nothing was more important than our customers. “As long as we’re open, you have to look out for them,” he said. “Nothing can have 100% of your attention or energy, because you always need to know what’s going on around you. If you're going to be a supervisor, you're going to have to learn how to prioritize, because things change all the time.”

Often, new managers are independent contributors who were promoted for their excellent work. They tend to be especially prone to “do-it-all-myself syndrome.” As managers learn how to be proactive, they’ll master the balance of planning ahead while dealing with challenges as they arise.

Final thoughts

It's not possible to be proactive all the time, but it’s not practical to be in problem-solving mode all the time either. Proactive means learning to dance between the day-to-day while keeping one eye on the future. 

Finding this balance between reactive vs proactive management styles isn't easy, but you don't have to do it alone. Working with a mentor or coach to develop self-awareness and perspective can help you become a more effective, less reactive leader.


Allaya Cooks-Campbell

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ITIL Reactive and Proactive Problem Management: Two sides of the same coin

Advisera Neven Zitek

While ITIL Problem Management has a logical and easy-to-understand description, implementing Problem Management within your own organization is extremely challenging. It happens more often than not, that Problem Management doesn’t produce any of the desired outputs upon implementation. In order to prevent that, you must recognize the importance of both the reactive and proactive parts of ITIL Problem Management.

At this point, I’d recommend reading ITIL Problem Management: getting rid of problems  just to establish a general overview of the relationship between Incident Management and Problem Management.

Reactive Problem Management


Figure 1: Reactive Problem Management

Reactive Problem Management reacts to incidents that have already occurred, and focuses effort on eliminating their root cause and reoccurrence. The main focus of Problem Management is to increase long-term service stability and, consequently, customer satisfaction.

When incidents start to occur, IT organizations want Problem Management involved early, but Incident Management strives to resolve the incident and restore service to usable levels as quickly as possible, and during that process, some important indications about root cause may be lost. So, in order to effectively pinpoint root cause, Problem Management may block Incident Management efforts to restore service. This is where confusion may arise regarding the difference between Incident Management and Problem Management.

What we need is clear and well-defined hand-over procedure, with agreed time frames within which Incident Management stops, and Problem Management starts. There should also be an agreed set of information that Incident Management passes to Problem Management during the hand-over, which includes what has been done so far, whether any workarounds are in place, information about affected Configuration Items (CIs), or other important information.

Problem Management processes all that information and outputs Requests for Change, updates the Known Error Database (KEDB) and Work-Arounds, updates Problem Records and produces management information.

Proactive Problem Management


Figure 2: Proactive Problem Management

Even though Reactive Problem Management relies heavily on other Service Management components, Proactive Problem Management relies even more. Proactive Problem Management is a continuous process that doesn’t wait for an incident (or series of incidents) to happen in order to react; it’s always active and always on guard.

Proactive Problem Management is extremely challenging in an environment where you have lots of services, different technologies, and many things going on at the same time. So, what makes efficient Proactive Problem Management?

With Proactive Problem Management, the focus is on continuous data analysis, and in order to do that, you need a large volume of quality data. There are several data analysis techniques that Proactive Problem Management uses in daily operation:

  • Pain Value Analysis – Instead of analyzing the number of incidents related to a specific CI or system over time, Pain Value Analysis is focused on the “level of pain” those incidents brought to the business. The formula for calculating “pain level” is: Pain value = (No. of incidents) x (duration) x (1/severity) x (weighting factor) . It’s very useful for detecting problems with equipment that is invisible to end-users (network routers, VOIP gateways, etc.).
  • Pareto Analysis – This is another great method for finding root cause for most common trivial issues. Group the incident/problem data by common group type, and create a cumulative percentage table. Drawing a graph will reveal the common group type that generates 80% of all incidents/problems, and you can focus further investigation from there.
  • Kepner-Tregoe® method – Kepner-Tregoe is a Registered Trademark of Kepner-Tregoe, Inc. in the United States and other countries, and is mentioned within ITIL materials related to Problem Management as one of the data analysis techniques. It revolves around: defining the problem, describing the problem in terms of identity, location, time (duration) and size (impact), establishing possible causes, testing the most probable cause, and verifying the true cause.

So, what’s so confusing about ITIL Problem Management?

You may be aware that ITIL Service Management practice components deeply rely on and interact with each other. Some may be observed in more “independent” fashion, but some can’t exist even on the drawing board without other components being implemented first.

One of the greatest examples of heavily dependent component is ITIL Problem Management . It’s closely related to Incident Management, and Incident Management is one of the first ITIL components that IT organizations implement. With basic Incident Management in place, organizations believe that Problem Management is simply an add-on, which can be used to “upgrade” Incident Management with Problem Management.

But, Problem Management can hardly be of any use if there is no Change Management, Asset Management, Configuration Management, Event Management, Availability Management, Capacity Management, Knowledge Management and many more components in place. Problem Management heavily relies on data stored throughout the Service Lifecycle in order to be effective.

I can give you a good example of Problem Management reliance on other Service Management components: A customer had repeatedly reported issues with his laptop performance, and the Incident Management team repeatedly resolved it by simply reinstalling the computer, over and over again. The customer was obviously not thrilled with the solution, but each incident was resolved within the SLA, and on the surface, everything looked peachy. However, repeated occurrence of the incident on the same asset triggered the Problem Management process, and after brief analysis, the results were very surprising. The customer initially had a SSD drive installed, but a year ago ordered a new one with larger capacity. At roughly the same time, the first incident reports about slow performance started. After deeper analysis, Problem Management discovered that the new hard drive installed was, in fact, not a SSD, and moreover, it was the large capacity variant of the slowest model possible. Even deeper analysis revealed that the customer, when ordering the new drive, never stated that it should be SSD, and the vendor delivered a regular, slow, high-capacity type.

Without quality data from the Incident, Asset, Change, and Configuration Management – Problem Management would be useless in this situation.

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Proactive VS Reactive: 5 Differences & How to Be More Proactive

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One of the most well-known theories of physics states, “for every action, there is an equal and opposite reaction (Newton's Third Law of Motion )”. This same principle applies to how we act and interact with our surroundings.

Most people have a tendency to fall into one of two categories when it comes to addressing the demands of their lives: proactive or reactive.  

America's approach to health is a perfect example. Don't get me wrong, we have the most advanced medical technology in the world… however, instead of taking a proactive approach to health and preventing illnesses from occurring, our system focuses too much on waiting until a person gets sick before treating the illness with tons of pills.

Approaching people with nutrition-based care in cohesion with medical consulting, as opposed to keeping them separate, would be more proactive .

Table of Contents

What Does Being Proactive Mean?

Being proactive means taking initiative and anticipating potential problems . It is the opposite of being passive , which is waiting for something to happen before taking action.

proactive vs reactive story | proactive vs reactive 7 habits | proactive vs reactive for students

A proactive person takes charge in their life by setting goals, planning ahead, and not relying on others to help them reach their objectives. These people are open-minded and prepared for any potential changes.

They take responsibility for their actions. A proactive person may go to the gym and eat healthy to prevent health issues as much as possible.

What Does Being Reactive Mean?

Reactive people respond to events… or wait to respond until after the event has already happened. Reactive people tend to focus on short-term solutions rather than plan for the long term.

They may also be more likely to react quickly and emotionally , instead of thinking through the situation before taking action.

They may also wait until they are sick or stressed before seeking help, rather than being proactive and addressing issues before they become major problems.

5 Differences Between Proactive and Reactive People

1. initiative.

Proactive people take the initiative while reactive people wait for something to happen before taking action. Proactive people are the ones who are constantly setting goals and creating plans to achieve them, while reactive people may be more likely to wait for an opportunity to present itself before taking any action.

When it comes to a work promotion, the reactive person may sit and wait for the chance to gain recognition… while a proactive person will be more mindful of their performance, take initiative on projects, and search out opportunities that may arise.

The proactive person may go as far as to call a meeting with their boss to discuss their promotion possibilities or ask for a raise.

Steps to Take Initiative at Work

If you want to start taking initiative at work, try these steps:

  • Start by writing down your career goals and creating a plan on how to achieve them.
  • Look for opportunities to make yourself stand out and get noticed.
  • Take on additional responsibilities and show your boss that you are eager to learn new skills.
  • Ask for feedback from people that you work with or have worked for and use it to help you grow.
  • Speak up about your ideas and be open to constructive criticism. 

2. Preparation

Proactive people anticipate potential problems and prepare themselves for them, whereas reactive people live in a much more chaotic environment with little preparation. For example, a proactive person may buy hurricane insurance before the storms come in, while a reactive person would wait until after the damage is done.

Steps to Prepare for Potential Weather Problems

If you want to become more prepared for any potential problems arising from natural disasters, try these steps:

  • Take the time to research potential problems from each particular type of natural disaster (hurricane, earthquake, flood, etc.)
  • Make sure you are aware of changes in your environment, both in the short-term and long-term.
  • Create a plan for how to deal with any potential problems. For example, do you have a place to go in case you have to evacuate?
  • Gather resources that can help you prepare for a natural disaster. This could include things like extra food and water, first-aid kits, and more.
  • Develop an emergency savings fund in case of unexpected expenses.
  • Learn from experts on the best ways to prepare for a natural disaster.

3. Problem-Solving Ability

Proactive people take charge of the situation and take action to solve the problem, whereas reactive people often just react to the situation without trying to find a solution.

proactive and reactive problem solving

For example, if there is a problem at work, a proactive person may look for ways to fix it… while a reactive person may just sit back and complain about it.

Steps to Improve Problem-Solving Ability

If you want to become better at problem-solving, try these steps:

  • Take the time to identify the problem and what is causing it.
  • Analyze the situation to come up with possible solutions.
  • Weigh the pros and cons of each possible solution.
  • Develop a plan on how to implement the selected solution.
  • Execute the plan and assess the results.
  • Think of ways to improve the solution if needed.

4. Goal Setting

Proactive people are more likely to set and work towards goals, while reactive people may avoid setting goals or procrastinate when it comes to achieving them. They wait for their hand to be forced before deciding to make a change.

Steps to Set and Achieve Goals

If you want to start setting and achieving goals, try these steps:

  • Write down your short-term and long-term goals.
  • Break down your goals into smaller, more achievable tasks.
  • Set deadlines for each task and make sure to hold yourself accountable.
  • Have a plan of action and track your progress.
  • Seek help and advice from people who have achieved similar goals.
  • Reward yourself for each milestone you reach.

5. Stress Management

Proactive people use coping strategies to manage their stress levels, while reactive people often just resort to procrastination or avoidance.

Steps to Manage Stress

If you want to gain better control of your stress levels, try these steps:

  • Identify what is causing you stress and develop a plan to manage it.
  • Set realistic goals and expectations for yourself.
  • Find healthy ways to deal with stress, such as exercise, meditation, or reading.
  • Focus on the positives and try to see the silver lining in difficult situations.
  • Prioritize your tasks and make sure to take breaks throughout the day.
  • Create a support system and ask for help when needed. 

Why Is Being Proactive Better?

Overall, being proactive is beneficial because it allows you to be in control of your life , create and work towards goals, develop problem-solving skills, and manage stress . With preparation, practice, and guidance, anyone can become a more proactive person. 

Becoming proactive will open up new opportunities for growth and success in both work and life.  It is a powerful tool that can help you make positive changes and achieve your goals.

History has shown us this.  Our society is filled with proactive people who saw a gap in what people needed, versus what was available.

proactive vs reactive examples | proactive vs reactive communication | proactive vs reactive psychology

They then created solutions and changed the world for the better. This includes people like Steve Jobs, Albert Einstein, Alexander Graham Bell and countless others who made the world a better place. 

Additionally, being proactive has several benefits on a personal level. It can help you become more organized, accountable, and confident.

It can also help you manage your time better, reduce stress levels, and even cultivate positive relationships with family and friends.

Do I Have to Be an Extrovert to Be Proactive?

No. It is important to acknowledge that there is no “one size fits all” approach when it comes to being proactive . Some people are more extroverted, while some are more introverted. It all depends on the individual and their personality type.

As an example, an introvert might be proactive in their work by coming up with creative solutions and researching options before speaking up. On the other hand, an extrovert might be proactive by being social and engaging with people in order to find solutions. 

The key is to identify your strengths and weaknesses and use them to become more proactive .  Whether you are an introvert or extrovert, there is no right or wrong way to be proactive. 

The important thing is to find what works for you and use it to your advantage. This may include making a list of tasks, setting reminders, or breaking down goals into smaller steps.

Whichever way you decide to become more proactive, remember that it will take time and effort to fully incorporate the habit into your life.

Final Thoughts on Proactive vs Reactive

Proactive and reactive are two very different approaches to life, but they both have their own merits . Ultimately, it is up to you to decide which approach best suits your personality and lifestyle.

Being proactive means taking action, setting goals, and managing stress levels in order to achieve success. On the other hand, being reactive means responding to situations as they come up, without taking control of the situation.

Needless to say, if you want to be in a leadership position or have more control over your life and decisions, being proactive is the way to go.  

No matter what approach you take, remember to stay positive and focus on the end goal. It’s more about getting there… than the path you took to do so.  With practice and dedication, anyone can develop more proactive habits and achieve success.

For more help in learning how to take charge, check out this article How to Get Your Life Together: 15-Day Plan for Taking Control .

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From Reactive to Proactive Management

Getting out of "firefighting" mode.

By the Mind Tools Content Team

proactive and reactive problem solving

Imagine that you're managing two important projects. Project A should be nearly complete, but it's nowhere near done. So, you pull people off Project B to help.

This project then falls behind, and people make mistakes because they're stretched. When your customer complains, you reallocate team members to address his concerns. A backlog of problems then builds up as your people get to grips with their new tasks. You're forced to put long-term planning aside, so that you can respond to these new problems.

This type of management is called "reactive management," or "firefighting." It's hectic, stressful, and inefficient – but it can sometimes become routine.

In this article, we'll look at reactive management in more detail, and we'll see why it happens. We'll then outline how you can move to a more proactive management style.

What Is Reactive Management?

Reactive management refers to a situation in which you can't – or don't – plan ahead for problems or opportunities. Instead, you react to them as they happen. As a result, you're always a step behind. You don't have time to look ahead to pre-empt problems, so they seem to happen "out of the blue."

In contrast, proactive management happens when you plan ahead to avoid or manage problems.

Why Reactive Management Happens

You might be in a reactive state for several reasons. For example:

  • A crisis may have forced you to change or abandon your plans. You need to make short-term decisions to cope with a fast-developing situation.
  • Your organization may have poorly planned processes or policies. You need to spend your time fixing these or working around them, instead of planning for the future.
  • You may find a reactive management style exciting. People can enjoy the "buzz" that goes along with it.

The Problem With Reactive Management

Firefighting is sometimes essential during a rush, or as part of a short period of change. However, it can have serious implications when it becomes the norm.

First, reactive teams are likely to deliver lower quality work. You may be able to fight fires successfully most of the time, but you will sometimes fail – in a way that you wouldn't if you were more proactive.

It's also likely that you'll need to shift your team members from one task to another, or ask them to deal with constantly changing information. This is inefficient, it can leave them frustrated, and they may start to look for more satisfying opportunities outside your team.

Your individual performance will fall, too. It's hard to find the root causes of problems when you have to focus urgently on symptoms. Plus, you're less likely to spot the strategic opportunities that proactive managers exploit, because you don't have the time and mind space to see them.

Reactive management is also stressful. When you deal with one crisis after another, you don't have time to unwind. You may be able to cope with this pressure, but your team members may be less resilient.

Moving to Proactive Management

If you've slipped into reactive management, follow these steps to move to a more proactive style.

1. Take Back Control of Time

Time is an essential weapon against reactive management. When you create more time, you give yourself space to plan, and to anticipate problems.

Use Eisenhower's Urgent/Important Principle to determine which tasks and responsibilities are critical. Delegate or delay any non-critical tasks, and use an Action Program to help yourself prioritize. You may even want to create a "stop doing" list, so that you can focus on essential tasks.

Encourage your people to do the same, offer guidance on prioritization , and explain how they can leverage their time to get more done.

You may find it helpful to schedule a regular block of time as "buffer time" to deal with unexpected situations. This way, you can also schedule regular project time, without leaving yourself over-committed when problems do come up.

2. Look at Processes

Dysfunctional processes can trigger or worsen reactive management situations. So, do a thorough review of all of the processes that affect your team. Also, look at people's working practices, as these may create delays or add complexity.

Map and challenge each process using Flow Charts or Swim Lane Diagrams . Then use tools such as Failure Mode and Effects Analysis (FMEA) to explore possible process improvements , and create checklists, aides memoire, and other documentation to help your people adapt.

Involve your team members in this work. They'll be able to fill you in on task-related difficulties, which will help you anticipate and avoid future problems.

Bear in mind that people may have a limited capacity to deal with change when they're busy. Don't make too many changes at the same time.

3. Understand and Manage Risk

Once you've improved the robustness of your processes, you can start to address the problems that you face with more confidence.

Conduct a Risk Analysis , and use a Risk Impact/Probability Chart to prioritize the risks that you face. Then, manage each risk that you've identified, starting with the high-probability, high-impact ones.

4. Focus on Morale

It's likely that members of your team will feel the pressure that comes with reactive management. Acknowledge the situation, and remind people of what you're doing to resolve it.

Then, use the Broaden and Build theory to bring positive emotions back to the team, and look for small wins. For example, say "thank you" after tasks are completed, acknowledge good work, and provide learning opportunities.

Let team members know that it's OK to ask for help , and create opportunities for your team to discuss problems, share information, and support one another, via team meetings or informal get-togethers.

It can be tempting to hire temporary workers to address work shortages or delays. However, think carefully when you take on more people. It may take time and energy to get them up to speed, which can cause further delays or problems.

It may be more sensible to hire short-term, expert contractors . They're likely to be more expensive, but they will be able to become productive quickly.

5. Build in Continuous Improvement

Make the most of your people's knowledge and experience by encouraging them to suggest changes.

Create opportunities for your team to explore and implement ideas that could improve processes, working practices, and end results. This approach, known as Kaizen , is a management technique that focuses on continuous improvement. It's a simple way to engage your team members, and help them focus on solutions.

You may want to schedule a regular time to discuss new ideas , set objectives that encourage creativity, or simply create a suggestions box.

With reactive management, also called "firefighting," managers spend most of their time dealing with problems instead of focusing on long-term planning.

Reactive management is stressful and inefficient. It can lead to high staff turnover in your team, and, in time, will lead to serious under-performance.

To move from a reactive approach to a more proactive one:

  • Take back control of time.
  • Look at processes.
  • Understand and manage risk.
  • Focus on morale.
  • Build in continuous improvement.

Keep in mind that reactive management is necessary at times. However, it can be destructive when it becomes the norm in a team or organization.

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What is root cause analysis? A proactive approach to change management

Root cause analysis (RCA) focuses on fostering a proactive approach to solving problems before they happen and eliminating the potential for flaws to reoccur in the future.

Tree roots

Root cause analysis definition

Root cause analysis (RCA) is a problem-solving process that focuses on identifying the root cause of issues or errors with the goal of preventing them from reoccurring in the future. RCA is typically part of service management methodologies and frameworks, such as ITIL , TQM , and Kanban , that focus on continuous process improvement . This type of analysis can help identify flaws in IT processes, potential security breaches, and faults in business processes.

When a problem is identified and removed, it is considered a “root cause” if it prevents the problem from reoccurring. If, however, a problem is removed and it impacts the event’s outcome, but not in the way intended, then it is a “causal factor.” RCA is typically used to find the root cause of software or infrastructure problems to improve the quality and efficiency of processes, and thereby to save time and money. Every potential cause in a given process is identified and analyzed to ensure the organization is treating the disease, rather than just the symptoms.

Reactive vs. proactive problem management

Reactive management and proactive management are the two main approaches organizations take to repairing issues and solving problems. With reactive management, problems are fixed soon after they occur, often called “putting out fires.” The goal is to act quickly to resolve issues and alleviate any effects of a problem as soon as possible.

Proactive management, on the other hand, aims to prevent problems from reoccurring. It is focused less on quickly solving problems and instead on analyzing them to find ways to prevent them from happening again. That’s where root cause analysis comes in. Its methodology is best suited to support proactive problem management’s goal of identifying and fixing underlying issues, rather than just reacting to problems as they happen.

Root cause analysis steps

While there’s no strict rulebook on how to conduct a root cause analysis, certain guidelines can help ensure your root cause analysis process is effective. The four main steps that most professionals agree are essential for RCA to be successful include the following:

  • Identification and description: Organizations must first identify the failures, errors, or events that triggered the problem in question and then establish event descriptions to explain what happened.
  • Chronology: After identifying these issues, organizations must then create a sequential timeline of events to better visualize the root cause and any contributing causal factors. Here, it’s important to establish the nature of the event, the impact it had, and where and when the problem occurred.
  • Differentiation: Once the sequence of events is established, data involved with a particular issue can be matched to historical data from past analysis to identify the root cause, causal factors, and non-causal factors.
  • Causal graphing: Those investigating the problem should be able to establish key events that explain how the problem occurred and convert that data into a causal graph.

Root cause analysis takes a systematic approach to identifying problems and requires the effort of full teams to properly perform the analysis. Those tasked with the analysis typically work backwards to determine what happened, why it happened, and how to reduce the chances of it happening again. They can trace triggered actions to find the root cause that started the chain reaction of errors in a process to remedy it. These steps help guide the process and give organizations a framework for how to successfully complete a root cause analysis.

Root cause analysis methods

RCA is already baked into several IT frameworks and methodologies as a step for change, problem, or risk management. It’s been established as a proven, effective way to support continuous process and quality improvement. But if you are conducting a root cause analysis outside of a separate process management framework, organizations typically employ the following methods to ensure a successful RCA:

  • Form a team to conduct the RCA and evaluate processes and procedures in the organization that have flaws. This team should be built by bringing together employees who work in relevant business areas or who work directly with the broken processes.
  • Once the analysis begins, it can take upwards of two months to complete. Each step of the process is given equal weight whether it’s defining and understanding the problem, identifying possible causes, analyzing the effects of the problem, or determining potential solutions.
  • Teams should meet at least once per week, if not more often, with meetings being kept to no longer than two hours with a loose agenda. The meetings are intended to be relatively creative, so you want to avoid bogging people down with too much structure.
  • Team members should be assigned specific roles or tasks so everyone has a clear understanding of what they should be investigating.
  • Upon finding a potential solution, it’s crucial to follow up to make sure that the solution is effective and that it’s implemented successfully.

Root cause analysis tools

You don’t need much to conduct a root cause analysis, but there are several tools that are helpful and commonly used to help make the process easier. Commonly used tools to perform an effective root cause analysis include:  

  • Fishbone diagrams: A fishbone diagram is mapped out in the shape of a fishbone, allowing you to group causes into sub-categories to be analyzed.
  • Failure mode and effects analysis (FMEA): FMEA is a technique that can be used to map out a system or process and identify the failures within it. It can be used not only to identify flaws but also to map out how often they happen, what actions have already been taken, and what actions have been effective in remedying the issue.
  • Pareto charts: A Pareto chart is a simple bar chart that maps out related events and problems in order of how often they occur. This helps identify which problems are more significant than others and where to focus process improvement efforts.
  • Scatter diagrams: A scatter diagram plots data on a chart with an x and y axis. This is another useful tool for mapping out problems to understand their impact and significance.
  • Fault tree analysis: A fault tree analysis uses Boolean logic to identify the cause of problems or flaws. They are mapped out on a diagram that looks like a tree, where every potential cause is included as its own “branch.”
  • 5 whys analysis: With 5 whys analysis, you will ask the question “why” five times too delve deeper into a problem to develop a clearer picture of its root cause.

Root cause analysis training

While RCA is a part of other frameworks and methodologies, there are training programs and courses designed to focus on helping people better understand how to perform the analysis. If you want to get more training on RCA, here are a handful of programs designed to help:

  • Workhub Root Cause Analysis training
  • Udemy Root Cause Analysis course
  • Pink Elephant Problem Management: Root Cause Analysis Specialist certification course
  • NSF Root cause analysis CAPA training and certification
  • Coursera Root Cause Analysis course
  • ASQ root cause analysis course
  • Lean Six Sigma Root cause analysis online training

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Proactive vs Reactive: How to prevent problems instead of fixing them faster

Stephen Wilson

I consistently stand in front of customers wanting the same thing from their performance monitoring toolsets. “We want to be more proactive ,” they proclaim in desperation. “We need to have a tool that will automatically alert us to a problem” they demand. “We want to address a problem before it becomes a problem .” This is a constant challenge for Operations. Think about it. How many times have you said, “I will change the oil in my car when the motor locks up and smoke is pouring out of the dash.” Yet this is how companies treat revenue-generating applications that they depend on every day. Pushing features and functionality through the application delivery chain often results in performance problems that are missed, bringing down applications in production.

In this blog, I share the struggle I’ve seen with companies that buy a performance management product in the belief it will make them more proactive. In fact, it requires much more than tools to make the transition from reactive to proactive.

The Status-Quo: Faster in being Reactive – but not yet Proactive

Companies that I deal with have to solve the same problems and questions as described in the intro when it comes to performance management. Let’s have a look at the typical process when pushing a new release into production. Each team has a different way of reacting to problems as fast as possible in case anything goes wrong:

  • The Operations Team may use as many as fifteen different tools to watch every aspect of the application. As soon as something is seen amiss everyone springs into action to try and identify the problem. Once the issue is isolated the deployment change is backed out. A feedback loop is started as teams try to determine the root cause.
  • The Development Team gets data from Ops but soon demands more as standard log output is not deep enough and they are typically not allowed to get direct access to these machines for debugging. Additional tracing code is hastily written into the application to get more information out to logs. These logs are combed over to try and understand where the problem is really coming from. Once the root cause is isolated new code is written to address it.
  • In haste the Testing Team attempts to use load generators to check and see if the problem is really solved. That only works if the problem is able to be recreated in a controlled environment. When a level of confidence is reached, the application is deployed and the cycle begins anew.

This process at its core is the definition of being reactive in practice. It is clear that – even with the best tools and additional resources one can only get faster in being reactive but this won’t help proactively prevent problems.

What does proactive mean to Ops, Test, Dev?

Proactive means different things depending on which team is asked:

  • Operations teams feel that getting proactive means having better alerts that are “smarter”. Baselines that quickly adjust for new norms without having to fly blind when new builds are introduced. The problem with this is that it does not make a team more proactive at all. It just makes that team quicker at being reactive.
  • Testing teams would like to catch issues earlier but the tools used make it hard to handle the changes coming from the development teams. They ask for a solution to help them manage things from release to release while adapting to new code coming through with very little setup time.
  • Development teams are trying to be more agile in their process, moving to a more task-oriented process with more automation in the testing and building of these applications. Visibility into the performance of each build is not the main priority.

Lack of communication and collaboration prevents being proactive

To address the need to be more proactive we have to address the way IT organizations look at managing the performance of applications. Current processes make this hard for one main reason: The lack of communication and collaboration during the lifecycle makes identifying and addressing issues very hard.

Lack of collaboration between Dev, Test and Ops prevents organizations from becoming proactive

Each team has very different drivers and goals that only compound this challenge. Let’s think about the scenario above. The tool sets used by each one of these teams are usually by a different vendor. These tools provide different types of information that are poorly shared with the other teams:

  • Operation Teams have an arsenal of tools ranging from infrastructure level tools to home grown solutions in an effort to tease out the most obscure data. All are used in the hopes that performance problems can be identified.
  • Testing teams have another set that allows for some level of performance but it is typically for response time analysis of an application under load. With more web 2.0 technology these tools are having a harder time delivering information around edge complexities like 3rd party APIs and toolkits.
  • Development Teams , while understanding performance, is primarily testing for functionality and data integrity. The tools used are more to automate the build process and for functional testing and recursion. Unit tests are written to derive that the right response was returned versus how long it took for that response to return.

Each tool provides performance but none of them allow for that data to be shared between teams which leads to problems when communicating and collaborating on performance topics across the lifecycle.

Becoming Proactive requires Process Changes

The Definition of Proactive : creating or controlling a situation by causing something to happen rather than responding to it after it has happened.

Having smarter alerts just make a team better at not being proactive according to this definition. The only way to become proactive is to address the problem at its source: the Application Delivery Chain . To truly become proactive with your applications you must be in control of them. Teams must come together to cause the application to do what they expect. A change in the process is imperative to make a transition from reactive teams to a proactive ecosystem. The phases of the application cannot be treated as independent isolated steps at moving code from concept to a working product that is generating revenue. It must be looked at as part of a whole. Each team dependent on another working together to bring a system from design to production ready in a timely manner. Take the previously discussed scenario. Had the team’s process been utilizing a unified platform for performance, the outcome would look very different.

So how does an organization begin to address the delivery chain and start to become proactive? IT must put performance as a key metric for delivering quality products for doing business. In order to address this, an IT organization must have a process and platform implemented that facilitates some key features. Those being communication, providing rich understanding of performance details and a single source of truth. This will deliver actionable data to the appropriate teams and empower decisions to be made with that data. Using a single platform gives performance data across all the teams involved while moving applications from the drawing board to the end user. Operations now feeds data back to the development teams for rapid feedback. Once Development addresses performance issues thorough testing can occur. Testing teams can now use real test cases extracted from user transactions to test against the performance fixes. QA now becomes empowered to give the final approval for any release going into production. Now, instead of looking for the smallest hiccup, operations expects normal performance. There comes a level of repeatability and expectation.

Unified platform with clear communication and collaboration cycles allows becoming proactive

Proactive comes when the decision is made to attack the problems at the root before they are in the wild causing consternation for your teams and your customers. This sounds hard to do and in fact, it can be if everyone is not bought into what the overall vision and goal is. Each team needs to understand that they are not an independent group. They are now a part of the overall ecosystem and if one part fails the whole ecosystem fails. Over the next few months we will explore what this process and platform should look like at each phase and ultimately look at the need for a Center of Excellence around performance and a template that can be used to start replicating this process in an IT organization.

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Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals

Gabriel souto fischer.

1 Programa de Pós-Graduação em Computação Aplicada—PPGCA, Universidade do Vale do Rio dos Sinos—Unisinos, Av. Unisinos 950, Bairro Cristo Rei, São Leopoldo CEP 93022-750, Rio Grande do Sul, Brazil

Rodrigo da Rosa Righi

Cristiano andré da costa, guilherme galante.

2 Programa de Pós-Graduação em Ciência da Computação—PPGComp, Universidade Estadual do Oeste do Paraná–Unioeste, Rua Universitária 2069, Bairro Jardim Universitário, Cascavel CEP 85819-110, Paraná, Brazil

Dalvan Griebler

3 Parallel Applications Modeling Group—GMAP, Pontifical Catholic University of Rio Grande do Sul—PUCRS, Av. Ipiranga 6681, Bairro Partenon, Porto Alegre CEP 90619-900, Rio Grande do Sul, Brazil

4 Laboratory of Advanced Research on Cloud Computing–LARCC, Três de Maio Educational Society—SETREM, Av. Santa Rosa 2405, Três de Maio CEP 98910-000, Rio Grande do Sul, Brazil

Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

1. Introduction

The Internet-of-Things (IoT) is a concept where physical objects (i.e., things) are connected through a network structure and are part of the internet activities in order to exchange information about themselves and about objects and things around themselves [ 1 , 2 ]. A particularly relevant scenario for IoT is healthcare [ 3 , 4 , 5 ]. IoT-assisted patients can be supervised uninterruptedly, thus allowing risky situations to be detected and appropriately treated right away [ 6 ]. According to Butean et al. [ 7 ], no matter how easy or complicated a situation is, if the medical staff do not react in an appropriate time, everything regarding patients’ health might become doubtful and unsafe. Hence, health professionals play a major role towards patients’ well-being [ 8 ]. In this kind of scenario, a static allocation of health professionals to health sectors may be inefficient, since some professionals may be misallocated to low demanding sectors, while leading to a lack of professionals in highly demanding sectors. Such a problem is illustrated in Figure 1 , where the set of available attendants are statically assigned to two service sectors, one for exams and another for medication. In the example, more attendants are examining than medicating patients, even though the number of patients waiting for exams is considerably smaller than those waiting to receive some medication. In this context, if each room has a required specialty, and if each health professional has a list with all its specialties, the idle attendants who have the required destination room specialty could be moved from the low demanding room to the high demanding one. In fact, the allocation of attendants should always adapt to the current conditions of the health sectors.

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Example scenario where there are more attendants examining than medicating patients, even though the number of patients waiting for exams is considerably smaller than those waiting to receive some medication, generating dissatisfaction for patients awaiting medication.

Therefore, it is necessary to find effective strategies to adapt human resources in real-time. Elasticity in cloud computing is one of the key strategies for adapting on-demand computational resources [ 9 , 10 , 11 ]. According to Rostirolla et al. [ 12 ], the elasticity concept can be extended to other areas besides computing. Today, most resources control approaches can be classified as reactive or proactive (also named by some authors as predictive) [ 9 , 10 , 13 , 14 ]. Reactive approaches are based on both static bounds and if-condition-then rules to manage elasticity [ 9 ]. Typically, users define an upper and a lower threshold on a target performance metric (e.g., CPU utilization, memory, response time) to trigger activation and deactivation, respectively, of a certain resources number [ 15 ]. A problem of using fixed thresholds is related to application overloading, illustrated in Figure 2 . After the system reaches an upper bound, there is a time interval for the delivery of the resource. During that period, we have an application overload [ 9 ]. Also, another problem is the lack of reactivity when using these parameters. There are situations in which is possible to anticipate the (de)allocation of resources, however, the resource configuration remains the same due to bad choices on setting the lower and upper thresholds [ 9 , 15 ].

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Elasticity approaches: ( a ) reactive; ( b ) proactive.

A proactive approach employs prediction techniques to anticipate system behavior (its load) and thereby decide the adapting actions [ 9 ]. This capability enables the application to be ready to handle the increase when it actually occurs [ 15 ]. To accomplish this approach, it is common to use time-series-based prediction techniques (such as Exponential Smoothing, Moving Averages and Autoregressive models) and machine learning algorithms (including Neural Network, Linear Regression, Support Vector Machine, Reinforcement Learning and Pattern Matching techniques) [ 9 , 15 ]. This approach is typically classified adversely as time-consuming for sensitive performance applications [ 9 , 16 ]. Also, Netto et al. [ 17 ] affirm that proactive elasticity strategies focus on method accuracy and ignore limitations such as the scaling up operation time, although it dependents on the workload characteristics. Hence, the reactive approach performs faster because there is no concurrent processing concerning the application. In the proactive approach, for each monitoring step, it runs a given prediction algorithm that can impact the normal execution of the application, since the background task can be costly.

Considering this background, we present a model of Elastic allocation of human resources in Healthcare environments (ElHealth, for short) as an alternative to the traditional static allocation of medical staff. ElHealth works by adjusting the medical staff allocation of smart hospitals (equipped with IoT sensors) based on reactive and proactive elasticity approaches. In particular, ElHealth uses IoT sensors to keep track of patients demand, which is modeled as a time series and is used to estimate demands. Such estimations allow to identify situations where the staff availability is unlikely to meet the demand. Building upon such estimations, ElHealth proposes an efficient allocation of the medical staff by moving such professionals and also allocating new human resources to the most demanding areas while taking into account their time constraints. The idea is to always offer a reasonable waiting time for patients regardless of the workload (number of them in the hospital room). In resources elasticity, there are advantages and disadvantages in reactive and predictive methods. Using ElHealth, we propose an evaluation of proactive and reactive approaches for reorganizing human resources in smart hospitals to identify which significantly decreases the waiting time regarding healthcare. ElHealth supports both elasticity approaches at run-time. The main scientific contributions of this article are threefold:

  • (i) We devise Human Resources IoT-based Elasticity, for automatic management of human resources in healthcare environments, making use of elasticity for smart, IoT-enabled hospitals;
  • (ii) A cost-benefit analysis of the use of reactive and predictive strategies (of elasticity in cloud computing) for human resources reorganization. The cost refers to the health staff allocation costs in each approach, and the benefit is the anticipation of problems, based on the reduction of waiting time for care.
  • (iii) We introduce Human resources cost and Elastic number of human resources used metrics for evaluating human resources elasticity.

This article is organized as follows. Section 2 presents the work related to our study. Section 3 presents ElHealth as well as the concepts of Multi-level Reactive and Proactive Elasticity of Human Resources. Section 4 expresses the methodology of evaluation of the model. Section 5 presents an evaluation performed with the developed implementation, as well as the results found. Finally, Section 6  presents the conclusions and future work directions.

2. Related Work

This section describes some approaches to manage elasticity in cloud and overviews approaches to managing the deficiency of resources to attend patients’ demand in healthcare environments. They were divided into two groups: reactive and proactive systems in Section 2.1 (where we discuss two papers of elasticity in cloud computing, one for each approach, and all articles found that extend the concept of elasticity to other areas) and human resources in Section 2.2 (where we discuss some works that focus on human resources lack in healthcare environments). Lastly, the initiatives were compared and analyzed in order to detach the current gaps in the research area.

2.1. Reactive and Proactive Systems

Reactive managers are those based only on thresholds to take elasticity decisions; more precisely, resource reconfiguration takes place when the lower or the upper threshold is violated. In the reactive scope, we highlight three initiatives: Al-Dhuraibi et al. [ 18 ], Elastic-RAN [ 19 ] and ElCity  [ 12 ]. Al-Dhuraibi et al. [ 18 ] presents a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies based on a dynamic configuration during the execution of the application. The experiments demonstrated that their model covers the elasticity policies provided by the well-known cloud public providers with negligible overhead. Elastic-RAN [ 19 ] proposes a multi-level and adaptable elasticity for Cloud Radio Access Networks (C-RANs). The adaptive algorithm feature refers to the moldable elasticity grain where resources in BBU pools level and BBU level are provisioned as close as possible to the current processing needs. Elastic-RAN might achieve gains up to 64% in the execution time when compared to a traditional C-RAN. ElCity [ 12 ] is a model that combines citizens and city devices data to enable an automatic and elastic multi-level management of energy consumption for a particular city. ElCity explores the cloud elasticity concept in multiple target levels (smartphones from citizens, city devices involved in the public lighting, and data center nodes), turning on or off the target levels resources on each level regarding their demands, estimated based on energy consumption monitoring and citizens movement. ElCity achieved a reduction of more than 90 percent of the energy spent in public lightning in the studied city.

Proactive managers try to predict the cloud behavior to anticipate elasticity decisions before any under or overload situation. In the proactive elasticity, we highlight two works: Hanafy et al. [ 20 ] and Proliot [ 21 ]. Hanafy et al. [ 20 ] proposed an elasticity control algorithm for a containerized cloud using two agents. The host agent monitors and predicts its utilization using Autoregressive Moving Average (ARMA) [ 22 ], while the master agent performs elasticity by handling failures in load interchange scenarios. The results demonstrated the algorithm capabilities to elasticate and handle flash crowds along with decreasing the management overhead and maintaining proximate load balancing. Proliot [ 21 ] combines cloud and high-performance computing to address the IoT scalability problem in a novel EPCglobal-compliant architecture. The model offers an elastic EPCIS component that is automatically allocated or deallocated concerning the system load. Proliot uses Autoregressive Integrated Moving Average (ARIMA) [ 23 ] and Weighted Moving Average (WMA) [ 24 ] to predict the IoT load behavior, anticipating scaling in or out operations. Proliot improves 300% the response time when compared with the scenario that is not using elasticity. Table 1 summarizes the aforementioned related work. Reactive approaches have a low computational cost compared to proactive approaches. However, proactive approaches can avoid overloading in applications by taking elasticity actions in advance.

Reactive and proactive related work comparison.

2.2. Human Resources in Healthcare Environments

Some approaches focused on optimizing the flow of patients to properly allocate health resources [ 25 , 26 , 27 ]. Cappoci et al. [ 25 ] used discrete event simulation technique in order to improve patients’ waiting times. To this end, using data from a Brazilian polyclinic, and queueing theory [ 28 ], the authors proposed some changes to balance the occupancy levels of the health unit’s staff and, at the same time, reach a shorter waiting time for patients. Results showed a significant improvement in the performance of the Polyclinic’s system. Vieira and Hollmén [ 26 ] investigated ways of minimizing bottlenecks in the flow of patients due to appointments, visits, usage of resources, etc. The objective was to improve patients’ satisfaction and maximize the hospital’s profit. To this end, using data from a Finnish hospital, the authors used k-Nearest Neighbours [ 29 , 30 ] and Random Forests [ 31 ] to predict such a flow. In the same line of thinking, Graham et al. [ 27 ] aimed at predicting the arrival of patients in the emergency department of a hospital to properly prepare the allocation of medical staff. To accomplish such a task, the authors used logistic regression [ 32 ], decision trees [ 33 ], and gradient boosted machines [ 34 ] with data from a British hospital. In both works [ 26 , 27 ], the objective was exclusively on identifying specific data patterns, instead of proposing counter-measures to improve the allocation of health resources.

In an attempt to increase health coverage, some studies proposed forecasting models to understand the evolution of doctors supply and patients demand to better adjust one to the other. Ishikawa et al. [ 35 ] concentrated on training enough physicians to meed the patients demand in Japan until 2030. Liu et al. [ 36 ] focused on a similar problem, but from a global perspective. In contrast to our work, the adaptation of the hospital’s resources to the patients’ flow was left aside for these works. Table 2 summarizes the aforementioned human resources related work. As we can see, there are several approaches to analyze and estimate the use of human resources in healthcare environments so that that patient flow can be improved, or to understand the evolution of the problem of the health professionals lack.

Human resources in healthcare related work comparison.

2.3. Comparison and Research Opportunities

Table 1 and Table 2 presents a comparison of the collected papers, presenting some of their main characteristics, and pointing out some of their gaps. Based on the selected papers, we can identify that despite the elasticity being proposed for cloud computing, and being employed in reactive [ 37 ] and proactive [ 20 , 38 ] approaches, the same can also be employed in other areas such as energy [ 12 ], IoT [ 21 ] and C-RAN [ 19 ]. In this way, we can see the potential of elasticity to be extended to other contexts, such as human resources. When we have the problem of the lack of resources in hospital environments, the articles found just focus on predicting the future demand of patients or the future quantity of available doctors, not proposing solutions to the problem, leaving others in charge of decision-making. The approaches that propose solutions, such as physician training [ 35 ], or the movement of a nurse between two rooms [ 25 ], are very specific and can not be used in other medical environments. In this context, we can enumerate some of the main gaps in the area as follows:

  • In the best of our knowledge, there are no approaches that evaluate the use of reactive and predictive elasticity for human resource management;
  • Although several models are capable of identifying current and future demand in a hospital environment, these models lack solutions to help to solve the problem of deficiency of hospital resources;

The lack of enough human resources in healthcare environments is not new and, based on studied works, we can see that this problem will remain in the future [ 35 , 36 ]. Hence, finding ways of optimizing the use of existing resources and adjust hospitals’ capacity to meet patients demand are challenges that can make all the difference. The use of data prediction and Internet of Things contributes towards future solutions or automation of processes in the health area. However, the potential of the technologies is being underused since it is possible to propose solutions such as optimization and better use of existing human resources.

3. ElHealth Model

According to the literature review, most of the approaches concentrate only on identifying the location and current/future health status of patients, neglecting the potential benefits that efficient health resources allocation could bring to the patients [ 39 , 40 ]. As presented in Section 1 , one of the major challenges faced in hospital environments refers to the large waiting queues. Moreover, considering that doctors reaction time plays a role in patients recovery [ 7 ], long waiting times may compromise patients’ future health.

Based on this background, we introduce ElHealth, a multi-level model for efficient allocation of human resources based on patients’ flow within hospital environments. In particular, ElHealth adapts the concept of elasticity in cloud computing to the context of human resources, adjusting the hospital’s attendance capacity to the demand of patients, where professionals are allocated, deallocated and reallocated according to the hospital needs. ElHealth groups information from several sources: patients arrivals and needs (using IoT sensors spread over the hospital environment and a hospital dataset), patients movement (using IoT sensors), and medical staff availability (from a dataset). Using these data, we measure real-time demand of patients, on reactive approach (which we discuss in Section 3.3.1 ), and we employ a time-series prediction algorithm to anticipate the future demand of patients, on proactive approach (as we discuss in Section 3.3.2 ). This information is then useful for applying the concept of elasticity-based allocation of resources. Based on that model, ElHealth computes an efficient allocation of hospital resources (medical staff and equipment), which contributes towards minimizing patients’ waiting queues. Hence, ElHealth introduces the concept of Human Resources IoT-based Elasticity in healthcare environments, which can be defined as follows.

Human Resources IoT-based Elasticity is an extension of the concept of resource elasticity in Cloud Computing [ 13 ] to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patients’ demand. The Human Resources IoT-based Elasticity uses IoT sensors to keep track of patients’ demand and, based on proactive and reactive elasticity approaches, proposes an efficient allocation of the medical staff by moving such professionals to the most demanding areas, always considering the quality of services currently offered by these healthcare environments.

The next subsections detail our model, bringing the main design decisions ( Section 3.1 ), the proposed architecture ( Section 3.2 ), and the Multi-level Elasticity of Human Resources concept using reactive ( Section 3.3.1 ) and proactive ( Section 3.3.2 ) approaches.

3.1. Design Decisions

We based our model on the premise that there are sensors scattered around the hospital, which can identify patients who pass through them. Firstly, they must be in all the entrances and exits, so that whenever a patient enters or leaves the hospital, it is possible to identify it. To detect the movement and location of patients, we assume the presence of sensors at the doors of all hospital rooms. Each patient must have a Patient Identification Wristband linked in the system and must carry it through all time in the hospital’s internal environment. The attendant responsible for the reception of patients should be able to perform the linking of a wristband to a given patient as soon as the patient is admitted in the hospital. Thus it is possible to identify when and where a given patient is as soon as he enters at the healthcare environment, along with the time he remains in each room while being attended to. Also, each healthcare professional must have a tag linked to him in the system and must carry it with him throughout his active period in the hospital. Thus, all available attendants can also be located inside the hospital in the same way as patients.

We use a Real-Time Location System (RTLS) [ 41 ] with room-level localization accuracy. According to Boulos and Berry [ 41 ] and Jachimczyk et al. [ 42 ], RTLS are systems for identifying and tracking the location of assets and/or people in real-time or near real-time. Furthermore, RTLS provides an automated means of collecting operational data on clinic activity such as room utilization rates, or patient wait times [ 43 ]. We based the choice of an RTLS on its ability to allow automatic identification, avoiding the existence of a human error in identification processes. ElHealth should be transparent to patients, in the sense that it does not need to report any conditions related to its movement through the hospital environment, being an activity performed automatically by the system.

With respect to the data prediction strategy, ElHealth uses a statistical-based approach through the implementation of the ARIMA model. According to Nisha and Sreekumar [ 44 ], ARIMA model uses historical information to predict future patterns. ARIMA is the most general class of model for forecasting a time series. Since we can describe the number of patients waiting for care over time as a time series, we chose to use the approach through ARIMA because it is a very flexible mathematical model, with an excellent predictive performance of time series when compared with other approaches [ 44 ]. ARIMA models are extremely useful in predicting different sectorial series since they can represent stationary series, and also non-stationary series. We use a non-stationary model based on seasonality in demand for medical staff, since accidents, epidemics, holidays, and other events, can alter patients’ demand for care.

3.2. Architecture

ElHealth architecture model three services: (i) a Web service, responsible for visualization layer, and ElHealth Web Interface; (ii) an inference service, responsible for data processing, movement records handling, patients demand prediction, and human resources allocation decisions; and (iii) a database service. These three services are part of our proposed ElHealth Service. Figure 3 presents the components and the network view in the proposed model.

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Components and network view in ElHealth model with ( i ) ElHealth Web Interface; ( ii ) ElHealth Service, for information processing and decision making; ( iii ) a RTLS, for track users’ tags; and ( iv ) Hospital managers, patients, or human resources.

ElHealth model is subdivided into five modules responsible for information handling from its capture by sensors to the final result displayed in the Web application. Each module has a specific function, having an input information and a specific output result that can be used as input from other modules. Figure 4 presents the proposed modules, detailing the architecture of the model.

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ElHealth model architecture detail where the information flow starts in ElHealth_Capture module that receives users’ movement records from RTLS sensors, and goes through different handlings over proposed modules, until the exhibition of elasticity notifications in ElHealth Web Pages.

ElHealth_Capture receives and pre-process data captured by sensors scattered around the hospital and sends to ElHealth_Formatter , responsible for process data, and identify patients’ movement through hospital environments and rooms. After, ElHealth_Predict identifies patients movement through the hospital environment. Based on previously generated movement records, the path that patients travel during their movement through the hospital, and the time spent in each environment are identified. Thus, this module identifies patterns related to the arrival of patients in these environments, and patterns related to the waiting time for care, using this information to predict future patients arrivals in each hospital environment.

ElHealth_Elastic manages system’s elasticity. It verifies human resources allocation in each of hospital environments, check the current patients’ movement (in reactive approach) and the predictions made by the previous module (on proactive approach). This module generates an intelligent and automatic allocation of human resources to meet patient demand better. We want to emphasize that the system generates notifications for human resources to reallocate, but effective reallocation depends on the people accomplishing what was indicated by the application. ElHealth_Elastic and ElHealth_Predict modules are the most important part and the core of our proposed model, since ElHealth_Elastic can request predictions from the ElHealth_Predict module to take elastic actions, performing resources analysis based on predictions performed by the previous module, and also can perform elastic actions based on current patient demand. In Section 3.3 will be detailed the algorithms and how the elastic management of the human resources in the hospital environment are performed. Finally, ElHealth Web Interface displays to human resources the elasticity notifications generated before.

3.3. Human Resources Elasticity

ElHealth employs the term elasticity with a slightly different meaning from that used in cloud computing. Here, it refers to the system’s ability to allocate/reallocate/deallocate human resources capable of attending patients in order to adapt to varying patient demand in real-time. In particular, in the context of this work, elasticity refers to:

  • Allocation , which denotes the capacity of the system to request health professionals who are not in the hospital to attend the current patients’ demand;
  • Reallocation (or migration) , which denotes the ability of the system to migrate professionals who are allocated to a particular hospital environment to some other environment where more professionals are needed;
  • Deallocation which denotes the capacity of the system to release human resources no longer needed to attend the current patients’ demand.

In order to perform allocation, deallocation, and reallocation of human resources, ElHealth model makes use of reactive or proactive approaches to monitor the demand of patients and the use of rooms in the hospital. Our model considers elasticity differently for: (i) the reactive approach, where our model must verify the use of any given room, and propose human resources movement if an upper or lower threshold is reached (as discussed next, in Section 3.3.1 ), and for (ii) the proactive approach, where ElHealth should verify if there are sufficient attendants to meet patients’ future demand from any given room in the hospital environment, with attendants moving between rooms (as detailed forward in Section 3.3.2 ). For this process, ElHealth should be able to alert people to allocate. However, the final decision should always be made by the health professional or hospital manager.

3.3.1. Reactive Elasticity

In reactive mode, ElHealth uses a multi-level approach where our model considers elasticity differently at (i) the room-level, where ElHealth must verify the use of a given room, and check if is necessary more or fewer attendants to meet patients’ demand, and at (ii) the hospital-level, where our model proposes attendants movement between rooms to meet patients’ demand. We use this multi-level strategy, since different rooms may have different time thresholds for care. In this way, a prior analysis of the need for each room-level is necessary in order to perform the load-balancing procedure (hospital-level). An example of these two levels is presented in Figure 5 .

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Multi-level Reactive Elasticity of Human Resources example with ( i ) room-level reactive elasticity, and ( ii ) hospital-level reactive elasticity.

ElHealth model adapts the reactive elasticity strategy using upper and lower thresholds for the context of people, based on the waiting time for care in each of waiting queues of a hospital environment. Figure 6 illustrates the use of thresholds where an upper threshold is reached (meaning that human resources should be increased to fulfill that needs) and soon after a lower threshold is reached (meaning that human resources could be released to other sectors). So, at room-level, in each monitoring cycle, ElHealth first checks the specific time thresholds of each analyzed room and compares with the waiting time in that room. In those where time is outside the upper or lower bounds, our model defines the need for allocation or deallocation of human resources.

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Reactive elasticity example based on waiting time for care adopted by ElHealth, where the delivery and release of human resources occur after the thresholds are reached.

At the hospital-level, ElHealth considers the possibility of moving health professionals between different hospital environments in order to optimize medical care time. To this end, the available options refer to: allocating new attendants, reallocating health professionals between different sectors, or deallocating human resources that are no longer necessary. ElHealth’s first option should always be the possibility of reallocating human resources already allocated to hospital care. The reallocation is prioritized because it is the option that brings fewer costs to the hospital since it performs adjustment of medical care without additional attendants. Algorithm 1 presents the pseudo-code for hospital-level reactive elasticity.

In what follows, we firstly discuss the reallocation concept, followed by the allocation procedures, rules, and algorithms. Lastly, we present the deallocation process. We note that, although deallocation appears first in the algorithm (line 9), it actually builds upon the human resources allocated during the preceding iteration of the algorithm. In ElHealth model, each room has a required specialty to the human resources that are allocated in it. In parallel, each health professional has a list of all its specialties. The process of reallocating or allocating human resources is only performed between professionals who have the required destination room specialty. This is necessary because in a laboratory exams room is required a nursing professional accustomed to blood tests for example, and even if we have X-ray technicians available for reallocation, they are not able to improve the attendance in the aforementioned room. In order to achieve human resources reallocation, all hospital rooms are in a list ordered by the attendants available for reallocation. In that way, whenever a room r needs a new human resource, the elasticity manager checks for available attendants, with room r specialty, in the first room of the list. If there is an available attendant, then it is reallocated to the needed room.

A potential problem that arises in the context of elasticity is the so-called hysteresis [ 45 ], which refers to the tendency of the system to return to the previous state in the absence of the impulse that caused the change. In the context of human resources elasticity, hysteresis occurs if a resource reallocated from a given room A to another room B and, in the subsequent time-step, room A needs that resource back. This kind of situation happens when the stimulus that led to the reallocation ceases to exist. However, when the resource is returned to the original room, the stimulus will emerge once again, leading the resource to be reallocated continuously between the two rooms. In order to prevent hysteresis of human resources, we employ a cooldown-based strategy [ 46 ]. In particular, whenever a resource is reallocated from a given room A to another room B, and if room B need a resource in the subsequent monitoring cycle, its need will only be met if another room has free resources, or by the allocation of a new attendant. In other words, the resource reallocated previously cannot be immediately returned, which avoids the hysteresis effect.

In some situations, the reallocation process may not be enough to improve the attendance level of the hospital. In such situations, the allocation of new resources may be necessary. We emphasize that, in order to minimize operational costs, the allocation is only performed if reallocation is not able to meet the patients’ demand. In an emergency situation, or exceptional cases, where all hospital staff are already in care and not available for reallocation, ElHealth proposes the allocation of new human resources. Thus, our model allocates health professionals who are not in the hospital but are available for allocation. We highlight that the hospital must have a strategy to define human resources available for external allocation. Since different countries have different labor laws, the rules that can make available for allocation the hospital staff on rest time can vary. Finally, if the algorithm identifies that the demand for care of all hospital rooms is very low and that the deallocation of attendants of some room does not harm the whole, ElHealth must identify which attendants were allocated outside of their regular working hours and deallocate them to lower the hospital’s financial costs. In the same way as reallocation, both allocation and deallocation are also protected by the cooldown-period. Also, if a given human resource is deallocated, it can no longer be allocated in the same work shift.

3.3.2. Proactive Elasticity

In proactive elasticity, ElHealth model uses a multi-level approach, slightly different of reactive elasticity, where (i) in the room-level, our model must identify the future use of a given room, and check if the number of attendants is sufficient to meet patients’ demand, and in (ii) the hospital-level, where ElHealth should verify if there are sufficient attendants to meet patients’ demand from all rooms in the hospital environment, with attendants moving between rooms. An example of these two levels is presented in Figure 7 .

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Multi-level Proactive Elasticity of Human Resources example with ( i ) room-level proactive elasticity, and ( ii ) hospital-level proactive elasticity.

ElHealth model adapts the proactive elasticity strategy using upper and lower thresholds for the context of people, based on the waiting time for care in each of waiting queues of a hospital environment. Figure 8 illustrates the use of thresholds, where ElHealth forecasts that the upper threshold will be reached and soon after ElHealth forecasts that the lower threshold will be reached.

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Proactive elasticity based on predicted waiting time for care adopted by ElHealth, where the delivery and release of human resources occur before the thresholds are reached.

At the room-level, in each monitoring cycle, ElHealth needs to predict patients arrival rate at any room based on current and previous arrivals on that room. The prediction is made using the ARIMA model based on the average care time with the current attendants’ allocation, and the estimated waiting time for the care queue. When ElHealth identifies that the waiting time will become higher or lower than the threshold values set by hospital manager, ElHealth should compute the number of health resources required to meet patients’ demand through the Proactive Human Resources Elastic Speedup. Proactive Elastic Speedup uses a predictive approach to determine the future demand of patients and dynamically define the adequate number of attendants, identifying the gain of future medical care time in a hospital environment. ElHealth proposes some mathematical formalism to estimate the Proactive Human Resources Elastic Speedup, which will be described in the sequence. Table 3 presents a summary of such mathematical notation.

Mathematical notation of ElHealth.

Let C V ( r , t i , t f ) denote the care vector of room r for the time interval between t i and t f . The size of any such vector is defined by s i z e ( x ) . Using these two functions, the average care time in the hospital’s room r between t i and t f times can be formulated as in Equation ( 1 ), where C D T ( x [ i ] ) refers to a care duration time x [ i ] that has already occurred in that room and x [ ] = C V ( r , t i , t f ) is a care vector that occurred in that room.

Equation ( 1 ) results in a numerical value of time. An example would be any room r , between 1 and 5 times, where the result could be defined as: A C T ( r , 1 , 5 ) = 15 minutes. Using this equation, it is possible to estimate the average time of a care in a particular hospital room. Due to the elasticity of human resources, at different time instants, there is a different number of attendants allocated to care in each of the hospital rooms. The average number of attendants in the hospital’s room r between times t i and t f is defined as in Equation ( 2 ), where N A ( r , t n ) refers to the number of attendants allocated to care in the room r at the instant of time n .

The same idea of the previous function is useful for patients’ reality because in different moments of time there are different amounts of patients awaiting care in each of the hospital rooms. Thus, the estimated number of patients waiting for care in the hospital’s room r between t i and t f times is defined by Equation ( 3 ), where N W P ( r , t i ) refers to the number of waiting patients for care in a room r at t i time instant, and N I P ( r , t n ) refers to the number of incoming patients in a room r at t n time instant.

Using the equations previously proposed, our model calculates the estimated care time of all patients waiting, and estimates the time that a new incoming patient needs to wait to be attended. The E C T ( r , t i , t f ) is defined by Equation ( 4 ), where A C T ( r , t i , t f ) refers to the average care time for room r between t i and t f times, and E N P ( r , t i , t f ) refers to the estimated number of patients who are waiting for care in a room r between t i and t f instants. An example would be the room r 1 , between two times t i and t f that would result in an average number of 4 patients and an average care time of 10 min as shown in Figure 9 .

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Calculating E C T in a hospital room r 1 with 4 patients waiting, and average care time of 10 min. In this hypothetical situation, at 0 min instant the first patient was called to the care. In 10 min instant, the first patient ends their care and goes away, so the second patient is designated to care, and so on, until instant 40 min, when the last patient is released. Thereby, all patients are attended within 40 min. Applying Equation ( 4 ), we obtain E C T ( r 1 , t i , t f ) = A C T ( r 1 , t i , t f ) · E N P ( r 1 , t i , t f ) = 10 × 4 = 40 min.

Knowing E C T ( r , t i , t f ) , we can analyze the average time for care of all patients waiting in the room r between t i and t f times. However, this value refers to a hospital room with a single attendant allocated for care, but in most cases will be more than one health professional working in that room, making it necessary to identify the average time with different numbers of attendants. In this context, ElHealth model uses a parallel allocation of human resources, such as the parallel allocation of virtual machines used in elastic systems [ 13 ] or the use of parallel processors in high-performance computing [ 47 ]. Thus, based on the Elastic Speedup proposed by [ 47 ], ElHealth introduces Equation ( 5 ) for Human Resources Elastic Speedup. Considering again the previous example ( Figure 9 ), with room r 1 between two times t i and t f with an average number of 4 patients, an average care time of 10 min and with two health professionals allocated, as shown in Figure 10 .

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Calculating the E C T in a hospital room using parallel allocation of attendants, with 4 patients waiting, average care time of 10 min, and 2 attendants. In this hypothetical situation, at 0 min time instant, there were 4 patients waiting and none in attendance by doctors, so the first two patients were called to care. In 10 min instant, the first two patients are released, and the last two patients are designated to care. Thus, at 20 min instant, the last two patients are released. Thereby, all patients are attended in only 20 min. Using Equation ( 5 ), we obtain: H R E S ( r 1 , t i , t f ) = E C T ( r 1 , t i , t f ) A N A ( r 1 , t i , t f ) = A C T ( r 1 , t i , t f ) · E N P ( r 1 , t i , t f ) A N A ( r 1 , t i , t f ) = 10 × 4 2 = 20 min.

H R E S ( r , t i , t f ) returns the estimated care time of a room r between the t i and t f times, considering a parallel allocation of attendants in that period of time, through the use of A N A ( r , t i , t f ) function. Thus, with the increase in the average number of attendants allocated, the estimated care time decreases, inversely proportional.

A problem of reactive elasticity is that the elasticity actions are taken after the upper threshold are reached, causing a state of overload in the hospital throughout the professionals’ movement period. Thus, an alternative to this problem is the use of proactive elasticity [ 48 ]. Thus, anticipating the moment when the upper threshold will be reached, people’s movement can occur in advance, minimizing or avoiding patients’ overloads in the hospital. In this context, we propose Equation ( 6 ) for Proactive Human Resources Elastic Speedup as follows:

where a is the number of attendants allocated between the future times f i and f f , and E C T ( r , f i , f f ) ′ is a prediction of the future care time for this room using ARIMA. We can compute E C T ′ as:

where A C T ( r , f i , f f ) ′ and E N P ( r , f i , f f ) ′ are predictions of the average care time and future patients at room r , respectively. Thus, for each room r being calculated, we generate a time series of A C T ( r , t i , t f ) that occurred in the past, and we use it to predict A C T ( r , f i , f f ) ′ . In addition, for each room we also generate a time series for N I P ( r , t i , t f ) , and can predict future patient input and find E N P ( r , f i , f f ) ′ .

Using the aforementioned equations, ElHealth can predict the waiting time of any hospital room. Varying a attribute in P H R E S equation, with the increase and decrease of the number of health professionals in attendance, ElHealth can identify how many attendants would be needed to adjust the waiting time of any room to the proposed thresholds, as defined by the hospital manager. Algorithm 2 presents our method to verify the need to allocate or deallocate human resources in any room r in a smart hospital.

At the hospital-level, ElHealth needs to test different allocations for the attendants to ensure that all rooms identified in the previous step (local-level) have enough attendants, and to minimize overcrowding. Our algorithm considers the possibility of moving health professionals between different hospital environments in order to optimize medical care time. As in the reactive strategy, between allocation or reallocation, ElHealth prioritizes the possibility of reallocating human resources already allocated to hospital care, to minimize hospital’s costs. To redistribute such health attendants between different hospital rooms, our model uses some strategies known from other contexts of scientific computing and adapts them to the proactive elasticity of human resources needs. Algorithm 3 presents the pseudo-code for hospital-level proactive elasticity. As in the reactive strategy, each room has a required specialty, and the process of reallocating or allocating human resources is only performed between professionals who have the required destination room specialty. A point to be observed is that those rooms where they need a specialty that no other hospital’s professional has, only the allocation of new human resources is performed.

In order to achieve a balanced reallocation of human resources, we developed a variation of the dynamic List Scheduling algorithm [ 49 ], which was originally used for process scheduling. Here, all hospital rooms are in a list ordered by the number of attendants available for reallocation. In that way, whenever a room r needs more attendants, the elasticity manager checks for available attendants, with room r specialty, in the first room of the list. If attendants are available, then they are reallocated to the room lacking them, and the list is sorted again. If more attendants are needed, the algorithm checks the first room in the list again, and so forth, until the room obtains all the required attendants.

Figure 11 illustrates the reallocation process, where Room 1 needs three more attendants and Rooms 2 and 4 have some free attendants. Following the logic of the adapted List Scheduling algorithm, in the first round, Room 2 is the first in the list, with three available attendants, and gives an attendant for Room 1. In the second round, even though all rooms in the list have the same number of free attendants, Room 2 remains at the top of the list, so another attendant is reallocated. Finally, in the third round, Room 4 becomes the first on the list, since it has two free human resources (as opposed to Room 2, which has only one), and an attendant of Room 4 is reallocated to Room 1.

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Reallocation through the adapted List Scheduling algorithm, with a sorted list of 4 rooms, and 12 attendants, where Room 1 needs to allocate more 3 attendants.

As in the reactive strategy, in proactive elasticity, we employ a cooldown-based strategy to prevent hysteresis of human resources. If the reallocation process is not enough to improve the attendance level of the hospital, ElHealth proposes the allocation of new human resources to hospital care. Lastly, if ElHealth identifies that the future demand for care of all hospital rooms is very low and that the deallocation of attendants of some room does not harm the whole, ElHealth proposes the deallocation of attendants allocated outside of their regular working hours.

4. Evaluation Methodology

We assess the performance of ElHealth through simulations in a virtual hospital environment. Considering the unavailability of data, the hospital environment was defined based on synthetic workloads. These data and its parameters are detailed in Section 4.2 . According to [ 50 ], synthetic workloads can be considered a representative form to evaluate elasticity in computational clouds. ElHealth was implemented mainly in Java, except for the ARIMA method, which was implemented in Python. For hospital queues simulation, we used a clock with discrete increments of ten seconds. At each advance in the simulation clock, our simulator verifies the patients who are in care and those who should leave the care. At each monitoring cycle, the arrival of patients should be checked. The data probability distributions were generated using triangular distributions (more details in Section 4.2 ), as implemented by StdRandom [ 51 ].

4.1. Considered Scenarios

Given the hospital simulation procedure, we consider three different scenarios for analysis. In all scenarios, we used the same input parameters. The differences in the scenarios are related to the use of the proposed model in the hospital environment and will be described as follows:

  • S1:   Hospital without ElHealth: in order to have data for comparison, the first test scenario is based on the simulation of a non-elastic hospital
  • S2:   Smart hospital with ElHealth’s reactive elasticity: the second scenario focuses on the simulation of the hospital environment with the use of the allocation, reallocation, and deallocation of human resources proposed in the ElHealth model, using reactive elasticity approach.
  • S3:   Smart hospital with ElHealth’s proactive elasticity: the third scenario is similar to the second, based on the simulation of the hospital environment with ElHealth’s elasticity model, but unlike the previous scenario, using proactive elasticity approach.

4.2. Performance Evaluation Parameters

To perform the simulation of the hospital environment, we use the data collected in the study of Capocci et al. [ 25 ] performed in a hospital environment located in Guarulhos City, in the state of São Paulo in Brazil. According to Capocci et al. [ 25 ], all patients upon entering the unit first go through reception, where a Personal Health Record (PHR) [ 52 ] is prepared. After this preparation, patients are referred to waiting for triage. In the triage procedure, the patients are examined by the nursing team and classified into priorities according to the urgency of the health problem and are referred to waiting for medical attention. In polyclinic analyzed by Capocci et al. [ 25 ], after first medical attention, 24% of patients are referred for x-ray exam, 37% for laboratory examinations (blood test, for example), 8% for electrocardiograms (ECG) exam, and 31% do not need more than physician examination. Also after doctor treatment room, only 1% of patients do not take medication and are released with only one prescription, but 50% of patients require intravenous medication, 30% intramuscular injection and 19% inhalation medication. After the exams, 60% of patients need to return to the doctor, and 40% are released. After a return care, 78% of patients are released, 2% need new exams, and 20% require new medication.

Also, according to Capocci et al. [ 25 ], the care time in each room of the hospital environment follows a triangular distribution, with minimum and maximum times and a more frequent average time. Table 4 shows the distributions for all possible care in this hospital unit, as identified by [ 25 ] in their study. All other parameters used in our simulation can be found in [ 25 ].

Triangular distributions of probability for care times.

In Brazil, the working model adopted for hospital environments is the so-called 12 × 36 h. According to Brazilian Law No. 13,467 [ 53 ], under this work regime, an employee can work for twelve consecutive hours (with a one-hour pause for lunch) and must rest for 36 h before a new work shift of 12 h starts. Under this regime, four health professions alternating shifts is enough to ensure a single position for 24 h, seven days a week. Also, according to the understanding of the law, if for any reason an employee needs to work within their rest period, it should be treated as overtime, unless the hours are compensated at another time. Thus, while a human resource of the hospital is in working time, three other employees who perform the same function are in their paid-rest period. According to Brazilian Decree-Law No. 5,452 [ 54 ], the minimum rest period between two working days must be eleven consecutive hours. In that way, even if there are overtime hours, an employee must rest eleven hours to return to the next work shift. Thus, these three resting employees shall not be arbitrarily available to a new allocation. In particular, any resting employee is only available under the following rules:

  • Rule   1: The minimum rest period for a human resource to be available for allocation is eleven hours;
  • Rule   2: An allocated employee cannot works outside of the regular work shift for a long time period. The largest possible work period allowed in Brazilian legislation is twelve hours. Thus, an allocated employee cannot work more than twelve hours;
  • Rule   3: Allocated employees must be deallocated no later than 11 h before they next normal work shift; and
  • Rule   4: Each employee must meet one of the 36 h rest periods within the same week in order to comply with a law determination that requires all workers to have a 24 h paid-rest period per week.

As our case study is based on Brazilian hospital data, we have set thresholds appropriate to our reality. So, based in Brazilian Law Project of 14 June 2018 [ 55 ] that proposes a maximum waiting time for care in hospitals, clinics, and laboratories of 30 min on regular days (from Monday to Sunday), we define ElHealth’s maximum load (i.e., 100%) in 30 min. Based on several works [ 12 , 47 , 56 , 57 ], we are using 4 combinations of thresholds when evaluating the second scenario, so considering 30% (9 min) and 50% (15 min) for lower threshold, and considering 70% (21 min) and 90% (27 min) for upper threshold. For proactive elasticity, we set ElHealth’s upper threshold in 30 min, (i.e., maximum load previously defined), and we set ElHealth’s lower threshold in 9 min (30% of maximum waiting time). For elasticity actions, we set 10 min for reallocation process (human resources movement between rooms), and 60 min for allocation process (to simulate the movement of a new human resource to the hospital).

4.3. Workload

We use the human resources allocation found in [ 25 ] research, where 11 health professionals were allocated, 24 h a day, seven days a week, through more than one work shift. To be specific, health professionals were allocated as follows: 2 attendants in a reception; 1 nurse working in patient triage; 2 doctors acting in doctors treatment rooms; 2 nurses working with collection exams; 2 nurses working throughout the medication area; 1 nurse acting on the electrocardiogram; and 1 radiology technician acting with the X-ray exams.

Regarding patients load, we modeled four workloads: constant, ascending, descending, and wave. The idea of using different load behaviors for the same application is used to observe how the input load can impact saturation points, bottlenecks, and the addition or removal of resources [ 56 ]. These four behaviors of workload are based on those proposed by [ 56 ]. Besides these four loads are representative to evaluate elasticity, wave workload is the most closely to the hospital reality, and the ascending workload represents the behavior of the model in a situation of increased patient load, which could be caused, for example, by a viral outbreak or epidemics. We want to emphasize that the ascending workload demonstrates the worst possible case, with an increasing entry of patients into a hospital. Figure 12 presents a representation of each workload of the model. The x axis expresses the time available in one day of care in the hospital unit, while the y axis represents the arrival of patients at each instant of time.

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Graphical representation of workloads used in ElHealth tests, where x axis expresses time available in one day of care, while y axis represents the arrival of patients at each time instant.

Since the workloads generate decimal numbers, we established a strategy to generate integers for the arrival of the patients in the hospital environment. This occurs because, in a real environment, it is not possible the arrival of 0.2 patients or 1.7 patients, for example. Thus, we adopted a load accumulation strategy, where if at any given moment there is something between 0.1 and 0.9 patient, this value is accumulated with next instant load. An example would be an instant with a load of 0.6 patient. Since there would not be an integer charge, a patient would not be introduced into the system, and the charge would accumulate for the next instant of time. At the next moment, with a new load of 0.6 patient, the accumulated load would be 1.2 patient, resulting in the entry of 1 patient in the hospital. Thus, there would be still 0.2 patient, which would be accumulated for the next instant and so on.

4.4. Performance Evaluation Metrics

In order to evaluate the proposed model, the following metrics are considered:

  • Maximum waiting time for care;
  • Human resources cost;
  • Elastic number of human resources used.

To evaluate the waiting time, we used as parameter the variation of the maximum waiting time between the scenarios and the adequacy of the maximum waiting time to the established limits. To determine the human resources cost, we had to propose a way to measure the cost of a human resource in normal working hours and the cost of a human resource outside of its working hours. According to Brazilian Law No. 13,467 [ 53 ] and Brazilian Decree-Law No. 5452 [ 54 ], the overtime pay will be at least 50% (fifty percent) higher than the normal hour. In this way, a health professional allocated outside of its working shift costs 50% more than an employee during its working shift. Based on this, we devised Equation ( 7 ) for Human resources cost as follows:

where H R ( t n ) refers to all human resources in their working shift at t n time instant, and A l l o c a t e d H R ( t n ) refers to all allocated, or in the process of allocation, human resources outside their regular working hours at t n time instant. With regard to human resources number, we proposed a metric for compare elastic and non-elastic health professional allocation, where we expect that our model uses the existing health professionals in the hospital in an optimized way. Thus, static allocation of S1, with eleven employees working, can be compared to ElHealth elastic allocation, with the number of human resources varying throughout the day. Table 5 presents all the evaluation metrics described above, relating the results expected for the second and third scenario with the use of ElHealth, when compared to the current hospital environment, without the ElHealth model.

Evaluation metrics and expected results in each scenario.

5. Performance Evaluation and Results Analysis

Based on the evaluation methodology proposed for the ElHealth model, we performed twelve simulations of the proposed hospital environment in order to collect results for analysis. For each proposed scenario, between S1, S2, and S3, a simulation was performed for each of the workloads, constant, ascending, descending, and wave.

For the maximum waiting time metric, we expected a decrease in patients’ waiting for care. Figure 13 shows the maximum waiting time identified for each workload in the proposed scenarios over the simulated one-week period. We perceive a significant reduction in the maximum waiting time between S1 and S2, and a second diminution when comparing S2 and S3, regardless of the workload used. After a thorough analysis, we can identify that in S3 for reception, triage, doctor treatment, and collection exams rooms, at no time was measured waiting time longer than 30 min, regardless of the workload used. As for medication, X-ray, and electrocardiogram rooms, there were a few moments when this limit was exceeded. Through the collected data, we identify a significant reduction in waiting time with the use of the reactive and proactive elasticity approaches for human resources organization when compared to the hospital without the use of the elasticity. Thanks to the reactive procedures, ElHealth has shown to decrease the waiting time by 96.13%, 95.27%, 96.05% and 93.4% for constant, ascending, descending, and wave workloads, respectively, as compared to the scenario where no human resources reorganizations are performed. In proactive procedures, ElHealth has shown to decrease the waiting time by 96.66%, 96.73%, 97.06% and 96.65% for constant, ascending, descending and wave workloads, respectively, as compared to the non-elastic hospital.

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Maximum waiting time at the hospital for each of the proposed scenarios, S1 (in red), best result between thresholds for S2 (in green for 70 × 50 and in orange for 70 × 30), and S3 (in purple), using ( a ) constant, ( b ) ascending, ( c ) descending and ( d ) wave workloads.

For human resources cost metric, we expected an increase in the cost between scenarios. Figure 14 presents the human resources cost for each workload in S2 and S2, the scenarios where the cost can variate. We can observe that cost ranged from 11 to 17.77 per hour, in the reactive approach, and ranged from 11 to 18.47 in the proactive approach. Furthermore, as exposed in the aforementioned Figure 14 , whenever ElHealth costs increase, the patients’ waiting time decreases. We can also see that the proactive approach achieved the most significant reduction in waiting time, with more cost than the reactive approach. In reactive procedures, the cost increased by 0.64%, 5%, 13.27% and 9.27% for constant, ascending, descending, and wave workloads, respectively. In proactive procedures, the cost increased by 7.09%, 7.36%, 22.82% and 3.27% for constant, ascending, descending, and wave workloads, respectively.

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Human resources cost compared with maximum waiting time at the hospital using ( a ) constant, ( b ) ascending, ( c ) descending and ( d ) wave workloads in S2 (best result between thresholds) and S3.

For the elastic number of human resources used metric, we expected an increase in the number of professionals in the hospital, as well as a variation of this number over the hospital care period. Figure 15 presents the elastic number of human resources used for hospital care in S3, the only scenario where the number of employees can variate. We can observe that the elastic number of human resources ranged from 11 to 14 per hour. Although there are moments with the allocation of up to 14 health professionals in care, the average per hour of care professionals turns out to be slightly lower depending on the time it takes for an employee to be allocated or reallocated in the hospital. Furthermore, as exposed in the aforementioned Figure 15 , whenever ElHealth reallocates or allocates peoples for care, the patients’ waiting time decreases.

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Elastic number of human resources used compared with maximum waiting time at the hospital for ( a ) constant workload in best result between thresholds for S2, ( b ) constant workload in S3, ( c ) ascending workload in best result for S2, ( d ) ascending workload in S3, ( e ) descending workload in best result for S2, ( f ) descending workload in S3, ( g ) wave workload in best result for S2 and ( h ) wave workload in S3.

Based on established metrics, we can note that the ElHealth model was able to improve the performance of the simulated hospital environment in all workloads used. Table 6 presents all the results found in each of the proposed evaluation metrics, highlighting the best results in green and the worst in red. As proposed in our evaluation methodology, we expected that the maximum waiting time presented a gradual decrease between scenarios S1, S2, and S3, and this in fact occurred, fulfilling the objective of this metric. For human resources cost, we expected an increase between scenarios S2 and S3, and our model has met expectations. For the elastic number of human resources used, an increase in the result was expected between scenarios S2 and S3, and our model once again was able to meet the proposed goal. Thus, the expected results in the evaluation methodology were achieved through the use of the ElHealth model in the proposed hospital environment.

Evaluation metrics and results found in each of the proposed scenarios, using constant, ascending, descending and wave workloads, where the best results for each metric are highlighted in green and the worst in red.

For maximum waiting time metric, our objective was the time reduction. As already shown, the ElHealth model was able to reduce the waiting time for the proposed hospital environment significantly. However, although the average maximum waiting times for the S3 scenario were within the established limit (9.42 min with constant workload, 12.7 min with ascending workload, 15.65 min with descending workload and 12.8 with wave workload), when we analyzed the longer waiting time identified in all the simulation period, the upper limit was exceeded (39, 48, 86 and 70 min with constant, ascending, descending and wave workloads, respectively). We believe that this occurred due to the limitations of the hospital environment used as the basis for this simulation. As there were not many care stations available to be allocated new human resources, our model was not able to reach the goal in this hospital environment. For human resources cost metric, we expected an increase among the proposed scenarios, and that is precisely what happened. When we compare with the previous metric, the increase in human resources cost is inversely proportional to the waiting time decrease. In the reactive approach, we had a considerable improvement in waiting time reduction, with little increase in cost. For proactive elasticity, we have a new improvement in waiting time reduction, with a new increase in the cost. Although the proactive approach has a higher cost, we believe it is still more efficient than the reactive approach because it can further decrease waiting time for medical care, anticipating more potential health problems. For the elastic number of human resources used metric, we expected an increase in the average number of human resources between scenarios C2 and C3, and this also actually occurred.

6. Conclusions and Future Work

IoT sensors allow smart hospitals capable of tracking people and objects in real-time. With this data, computer systems can be used to generate knowledge and value for hospital managers. This work puts efforts in this direction, taking data captured from IoT sensors and generating decision-making value on them. Thus, this article presented the ElHealth model. Unlike related work, ElHealth not only proposes the use of elasticity to anticipate eventual problems in the future but also presents a model to allocate, migrate and deallocate people in hospitals in such a way to provide benefits at patients viewpoint. Using IoT-sensors and an ARIMA-based prediction engine, we can instrument a smart hospital to collect data in time-series, so better arranging professionals and either preventing or mitigating patient treatment problems, which sometimes are related to life or death issues. In this way, we extended the concept of elasticity from cloud computing to the context of human resources management, while proposing new mathematical formalisms, algorithms, and definitions to provide a dynamic and elastic allocation of professionals in hospital environments.

We expect that the model proposed in this work can help to decrease the waiting time of patients for healthcare. The idea is to provide such facility in a transparent way for the patients, i.e., they do not need to follow additional procedures in the hospital, but only wear a wristband which serves as identification. We also hope to, with the use of ElHealth, we can identify bottlenecks in the patients care flow and help optimize processes in healthcare environments. Moreover, the provided data can also be used for decision making in terms of changes in hospital capacity and infrastructure. In ElHealth’s case study, the waiting time is decreased by 96.4% and 96.73% for reactive and proactive approaches, respectively. In the reactive approach, we had a considerable improvement in terms of waiting time reduction, with little cost increasing. On the other hand, with the proactive approach, we had more waiting time reduction, with an increase in the cost. Even with the higher cost, we believe that proactive elasticity is more efficient than the reactive approach since with a shorter waiting time, more potential health problems can be anticipated.

Although presenting encouraging results, we envisage some limitations that must be addressed on implementing ElHealth model in a real hospital environment: (i) employees and patients must carry their identification tags throughout their time in the smart hospital; (ii) ElHealth only generates notifications for human resource, but the effective movement of staff in hospital environments depends on their individual decision to follow the recommended guidance; (iii) previous installation of RTLS sensors in corridors and doors of the hospital.

As future work, we envisage the implementation of the IoT system, as well as the development of a prototype that implements all the modules and algorithms proposed by ElHealth, so enabling the deploying in a real hospital environment. Another possibility concerns the adaptation of the model to use other prediction algorithms on the proactive approach, including Artificial Neural Networks and Random Forest approaches. Also, we visualize a new approach to perform an evaluation based on a function incorporating constant, ascending, descending and wave workloads with different coefficients, since in an actual hospital environment a mix of these workloads could appear and modify over time.


The following abbreviations are used in this manuscript:

Author Contributions

G.S.F. is the designer and developer of ElHealth and wrote the majority of the paper. R.d.R.R. and C.A.d.C. contributed to ElHealth proposing and design. G.S.F. and G.G. provided related work discussion. G.S.F. and R.d.R.R. conceived and designed the experiments. G.S.F. performed the experiments. G.S.F. and R.d.R.R. analyzed the data and wrote the results section. R.d.R.R., C.A.d.C., D.G. and G.G. contributed with the paper structuring, language and detailed review. R.d.R.R. also contributed with the paper proposal, scientific contributions and conclusion evaluation.

This research was partially funded by Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES), Finance Code 001; the Foundation for Research of the State of Rio Grande do Sul (FAPERGS), Brazil; and by Brazilian National Council for Research and Development (CNPq).

Conflicts of Interest

The authors declare no conflict of interest.

Proactive Problem Solving and Creating Non-Events

proactive and reactive problem solving

By Christoph Goldenstern , Kepner-Tregoe

  • Problem Management Start reducing cost and improving IT stability with better problem management. Learn more

Proactive problem solving is all about identifying problems and resolving them before the impacts are felt by the business.  What is the impact of an event that never occurs?  There isn’t one.  While not all problems can be avoided entirely, there are often early warning signs that indicate that a problem is developing.  These ‘smoke signals’ are only of value to you if you know what to look for and can take preemptive action to avoid a firestorm.

Problem management is a process and the key to proactive problem solving is understanding each step in the process – what the signals are telling you and what you need to do with the information.

Monitoring and Instrumentation

The first step in the problem management lifecycle is all about keeping a lookout for signs of trouble. This requires having the right monitors, sensors and collectors in place to generate data about how activities and processes are performing.  You need to monitor both individual components and entire workflows to ensure you don’t miss anything.  Monitors can help you identify things like speed, accuracy, waste and operating environment characteristics that describe your process.  They can also measure things like volume, speed and quality characteristics of your production outputs and business process outcomes.

Proactive problem-solving starts with generating the right set of data about your processes and systems to give you as much early warning as you can get.  Many organizations are looking to new technologies like IoT devices, embedded sensors in manufacturing systems and standardized telemetry capabilities in their IT systems to offer additional real-time insights into their operations.

Turning Monitoring data into alerts

It’s great that you have monitors and instrumentation collecting data, but to identify problems, you will need to filter and organize the signal data to help you figure out what is ‘normal’ versus data signaling there is a problem.  This is where process control and problem-solving methodologies come in.  These methods can help you identify when something is outside of expected range of tolerance, analyze potential incidents and outages before they turn into crisis situations and identify patterns that indicate something in your process might need a deeper assessment.

The sooner you can separate incidents from events, the sooner you can diagnose and take steps to actually resolve them.  Effective problem diagnosis eventually comes down to people and how well they are able to identify “deviations” from natural performance variation.  This initial situational appraisal step is often times overlooked, but essential when wanting to take meaningful action.

There are 4 key components that your employees will put to use in diagnosing problems:

Of these components, knowledge and skill are typically the ones within your immediate control.  Successful proactive problem-solving hinges on your staff, first and foremost of all, being able to gather the most relevant data, visualize the cause-effect relationships as well as the “environmental circumstances” and from there work towards the underlying root causes.

Decision Making

Once you understand the cause of a problem, there are likely different actions you can take to resolve it.  Each alternative will likely have its own risks, costs, benefits, and implications to your organization so making informed decisions is essential.  Some of the factors that your decision makers should consider are:

  • Cost/Benefit of each alternative
  • Risk of and confidence in the proposed solution
  • Balancing short-term and long-term effectiveness
  • Full vs. partial mitigation of the business impact
  • Negative impacts of not taking action

In proactive problem-solving situations, decision makers will often find themselves weighing the impacts of avoiding the anticipated problem event against the impact of disrupting operations to avoid the event.  When this happens, risk management is essential to prepare for unintended consequences and their impact.

Proactive action

What makes proactive problems solving such a powerful tool for businesses is the ability to initiate actions BEFORE the business realizes that a problem is occurring (or reoccurring in another part of the business).  The proactive action can take many forms, from preventative maintenance, tune-ups and optimization of operations to specific process changes resulting from problem analysis.  Good hygiene practices, patching, data management and frequent health checks can prevent problems from occurring in the first place.  Applying fixes in a timely manner can help mitigate the impact of problems that already occurred.

By paying attention to the signals coming from your environment, diagnosing them quickly and making data-based decisions – you will be able to implement proactive actions to turn your potential problems into non-events.

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Main Contents:

Today’s technology companies face the difficulties of the digital era, including rising user expectations, technological changes, and severe rivalry. Hence, managers and IT professionals frequently push technology solutions to proactive problem-solving .

However, many companies still lack an appropriate and rigorous strategy for developing issues, identifying core causes, implementing necessary remedial measures, assessing the consequences, and ultimately creating a better knowledge of the task that enhances people’s day-to-day employment practices. This is where proactive problem management methods come in.

To prevent issues from happening in the first place, you must have a plan for every possible scenario. This blog post will explore the importance of proactive problem-solving and how to do it quickly, creatively, and permanently.


What is proactive problem management?

What is proactive problem-solving?

It is a problem-solving approach that focuses on identifying solutions before they occur by employing proactive problem solving techniques.

The key is not necessarily the reaction but how you react to it. Preventing issues is what proactive problem-solving entails. The emphasis is on resolving the root source of the problem rather than its consequences.

A proactive development team is a team of developers who don’t wait for solutions; they are proactive about discovering problems to solve. This involves ensuring that all team members are trained in dealing with any issue that may arise and having a backup plan in place in case something goes wrong.

When teams are proactive, they solve problems preemptively for two main reasons: firstly, so that problems don’t affect their team’s productivity or output, and secondly, their organization can gain a competitive advantage and satisfy clients.

How to build a proactive problem-solving team


Problem-solving teams are created to work together permanently

Identify root causes

Once the fundamental cause is identified, a team can remediate the defect at its source and prevent it from future occurrences.

For example in proactive problem management , developers can review the design and requirements documentation to make corrections if the defect results from a design error. If a testing mistake causes the defect, developers can update the test cases and metrics.

Hence, a proactive problem-solving team is a team that can identify the root causes.

Types of defect

[su_spoiler title=”Errors, omissions, or gaps in the original requirements.” style=”fancy”]These flaws can arise when a need is missed or forgotten when it is written incorrectly, when stakeholders are not adequately understood, or when developers are misinterpreted.[/su_spoiler]

[su_spoiler title=”Errors in the design or architecture.” style=”fancy”]These issues arise when software designers build an inefficient software algorithm or process, or when that algorithm or process fails to provide results with the requisite accuracy.[/su_spoiler]

[su_spoiler title=”Errors in the coding or implementation.” style=”fancy”]These defects include traditional bugs caused by everything from missing brackets to ungraceful error handling.[/su_spoiler]

[su_spoiler title=”Errors in test planning or test execution.” style=”fancy”]These defects are the result of inadequately tested features and functions.[/su_spoiler]

[su_spoiler title=”Errors in deployment.” style=”fancy”]An example of one of these problems is when a team allocates insufficient VM resources.[/su_spoiler]

[su_spoiler title=”Errors in the process or policies a team uses to govern the development cycle.” style=”fancy”]For example, this defect crop up when a team obtains signoffs or approvals without good design, coding, or testing review.[/su_spoiler]


How to implement proactive problem management process

Approaches to root cause analysis

The Fishbone diagram is one of the most popular techniques.

A fishbone analysis, also known as an Ishikawa diagram or a cause-and-effect diagram, is intended to assist analysts in visualizing a root cause by categorizing potential reasons into categories that branch out from the initial issue. The resultant graphic resembles a fish skeleton, thus the name.

The underlying problem or issue is usually written at the “head” of the fish. The “bones” are categories of possible causes. Then we can find out the principal reasons under each group; if necessary, the diagram might include secondary and tertiary factors.


Proactive approach to problem solving

Learn more: When do you need to hire a professional software QA team?

Proactively determine solutions

Once you’ve identified the issue, it’s time to devise a solution. It is sometimes possible to become so engrossed in identifying issues that solution definition becomes secondary.

When delivering a fix for the identified problem, we must consider two factors: resolving the issue and preventing it from recurring in the future. We’ve all seen “hotfixes” that last forever and cause technical debt.

Furthermore, enablement needs to propose solutions among team members first to ensure they continue to understand the context of the issue through the eyes of the stakeholders and connect the solution to stakeholders’ pain points. Then continuing to communicate with stakeholders proactively early and during the implementation will assist in creating further trust and enthusiasm in solutions.

Empower open communication and ongoing feedback

Proactive problem-solving begins with getting everyone on the same page about an overall plan for how you’re going tackle the project. This includes setting specific goals and objectives. Hence, communication is key here—be sure that everyone knows their role and what they are expected to do throughout the entire project life cycle.

The evolution of management is an ongoing process of open communication and feedback. Team members will receive the support needed for any improvements or changes in direction from management if necessary.

  • Feedback from all members of the development group should be given regularly, even if it’s negative or positive. Developing a clear feedback process with the team puts everyone on the same playing field for future progress.
  • Encourage open communication among peers by making space for discussion in meetings. The team may focus on what went right and wrong in a productive and non-occupational way through meetings.
  • Encourage members of the team to ask questions. Never disregard a question or make someone feel insufficient for posing one. Questions contribute to critical explanations, discoveries, and, in many cases, process improvements that the team would not have identified otherwise.

Read more: Proactive communication – successful key of all offshore development team

Characteristics of InApps’ proactive problem-solving team

We win our client’s trust with high skills, market knowledge, well-communication, and 24/7 dedicated support.


Proactive problem solver

Flexible approach

We provide each of our clients with a unique custom solution. We always have meetings to deeply understand our client’s business models and requirements or the pain points before making the proposals.

With InApps, clients can participate in projects by prioritizing, defining functions, developing iteration plans and reviews, and developing software versions that incorporate new features.

Proactive support

We handle issues and fix urgent to minimize complaints.

Our team uses platforms like Slack for internal conversations between meetings. When we require the client’s feedback, we use technologies like Basecamp to facilitate communication proactively.

This is also useful if the client needs to bring anything to our notice for discussion. We can communicate, ensure information is distributed, and plan spontaneous conversations to walk through more complex issues.

High troubleshooting skills

Need to fix bugs to launch your web/app as soon as possible? We offer dedicated teams with proactive troubleshooting skills to quickly fix all your urgent issues.

Trusted and high technical skills are the factors that made InApps build a successful high-performing offshore team .

Rapid response & quickly fix all urgent issues

We have a unique program to train talents to become a SWAT team that works effectively with clients. Our offshore team quickly solves the problems from the root causes and responds to the client within 24 hours. 

Read more: InApps’ Automation Management: Proactive Solution for Software Development

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9 Practical Tips to Choose a Mobile App Development Company for 2023

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Need to boost semiconductor fab efficiency? Look to maintenance

The global semiconductor industry is expanding at an unprecedented rate. Our analysis suggests that the industry will grow by 6 to 8 percent per year through 2030, when it will hit the $1 trillion annual revenue mark.

Demand from end markets—for uses such as computing, data storage, wireless communication, and AI—is responsible for about 60 percent of this growth. The rest comes from industries that require mature nodes (wafers less than 200 millimeters [mm]), such as the automotive, industrial, and wired-communication segments. And while the dynamics in end markets that require leading-edge chips (such as in computing and AI) may create minor fluctuations in demand, the semiconductor industry will grow overall, at least for the next decade.

The industry’s growth has been accompanied by investment. For the past 20 years, we’ve seen the leading-edge 300 mm wafer receive generous amounts of investment in the form of R&D and operational best practices, which has made it relatively easy to increase manufacturing capacity. But for wafer platforms that are 200 mm or smaller—which are frequently used for automotive, industrial, and wired communications—the story is different.

Known as “mature nodes,” chips made on these platforms have historically responded to fixed demand and flat growth. But that changed when roaring end-market demand pushed mature node–manufacturing facilities—known as “fabs”—to their full capacity.

Many fabs need a capacity boost—and quickly. Others need a performance boost to minimize the cost of tools being down for maintenance. But most fabs with mature nodes are at least 20 years old, with the equipment, processes, and productivity to match.

In this context, fab efficiency takes on outsize importance for meeting market demand and cost expectations. The potential is significant. In our experience, improving equipment reliability can help a fab enhance its tool availability (the share of time that a piece of equipment is ready to process incoming work) by more than 15 percent. When applied to bottlenecks, about 70 to 80 percent of this improvement is transformed into the overall equipment effectiveness (OEE) (an overall measure of a manufacturing operation’s utilization relative to its full potential) of the fab. As a result, the fab can quickly tap into significant latent capacity—often by more than 10 percent—without adding tools or expanding its footprint.

Achieving this requires a significant change in mindsets, processes, and systems away from reactivity and toward prevention and advance planning. Fab leaders would need to prioritize equipment recovery, conduct consistent planned maintenance, and manage parts efficiently. The insights in this article would bring the most benefit to wafer platforms of 200 mm or less, but the concepts are applicable to all semiconductor fabs.

Old habits keep fabs in reactive mode

Many fabs running mature nodes are not yet using the latest advancements in data management systems, lean manufacturing processes and practices, and Industry 4.0 tools. Cost reduction over a period of years and loss of institutional knowledge through talent attrition has weakened the capability of the production floor. Taken together, these factors make responding to maintenance issues difficult—and inefficient when it does happen.

Over time, a lack of planned, structured actions creates a cycle of reactionary maintenance for many fabs, resulting in lower availability and lower wafer output for the fab overall. In our experience, some fabs’ maintenance ratios (M-ratios) have dipped below 1.0, which means unplanned-maintenance time surpasses scheduled-maintenance time. Getting mired in this kind of firefighting diverts attention and resources from planning and implementing a strategic maintenance program. If unplanned maintenance significantly surpasses planned maintenance, the tools are effectively controlling the maintenance organization, not the other way around. Pressure to hit quarterly shipment targets can heighten the pressure on fab teams, which is not always conducive to rigorous problem solving. The result is reactionary responses to issues, which lead to longer downtimes, reoccurring failures, and reduced tool availability—ultimately affecting wafer output.

Tool availability is one of the crucial factors that affects OEE besides tool performance, wafer loading, and quality. Tool availability sometimes dips, but decision makers’ reaction to it are pivotal (see sidebar “Critical factors in overall equipment effectiveness”).

Critical factors in overall equipment effectiveness

Seven factors significantly affect fab overall equipment effectiveness (OEE).

Unscheduled downtime, as measured by the maintenance ratio (M-ratio). The M-ratio is the ratio of scheduled downtime (ideal because it’s generally preventative and planned) to unscheduled downtime for any equipment. A healthy M-ratio is greater than or equal to 4.0, meaning that 20 percent—or less—of production equipment downtime is unplanned.

Scheduled downtime. This includes a set of effective preventive actions aimed at avoiding potential tool failures, based on root-cause analysis of past failures or on OEMs’ prescribed actions.

Speed loss. This is when equipment runs more slowly than its target run rate.

Idle time. With and without wafers waiting to be processed, this is time in which equipment is available for production but has not been loaded.

Standby loss. This loss occurs due to loading and transporting wafers and parts and due to unclear alternative wafer flows when the primary path is blocked because a tool is down.

Qualification time. This is time spent after maintenance to test and ensure equipment is performing to specifications.

Engineering time. This is time spent by engineers to inspect, adjust, and improve equipment.

In theory, decision makers and practitioners would prepare and implement an effective planned-maintenance schedule, tamping down the frequency of unexpected maintenance over time. In practice, doing so is difficult because the steps in a wafer flow (the path a wafer takes in the chip-making process) are interdependent and complex—as is the equipment. As a result, we’ve observed that managers focus on firefighting in 40 to 70 percent of the instances in which a tool goes down. This approach necessarily drives them to prioritize short-term wins. In the remaining cases, managers may try to fulfil a plan, but those efforts tend to be doomed because firefighting consumes a disproportionate amount of resources.

Transforming fab availability for the better

In our experience, tool availability can be improved in most cases by addressing unplanned maintenance. We found that an hour of planned maintenance can typically save three to four hours of unplanned maintenance.

A best-in-class maintenance program for semiconductor fabs consists of three overarching elements: resolve problems quickly with short-loop root cause analysis (RCA), build a strong planned-maintenance foundation, and manage parts efficiently.

Resolve problems quickly with short-loop RCA

Short-loop root cause analysis in context.

Our discussion is focused on short-loop root cause analysis (RCA). However, there are three other effective approaches to solve problems and reduce unplanned downtime in semiconductor fabs.

  • Reliability-centered maintenance. The most comprehensive of the approaches, this technique involves analyzing failure modes for critical tools’ main components and designing measures to mitigate the risk of failure.
  • Weeklong kaizen (that is, continuous improvement). Teams that use this approach first investigate failure modes that have not yet been identified to understand their possible causes and then validate and identify solutions to those causes. Depending on how severe the failure modes are, kaizen events can be as brief as a day.
  • Solution working session. Teams use this approach when they already know the main root causes of a problem and need to focus on devising solutions.

Short-loop RCA, an agile problem-solving technique that can help teams break down causes and generate solutions, can help teams arrive at a shared understanding of the main causes of tool downtime and develop plans to address them. The approach can resolve moderately complex maintenance problems, which, in our experience, account for about half of the issues that arise in corrective maintenance (see sidebar “Short-loop root cause analysis in context”).

Semiconductor fab teams can use short-loop RCA to find the best way to resolve problems that arise with specific tools (exhibit).

For each piece of equipment targeted for root-cause analysis, teams would articulate the problem to be addressed and assess its frequency and impact. Teams would also identify the internal and external resources needed to investigate the problem.

The next step would be to collect historical data on the piece of equipment, reconfirm the nature of the problem with the relevant machine technician, and identify any adjacent issues.

To conduct the RCA, the working team would gather in a dedicated space to develop hypotheses, employ five-why analysis (a particular interrogative problem-solving technique used to identify the source of an issue), prioritize possible root causes, and brainstorm potential solutions. The outcome of this exercise could be a course of action that’s likely to succeed. If not, the team could go back to using historic data to generate insights that could translate into solutions.

As a final step, the team would implement their solutions and track their efficacy. Documentation here will be crucial, especially if teams have insights about whether a particular piece of equipment’s problems are systemic and whether they have implications for other areas of the business such as the fab’s strategy on spare parts.

Analytical frameworks such as fuzzy logic (which uses an imprecise set of equipment notes to identify key tool downtime issues) can help teams recognize the most common problems based on downtime hours and frequency. Ad hoc teams—including suppliers and tool vendors, if needed—can then be assembled for workshops focused on pinpointing the root causes of each issue (see sidebar “Applying fuzzy logic to maintenance in a semiconductor fab”).

Applying fuzzy logic to maintenance in a semiconductor fab

Tools in fabs maintain a running log of tool downtime incidents (that is, when equipment experiences running stoppages) as they occur. Over time, the amount of raw data in logs tends to swell and suffer from a lack of structure and organization. As a result, decision makers on the floor struggle to prioritize issues to maximize the efficacy of tool recovery.

One fab used analytical techniques such as fuzzy logic (that is, using an imprecise set of equipment notes to identify key tool downtime issues) to comb the entire dataset—the text of the log entries—for keywords associated with different types of errors, including the abbreviations used by technicians and equipment engineers. Fuzzy logic (and similar analytical tools) can reduce the time it takes to analyze the notes from a few hours to a few minutes and provide additional insights through customized dashboards.

The fuzzy logic framework helps to plot the total amount of downtime caused by different incidents against the number of times those incidents occurred (exhibit).

The issues that were both high impact (as measured by the amount of downtime they caused) and high frequency were marked as a bigger priority and received the greatest level of resource allocation. The issues that happened infrequently but had high impact received attention from teams in workshops dedicated to understanding the root causes behind those issues. Technicians received training to address the remaining issues.

Of course, implementing these habits requires the right expertise, resources, discipline, and enforcement. One benefit of having this consistent framework is that fab teams can use it with the talent they already have. It can also help offset the effects of attrition of experienced staff. Fab teams could allocate resources toward fully resolving any problems teams identify so that each issue can be addressed only once. The knowledge captured from the experience can be used to train staff and resolve issues in the future.

Build a strong planned-maintenance foundation

In our experience, effective planned-maintenance programs increase fab availability by 5 to 7 percentage points. Although the rewards are significant and the steps—planning, implementation, and continuous improvement—are straightforward, planned-maintenance programs are difficult to establish consistently in many fabs. The status quo of resource-sapping firefighting is one hurdle, as is outdated maintenance procedures.

Commitment from the top down, accountability, and buy-in from the stakeholders leading the effort can help a planned-maintenance program take root. The foundation for a successful planned-maintenance program is an opportunity for the fab team to gradually adopt the right practices. Each milestone allows teams to recognize the impact of their effective preventive work. Standards and ratings, such as bronze, silver, and gold, for different tasks can motivate teams to improve and achieve higher levels of performance.

The most successful programs have weekly team meetings to review progress measured by KPIs and to conduct feedback that helps teams plan for further improvements. Planning in these programs involves scheduling maintenance in advance and notifying the relevant stakeholders. The most effective teams schedule maintenance activities for the next two weeks and give stakeholders at least ten days of notice.

The implementation stage is focused on checking in with stakeholders and removing hurdles to planned-maintenance activities. Best-in-class teams check in on at least two planned-maintenance activities on each shift and resolve roadblocks within 30 minutes.

From there, continuous improvement is an ongoing process of adopting—or rejecting—possible improvements. The most effective teams make adoption and rejection decisions within a week of receipt so that new suggestions are rapidly integrated into the way they work.

Manage parts efficiently

Parts management can be a silent killer of fab availability because replacement parts are not always associated with acute problems. But a solution—in this case, a part—that’s delayed or unavailable prolongs or worsens the initial problem. Procurement teams may need to tap alternate suppliers, parts may not be accounted for in inventory, and multiple teams or team members may work on the same issues without coordination or clear lines of responsibility.

In our experience, a missing or delayed part can create tool downtimes that are up to six times longer than the expected downtime if the part was in stock. Spread across an entire fab, parts management is an open-ended—and critical—challenge.

Because challenges related to parts management are variable and often open-ended, a focus on identifying the right issues is important. Disconnects often occur—for many different reasons. As a result, procurement teams may end up focusing on sourcing nonpriority parts while inventories of critical, work-stopping parts remain unfilled.

A control tower—a cross-functional team that uses real-time data to make decisions quickly—can help analyze problems and create and implement solutions regarding parts management. The control tower can transparently resolve immediate issues and provide systemic fixes that later become part of a strategic tool set to improve processes and manage suppliers. And because of the complexity and regional specifics of the semiconductor supply chain, a parts management risk team would be an important operator of the control tower.

Demand for semiconductors will continue to grow, and fabs are feeling the pressure. One way to relieve the pressure, particularly for fabs producing wafer sizes of 200 mm and smaller, is to shift from reactive firefighting to proactive management of equipment recovery, planned maintenance, and parts management. Entire fabs—not to mention the global semiconductor industry—stand to benefit.

Ryan Fletcher is a partner in McKinsey’s Southern California office, where Abhijit Mahindroo is a senior partner; Yorgos Friligos is an associate partner in the Miami office; and Joydeep Guha is a senior expert in the Bay Area office.

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Reactive or Proactive? How Robots Should Explain Failures

As robots tackle increasingly complex tasks, the need for explanations becomes essential for gaining trust and acceptance. Explainable robotic systems should not only elucidate failures when they occur but also predict and preemptively explain potential issues. This paper compares explanations from Reactive Systems, which detect and explain failures after they occur, to Proactive Systems, which predict and explain issues in advance. Our study reveals that the Proactive System fosters higher perceived intelligence and trust and its explanations were rated more understandable and timely. Our findings aim to advance the design of effective robot explanation systems, allowing people to diagnose and provide assistance for problems that may prevent a robot from finishing its task.

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The role of customer service in building brand reputation.

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Cofounder and CEO of PissedConsumer , a review platform that helps consumers be heard and brands improve their customer service processes.

Getting customer service right is something that I've always been passionate about. Even from the early days of starting my own company, I knew that putting customers first would be key to finding success. Now, all these years later, I still truly believe there is no better way to establish a strong, trusted brand than by making outstanding customer service a top priority.

So many seem to view customer service as an "extra" rather than an essential part of managing the business's reputation. But in reality, how a company treats its customers on a daily basis is what the brand represents. More so now than ever before, people aren't just buying products or services—they’re buying into the overall experience. If the experience is positive every time, they will keep coming back. And they'll spread the word online through reviews too.

Understanding The Connection Between Service And Reputation

There is a direct correlation between how customers experience your company through interactions and transactions and the reputation your brand develops online through reviews and word of mouth.

Poor service leaves customers frustrated and unwilling to recommend your brand to others, whereas going above and beyond builds loyalty, trust and positive associations with your name. With 91% of surveyed consumers stating that their purchase decisions hinge on what they read in online reviews, neglecting to address this trend is a costly mistake.

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Brand reputation represents the perception and goodwill your company has built up over time based on the collective experiences of all your customers, both good and bad. It speaks to your organization's values, reliability and ability to consistently meet or surpass expectations. A strong reputation rooted in outstanding service sets your business apart from the competition and gives people the confidence to buy from you again without hesitation.

Prioritizing Proactive, Empathetic Service

One of the most crucial aspects of online reputation management is focusing customer service efforts on being proactive rather than reactive. Waiting for issues to arise and complaints to come in before addressing problems does nothing to improve your reputation. Instead, anticipate potential customer pain points, take preventative measures and reach out carefully to understand customer needs and catch minor problems before they escalate.

Empathy is also key—seeing the customer's perspective, expressing understanding through mirroring and verbal nods, genuinely apologizing for mistakes and working with patience and care to solve their specific problem helps them feel heard. I cannot emphasize enough how important it is for customers to feel that they are truly being listened to.

Learning From Mistakes And Complaints

No business is perfect, and the mature mind acknowledges this by viewing complaints and failures not as liabilities, but as opportunities. Indeed, as they always say, every criticism is a learning opportunity, and a proactive leader will conduct thorough root cause analysis to uncover recurring problems, collect honest customer feedback without being defensive and use this knowledge gained to make process and product improvements.

Taking responsibility for errors with a sincere apology goes a long way. But words must be followed by visible action. I've seen some turn reviews around by documenting the changes made and following up personally with impacted customers to ensure satisfaction. The goal is to emerge stronger and smarter rather than blindly repeat past missteps.

It's reassuring to see companies turn things around when they recognize they have a problem. A subscription-based coffee business I know was struggling with bad reviews until the founder completely overhauled their customer service system and took on board the main issues his customers were facing. In a matter of months, their ratings improved and not a single, new negative review appeared under their name—their reviews are now glowing with praise.

Consistency Matters

Remember that customers see a company as a single entity, not as a cluster of individuals with diverse attitudes and aptitudes. Customers expect the same level of service whether they interact with you in person, over the phone or online.

Consistency plays a big part in building trust in customers, and getting it right demands a clear road map for your customer service team. With customers reaching out via direct message, email, phone and social media, the cumulative experience is going to be the determinant of customer satisfaction and a healthy customer relationship.

Steps For Success

Here are a few of my recommendations, in brief, for ensuring good customer service that builds a strong reputation:

• Simplify the customer journey wherever possible so expectations are clear and you deliver exactly what you’ve promised.

• Ongoing staff training is crucial—take time to develop skills such as active listening, problem-solving and product knowledge.

• When issues do arise, have a clear, complaint resolution process to ensure a fast response and resolution.

• Monitor reviews and don't hesitate to reach out to dissatisfied customers to ask them what needs to be done.

• Seek regular feedback, whether through surveys, focus groups or calls—never rest on your laurels.

Always be prepared to solve issues quickly. Don't evade difficult conversations. Have them. Understand. Learn. If you’re in the wrong, then own it and say so. These are the steps needed to improve customer service. Your online reputation is just a mirror image of how you conduct business.

Building Trust Is An Ongoing Effort

Great customer service with a dedicated customer focus is paramount. When a single poor interaction can go public and repel an exponential number of potential customers, constant refinement of your customer service based on real feedback is crucial. Do this well and see customers come back. And bring others along too.

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Introduction, conclusions, acknowledgements, declaration of conflicts of interest, declaration of sources of funding.

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Virtual wards for people with frailty: what works, for whom, how and why—a rapid realist review

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Maggie Westby, Sharea Ijaz, Jelena Savović, Hugh McLeod, Sarah Dawson, Tomas Welsh, Hein Le Roux, Nicola Walsh, Natasha Bradley, Virtual wards for people with frailty: what works, for whom, how and why—a rapid realist review, Age and Ageing , Volume 53, Issue 3, March 2024, afae039,

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Virtual wards (VWs) deliver multidisciplinary care at home to people with frailty who are at high risk of a crisis or in crisis, aiming to mitigate the risk of acute hospital admission. Different VW models exist, and evidence of effectiveness is inconsistent.

We conducted a rapid realist review to identify different VW models and to develop explanations for how and why VWs could deliver effective frailty management.

We searched published and grey literature to identify evidence on multidisciplinary VWs. Information on how and why VWs might ‘work’ was extracted and synthesised into context-mechanism-outcome configurations with input from clinicians and patient/public contributors.

We included 17 peer-reviewed and 11 grey literature documents. VWs could be short-term and acute (1–21 days), or longer-term and preventative (typically 3–7 months). Effective VW operation requires common standards agreements, information sharing processes, an appropriate multidisciplinary team that plans patient care remotely, and good co-ordination. VWs may enable delivery of frailty interventions through appropriate selection of patients, comprehensive assessment including medication review, integrated case management and proactive care. Important components for patients and caregivers are good communication with the VW, their experience of care at home, and feeling involved, safe and empowered to manage their condition.

Insights gained from this review could inform implementation or evaluation of VWs for frailty. A combination of acute and longer-term VWs may be needed within a whole system approach. Proactive care is recommended to avoid frailty-related crises.

This rapid realist review covers how, why and in which contexts virtual wards may be effective, and informs service planning.

Frailty virtual wards may provide short-term acute care for frailty crisis or longer-term proactive care to prevent a crisis.

Evidence-based theories show how virtual ward components combine to deliver frailty care and empower patients and caregivers.

A whole-system approach is key to good virtual ward frailty management, involving continuity of care (referral and discharge).

Sustainability of virtual wards requires a focus on proactive care to prevent frailty crises and reduce hospital admission.

Frailty is a dynamic and multidimensional syndrome associated with age-related decline in multiple physiological systems [ 1–3 ]. People with frailty are vulnerable to unpredictable deteriorations in health, and minor stressor events can lead to medical crises, from which the person does not fully recover [ 1 ]. Medical crises in people with frailty are associated with poorer outcomes [ 4 ], and can lead to increased care dependency and acute hospital admission [ 1 , 5 , 6 ].

The UK has an ageing population with an increasing prevalence of frailty [ 1 , 7 , 8 ], and the need for innovation in frailty management is recognised [ 5 , 9 ]. People with frailty form a diverse group, requiring different levels of support from health and social care. Their dependence on others for activities of daily living may put them at a risk of hospital admission and delayed discharge because of a lack of community services [ 10 ].

Delivering services to support people living with frailty requires a multidisciplinary team (MDT) that can provide an iterative, tailored, whole-person approach to diagnosis, assessment and treatment, aiming to promote function and independence, intervene with crises, and prevent exacerbations [ 1 , 5 , 11–13 ]. Virtual ward (VW) models combine components of care under a common scheme, delivering multidisciplinary care to patients in their own homes, aiming to mitigate their risk of unplanned hospitalisation.

VWs for people living with frailty or long-term conditions were first introduced in the 2000s in the UK [ 14–17 ]. Further development of frailty VWs was influenced by seminal work from Ireland by Lewis et al. [ 16 ] Building on this earlier work and experience of COVID-19 VWs [ 18 ], NHS England (NHSE) issued guidance on short-stay (a few days) VWs for patients with ‘acute exacerbations of conditions related to frailty’, with planned roll-out [ 19 ]. The term, ‘virtual ward’ is, however, used to cover a variety of models.

Evidence of VW effectiveness is limited. Five studies [ 15 , 20–23 ] and one systematic review [ 24 ] that compare VWs with usual care (one UK-based [ 15 ]) report inconsistent findings. Suggested explanations for poor effectiveness include failure in MDT functioning [ 15 ], indicating there may be crucial mechanisms by which VWs ‘work’ to improve patient outcomes.

Multidisciplinary VWs are being introduced, driven by the limited availability of acute hospital beds, and the desire to treat people in their own homes, but initiatives are complex and diverse. As part of a larger programme investigating combined interventions for people with frailty in the UK, our research questions sought, first, to determine what multidisciplinary VW models were in operation in the UK; and second, to understand how and why they may work (or not) within their specific contexts (rather than whether they work), focusing particularly on frailty VWs.

Rapid realist reviews are a suitable framework for answering such (how and why) questions because they incorporate a range of evidence and account for contextual variation within and across VW models. Rapid realist reviews allow investigation of a defined topic area to inform policy by identifying key components of services that should be customised to achieve effectiveness [ 25 ].

Our intention to focus on UK evidence was to reduce the large variations potentially introduced by different health and social care systems worldwide.

This rapid realist review aimed to synthesise relevant evidence, producing initial programme theories explaining ‘what works, for whom, and in what circumstances?’ that can be tested empirically in future work [ 26 ].

Preliminary scoping of the literature informed development of the review protocol, including our definition of VWs. We conducted two rounds of literature searching and three rounds of realist synthesis. Stakeholders were engaged in both. For further details, see below and Appendix I [ 25 , 27 ].

Inclusion criteria

We used a broad definition of (multidisciplinary) VWs, limited to three essential components ( Box 1 ; Appendix II ) [ 17 , 24 , 28 ]:

Care is provided to the patient in their own home in the community.

A multidisciplinary team makes decisions/plans care remotely from the patient.

The MDT provides oversight of patient care.

‘Virtual’ refers to the way MDTs plan each patient’s care, remote from the patient—as opposed to remote patient monitoring [ 28 , 29 ] ( Appendix II ). We placed no restrictions on the timelines and aims of the VWs, because we intended to identify different models.

Other inclusion criteria were: (i) people with frailty or multi-morbidities; (ii) set in the UK; and (iii) relevance, i.e. whether data can contribute to theory development and refinement (rapid realist review).

We accepted authors’ definitions of frailty, and included populations with multimorbidities where frailty was not reported. Frailty and multimorbidity, although different clinical conditions, are interrelated and both are predictors and outcomes of each other [ 3 ] ( Box 1 ).

BOX 1: Definitions

Context: backdrop of the intervention and variations of this across sites, which existed before the VW implementation and are outside of the mandate of service redesign (e.g. policy, staff skills, IT systems).

Mechanism: reasoning of stakeholders in response to resources offered by the intervention (e.g. trust and motivation to act).

Outcome: includes intended and unintended outcomes of interest, such as: hospital admissions, safety, clinical outcomes, resource use, patient and caregiver satisfaction, etc.

CMO Configurations (CMOCs): propositions explaining how the interaction between contexts and mechanisms can lead to outcomes of the intervention (i.e. VWs for frailty).

Virtual ward: cares for patients in their own homes (in the community) and there is an MDT that makes decisions/plans patient care remotely (virtually) and the MDT provides oversight and integration of patient care:

The virtual part of a VW is the way multi-disciplinary teams of health and care professionals plan each patient’s care, using digital technology to help them meet.

Multidisciplinary team (MDT): people with frailty—a multidimensional condition—require care tailored to their needs from a multidisciplinary, integrated health and social care team. This MDT may include primary care, community care and secondary care professionals, alongside social workers, pharmacists, physiotherapists, mental health professionals, voluntary sector staff, etc.

Frailty: a state of increased vulnerability to unpredictable deterioration in health, associated with an age-related decline in multiple physiological systems, which puts the person at high risk of frailty crises. In people with severe/moderate frailty, crises may be triggered even by minor events, leading to subsequent adverse outcomes and acute hospital admission:

Frailty crises include severe falls, delirium, and sudden immobility.

Triggers can be something small, such as a minor infection or injury, constipation, new medication, a visit to A&E (or can be bigger events/acute illnesses)

‘Acutely unwell’: person with frailty who is either at high risk of a frailty crisis (and requiring preventative treatment) or already in-crisis (and requiring reactive treatment initially).

For theory building, any type of evidence and research design was eligible, from peer-reviewed papers to grey literature, including service case reports, videos and blogs.

We intended to restrict the review searches to the UK. However, alongside limited UK evidence, the first main search identified the papers of Lewis et al., reporting work from Ireland that operated within similar population demographics, with some similarities in healthcare provision (a public-private system), and we deemed it relevant to include this work because it had been influential for frailty VWs in the UK. Four further Irish documents did not meet the inclusion criteria.

We excluded VWs in care homes, children, people with COVID-19 or a single condition (e.g. cystic fibrosis).

Searching and selection of documents

Literature searching was done iteratively within two rounds (see Appendix III for the full search strategy and Figure 2): for the rapid realist review, the first main search (to 8 November 2021) was to find relevant ‘core’ documents from which we extracted if-then-because statements in an iterative way. Then after synthesising the data, we conducted an updated and revised search (to 27 June 2022) to find further evidence and to address evidence gaps identified by stakeholders, thereby refining and expanding on our original programme theories.

During initial literature scoping for protocol development, we noted debate around VWs being a distinct model of care compared with hospital-at-home. Following advice from topic experts, we initially agreed the ‘hospital-at-home’ in scoping documents did not fit our VW definitions, and therefore excluded studies reporting hospital-at-home. However, during engagement meetings, stakeholders shared documents not found by either scoping or first round searches, which they considered pertinent. This included a hospital-at-home model that met our frailty VW definition [ 30 ]. We then broadened the search terms to include hospital-at-home variations and removed the exclusion criterion. Hospital-at-home models were eligible if they met our definition of VWs ( Appendix II ).

In both rounds, we searched Ovid multi-file databases (MEDLINE, Embase, PsycINFO), using terms relating to multidisciplinary teams, remote/virtual care, frailty/multimorbidities/older people and the UK. In the second search, we added the extra terms, examined reference lists of systematic and other realist reviews, searched for grey literature and conducted a forward citation search using included documents.

Title and abstracts, and full texts were screened in duplicate for the first search, with discrepancies resolved by a third reviewer, identifying relevant and information-rich ‘core’ documents. Following the second search, results were single screened in reverse chronological order, back to 2018 and full papers assessed in duplicate.

Data extraction and synthesis

Eight ‘core’ documents from the first search were reviewed by three authors, who extracted data as ‘if-then-because’ statements that captured relevant causal insights; we grouped these statements thematically into 21 topic areas. These topics informed the first stakeholder meetings.

Next, we developed ‘context-mechanism-outcome configurations’ (CMOCs) ( Box 1 ). Synthesis was iterative: preliminary CMOCs were articulated, using the extracted data and stakeholder engagement, and then elaborated and refined from further included documents. This process ( Appendix IV ) resulted in 12 CMOCs ( Appendix V ), which we summarise below, alongside implications for practice.

We assessed the trustworthiness, plausibility and coherence of the data and checked our findings with stakeholders. We did not formally appraise the evidence quality using checklists or assess confidence in the evidence because these tools could not capture the different ways that documents contribute to a programme theory, and we were generating theories, rather than testing them [ 31 ].

Stakeholder engagement

Stakeholders were recruited through our organisation’s patient and public involvement (PPI) programme. They included one GP with frailty expertise and COVID VW experience, three carers of people with frailty, three patients (one with frailty, and one with COVID VW experience), a general practice administrator with VW experience, and two geriatricians known to the team, who had frailty VW experience.

We engaged with stakeholders at two timepoints. First, we developed and presented the patient pathway within a VW ( Figure 1 ) to one clinician and two PPI contributors, requesting feedback, and facilitated discussion on the topic areas of the if-then-because statements. This generated further statements, based on stakeholder experience, derived from the meeting transcripts.

Patient pathway within a VW model.

Patient pathway within a VW model.

Meanwhile, building on VWs for COVID-19 [ 18 ], NHSE had issued guidance on short-stay (a few days) VWs for patients with ‘acute exacerbations of conditions related to frailty,’ with planned roll-out of these [ 19 ]. Therefore, in the second round of stakeholder engagement, we presented draft CMOCs and made comparisons with NHSE guidance.

The second round involved three clinicians and five PPI contributors. Based on their feedback and the NHSE guidance, we broadened the review to acute, hospital-at-home models, provided they met our VW inclusion criteria.

We describe document characteristics, the different VW models, and summarise 12 CMOCs under three main themes. Full details of the CMOCs are in Appendix V .

Document characteristics

The search process is depicted in Figure 2 . Details of included documents are in Appendix VI . We included eight core documents from the first search [ 16 , 17 , 32–37 ] and 20 documents from stage 2 (nine peer-reviewed [ 15 , 30 , 38–44 ] and 11 grey literature [ 6 , 28 , 29 , 45–52 ]). Ten documents report on four studies [ 15–17 , 29 , 30 , 36–38 , 44 , 49 ].

Flow diagram.

Flow diagram.

Thirteen documents specifically included people with frailty; [ 6 , 16 , 28–30 , 36 , 37 , 43–45 , 49 , 50 , 52 ] four described patients as ‘frail’ or measured frailty; [ 35 , 41 , 42 , 46 ] eight included people with at least one chronic condition with high risk prediction scores for hospitalisation; [ 15 , 17 , 32 , 33 , 38 , 39 , 47 , 51 ] two had implied chronic conditions and complex needs; [ 34 , 48 ]; one listed urgent care needs [ 40 ]. Most studies were conducted over 5 years ago, and four were in 2020–2022 [ 28 , 35 , 45 , 49 ].

VWs provide care at home for people with frailty or chronic conditions at high risk of hospital admission. We distinguished two main VW models: model 1—longer-term (more than 3 weeks, typically 3–7 months) with mainly proactive care for people at high risk of a crisis, and model 2—short-term VWs (1–21 days) with mainly reactive care for those already in-crisis ( Table 1 , Box 1 ).

Fifteen documents describe longer-term VWs [ 15–17 , 28 , 32–34 , 36–39 , 41 , 43 , 50 , 51 ] ten short-term VWs, [ 29 , 30 , 35 , 40 , 42 , 44 , 45 , 47 , 49 , 52 ], two both models [ 6 , 46 ] and one was unclear [ 48 ].

Originally, longer-term VWs (e.g. [ 17 ], model 1a) were intended to reduce acute hospital admissions by proactively treating older people with chronic conditions at a high risk of admission. Patient selection was usually based on risk prediction modelling, aligning with UK policies for caring for vulnerable people [ 53 ].

Subsequent VWs focussed on frailty (e.g. [ 6 , 16 , 28 , 50 ]; model 1b) reflecting 2016–17 changes in UK policy and emphasis [ 2 , 54 ]. VWs aimed to assess and stabilise people living with frailty at high risk of a crisis, exemplified by Irish VWs [ 16 ], which influenced development in the UK, including NHSE guidance [ 19 ]. Reactive care was first offered to alleviate any acute exacerbations, before focusing on proactive care to reduce risk of future crises, for example, based on the Comprehensive Geriatric Assessment (CGA) [ 55 ].

Longer-term VWs are alternatives to usual care in the community for people at high risk of exacerbations. In some VWs, a traffic light system (red/amber/green) is used to prioritise assessment, monitoring, and intervention according to risk of deterioration [ 16 , 17 ].

Short-term VWs admit people with frailty already in-crisis or very near to a crisis (e.g. [ 30 ], model 2), offering, principally, acute reactive care. Proactive care may be started in the VW if there is time, alongside planning for continuity after discharge to primary care. Short-term VWs are alternatives to inpatient hospital treatment. NHSE guidance on frailty VWs is based on this model [ 19 ].

Both models admit patients who are frail and ‘acutely unwell’ ( Box 1 ), and aim to reduce system burden and improve frailty care. One is better suited for preventing crises, the other for in-crisis management. Both models aim to prevent hospital admission, either by preventing the crisis from occurring or by treating the crisis in the community instead of hospital admission.

Boundaries between models were sometimes blurred, for example, in one longer-term VW, patients with frailty who became ‘unstable’ were admitted to hospital; [ 36 ] one short-term VW additionally aimed to be proactive; [ 49 ] and some VWs were difficult to classify (e.g. [ 34 , 39 , 42 , 43 , 47 , 48 ]). Two studies described both a short-term reactive ward and a longer-term proactive ward, potentially working in tandem [ 6 , 46 ](South Sefton) ( Appendix VI ).

CMOC Theme 1: VW building blocks ( Table 2 )

CMOCs in this section describe underlying structures essential for VW operation. They include common standards agreements, information sharing processes, MDT composition and co-ordination, and MDT meetings (or ‘Virtual Ward Rounds’). These CMOCs are not limited to a particular model of VW, and may also apply to non-frailty multidisciplinary VWs.

Summary of CMOCs for Theme 1: VW building blocks

Implications for VW operation

Sufficient motivation and co-operation amongst the teams involved are needed to develop and successfully introduce common standards agreements [ 15 , 16 , 32 , 35 , 42 , 46 ]. These may need review and revision as the VW becomes more established [ 6 , 43 ] [Clinician].

Similarly, introducing effective IT integration requires perseverance and collaboration amongst organisations [ 15 , 32–34 , 43 , 44 , 47 ]. Ineffective information sharing can mean duplication of effort and ‘silo working’ [ 17 , 33 ]—frustrating for both staff and patients—and may impede the timeliness and/or appropriateness of decision-making [ 33 , 34 , 39 , 43 ].

Team composition varies according to the aims of the particular VW model and local patient need. It may include geriatricians, physiotherapists, pharmacists, social workers, mental health professionals, voluntary sector, community organisations and other clinical specialities (e.g. cardiology) [ 6 , 16 , 17 , 28 , 29 , 32 , 35 , 41 , 44 ]. The MDT usually meets remote from the patient, with decisions enacted by community teams [ 6 , 16 , 43 ]. The role of the VW co-ordinator is pivotal [ 16 , 28 , 38 , 47 , 50 , 51 ]. Successful communication and care documentation are essential [ 6 , 17 , 32 , 42 ]. Ideally, all professionals feel confident they will have accurate information when they need it, facilitating prompt, well-informed decisions on patient management [ 16 , 29 ].

The VW facilitates shared learning across traditional role boundaries, enhancing collective capacity for patient care [ 35 , 43 , 45 , 51 ]. Co-location of VW team members could increase their connectedness and joint working [ 6 , 41 ]. However, poor understanding of VW aims could lead to role protectionism that undermines MDT functioning [ 33 ].

Effective MDT meetings are crucial for the VW to function as a forum to integrate and prioritise patient care [ 6 , 16 , 17 , 43 , 45 , 51 ]. Online meetings can facilitate attendance and save time, but professionals involved must have time and capacity to attend [Clinician]. Disparity in attendance could delay decision-making and demotivate attendees [ 17 , 32 , 38 ].

CMOC Theme 2: VW delivering the frailty patient pathway ( Table 3 )

CMOCs in this section concern how the VW can optimally deliver care for people with frailty who are acutely unwell. CMOCs could be directly (*) or indirectly (#) applicable to multidisciplinary non-frailty VWs. CMOCs comprise patient selection*, comprehensive assessment and evaluation # , medication management*, intensive case management* and proactive care # .

Summary of CMOCs for Theme 2: VW delivering the frailty pathway

These CMOCs are mainly informed by evidence related to 16 longer-term VWs, especially for patient selection, intensive case management and proactive care. Short-term evidence also contributed, and the Irish papers added explanatory information on how VWs function.

Implications for delivering patient care

Patient selection processes should be coherent with the aims of the VW and its common standards agreements. Professionals’ perceptions that the VW is prioritising the ‘right’ patients—taking an acceptable stance on risk of harm and likelihood of benefit—may be important for their trust and motivation. Conversely, the benefits of working in the VW may become less clear if patient selection is ineffective [ 16 , 17 , 32 , 33 , 45 , 47 ].

The most appropriate member of the VW ensures that the patient and/or caregiver are involved in the development of the management plan through a shared decision-making process. The VW coordinator ensures that the wider team is involved in refining and delivering it [ 16 , 28 , 33 , 47 ]. The VW can be a supportive learning environment that facilitates this way of working—however, this could be threatened if key team members cannot maintain regular communication necessary for integrated case management [ 17 , 47 ].

VWs aim to stabilise people living with frailty and mitigate future risk by facilitating timely, proactive interventions [ 6 , 15–17 , 28 , 32 , 33 , 36 , 39 , 41 , 44 , 46 , 47 , 49 ], and to provide as-needed acute care for crises [ 6 , 16 , 44 , 49 ]. Depending on the VW model, there is greater emphasis on one or other type of care. Some VWs may have insufficient capacity or time to stabilise patients and rely on GPs to continue the treatment plan, which requires mechanisms ensuring good continuity of care at discharge from the VW [ 6 , 30 , 49 ](Midlothian).

CMOC Theme 3: patient and caregiver experience ( Table 4 )

Theme 3 is concerned with patient and caregiver experience, and CMOCs comprise improved communication, at home instead of hospital and caregiver experience. Evidence relating to patient and caregiver experience was limited overall. These CMOCs are derived from evidence from both short-term and longer-term VWs. CMOCs could be directly (*) or indirectly (#) applicable to multidisciplinary non-frailty VWs.

Summary of CMOCs for Theme 3: patient and caregiver experience

Implications for patient and caregivers’ experience

VWs should aim to include the patient and/or their caregiver in decision-making without over-burdening them. Improved communication between the patient/caregiver and the VW, via a known point of contact (e.g. a well-informed, reliable co-ordinator), is expected to be reassuring [ 15 , 28 , 48 , 52 ]. It is important to have clear communication on discharge and its timing [ 16 , 44 ].

Caregivers and patients ideally feel more confident because of VW intervention [ 16 , 48 ]. However, revoking VW support at discharge may result in increased anxiety, especially if the patient or caregiver does not feel well equipped by the VW to continue at home, or proactive care is not established [ 44 ]. Ideally, patients feel empowered to manage [ 33 , 38 , 46 ], but conversely, if VW input means patients feel less enabled, they could lose confidence, potentially increasing stress for both patients and caregivers [ 33 ].

Effective continuity of care with primary care is important at discharge for the patient/caregivers to regain confidence living outside the VW [ 15 , 16 , 47 ]. Communication with the GP should support continuation of the management plan, otherwise, patients and caregivers may be left with uncertainty and heightened anxiety [ 44 ].

In some cases, the home environment may not be safe, and hospital may be more suitable [ 44 ]. It may be that caregivers are unable to take on additional responsibilities, for example, for patients experiencing delirium or other frailty crises, or the home setting is unsafe for delivering acute interventions [ 44 ].

This rapid realist review drew from 28 documents and the experiences of clinicians and PPI contributors to identify different VW models operating in the UK. Evidence from all models, including some from Ireland, were used to explain how multidisciplinary VWs can be effective.

In a field where what constitutes a VW is uncertain, we refined, with stakeholders, a definition of VWs as a service delivery model in which an MDT meets and plans patient care remote from the patient. The VW co-ordinates multidimensional interventions at home for people with frailty who are acutely unwell (at a high risk of crisis or in-crisis).

Summary of findings

We identified two main VW models, which differ in their aims, duration and patients admitted, but in practice, show overlap. Longer-term VWs provide proactive treatment to stabilise the medical and functional status of people living with frailty and reduce the risk of a crisis in people at high risk of deterioration. Short-term VWs (1–21 days) provide acute reactive care to people with frailty already in-crisis, and, if time, start proactive care before discharge to GP care. However, although service models may be different, their populations are closely related: frailty is a continuum, and a minor event can ‘tip’ somebody at high risk of a crisis into crisis. Both models treat patients who would otherwise be in hospital, but at different stages: one seeks to prevent the crisis, and the other provides acute reactive care outside of hospital.

Both models treat people with frailty who are acutely unwell, and each requires a remote MDT to plan patient care. MDT composition will be similar, and appropriate for the VW population, and VW co-ordinators are needed in each. The two models have contributed complementary information to the findings of this review. There are differences in the selection of patients and in post-discharge care: longer-term VWs can discharge to standard primary care as their patient would be stable. For short-term VWs, more responsibility would be passed to primary care because of the need to start/continue proactive care.

Fundamental to VW functioning are key building blocks with their underlying mechanisms. These comprise robust information sharing and common standards agreements that the teams can understand and work within; multidisciplinary teamwork, featuring remote MDT decision-making meetings alongside in-person care; and effective co-ordination, with links to external services (such as out-of-hours). Also important are good relationships within the VW, in-person contact between staff and patients, and involvement and inclusion of patients and caregivers.

Pertinent mechanisms relate to the motivation of professionals to work together and their ability to do so. Ideally, the VW operates as a ‘team-of-teams’ providing mutual support, trust in shared goals and benefit from reciprocal learning. Perceptions of patient safety and benefit, starting small and taking time to introduce formal agreements and learn new ways of working may be necessary for professionals to ‘buy in’ to the VW model. Also essential is good communication between patients, caregivers and staff, and enabling them to feel safe at home and empowered to manage their own care.

Ideally, the VW components combine to ensure the VW can deliver timely interventions to people with frailty who are acutely unwell. However, VWs do not usually provide 24-hour cover. For some people with frailty, crises have an impact on the caregivers who must take on extra responsibility, particularly outside of VW operating hours. Caregivers may feel unable to cope at home with frailty crises (especially incidents of delirium), leading to stress and risk of burnout or patient hospitalisation. VWs may not necessarily be the best arrangement for every situation—acute care in hospital may be required.

Whole system context

Delivery of VWs for people with frailty should be considered in a whole system context, including transfers of care into and out of the VW.

In longer-term VWs, patients are mainly referred from primary care, following set criteria. In short-term VWs, referrals are likely urgent, and may be from primary care, emergency services or early discharge from hospital. Before reaching a crisis, patients with frailty may have been treated in the community to prevent deterioration, possibly under GP-managed schemes.

Timings and arrangements for discharge to GP care differ: in longer-term VWs, discharge is when the MDT determines patients are stable following proactive care; the co-ordinator arranges good continuity of care. In short-term VWs, discharge may occur when acute events have been resolved; CGA may have been initiated in the VW, but there is insufficient time to establish proactive care. Effective continuity of care on discharge to primary care therefore becomes essential.

Increasing prevalence of frailty would confer greater demand for VW admissions and, potentially, re-admissions if people with frailty are not stabilised. This means that short-term VWs alone may not be sustainable, but they can form part of frailty management in the whole system. There is urgent need for evaluations of short-term VWs. If proactive VWs can prevent crises in people with frailty, they could improve patient outcomes [ 4 ], but their cost-effectiveness has not yet been demonstrated.

Rather than an either-or approach to the VW models, it may be that a combination is optimal, particularly in view of the closeness of the two states—high risk of crisis and in-crisis. One study reported such a combined model, comprising a longer-term VW, urgent care, and a care home [ 6 ]. Future work could explore a combined approach to acute reactive care and proactive care; for example, with red/amber/green wards within one VW, sharing the same staff and MDT.

Applicability to non-frailty multidisciplinary VWs

About 60% of the documents describe VWs for people with frailty, but many of the findings (CMOCs) can be applied directly to multidisciplinary VWs for other complex conditions. The specific disciplines and interventions involved would vary, but the underlying mechanisms may be transferable.

Cost implications

All VWs require investment of resources, which could be offset if VWs are effective in improving decision-making, reducing unplanned or prolonged hospital admission, and minimising duplication of effort between care providers. Cost implications of different VWs models would vary, particularly for staffing and length of stay in the VW, possibly balanced by treatment continuity after discharge. The effectiveness of VWs to mitigate hospitalisation may be highly contingent on resources being available elsewhere (e.g. domiciliary care workers). Cost-effectiveness research should take a broad perspective, including quality of life and costs for caregivers.

Improved use of limited capacity in both hospital and community care is a driver for VW implementation in the NHS [ 42 ], recognising that current reactive and hospital-centric care pathways are unsustainable. A more proactive system of care is required [ 46 ]. Improvements in frailty management could be cost saving at the system level if people can be reached before a crisis and are better supported to manage at home.

Comparison with other work

Existing systematic reviews of effectiveness of VWs are limited [ 24 ] are restricted to RCTs (of which there are few for frailty VWs), and do not answer questions about how and why VWs are effective. In contrast, this review draws on a range of document types, including grey literature, to answer these questions. Our work may complement systematic reviews of RCTs of community-based complex interventions, which use techniques such as component network meta-analysis to determine components of importance [ 56 ].

In December 2021, NHSE produced guidance to introduce ‘virtual wards’ for patients with ‘acute exacerbations of conditions related to frailty’ [ 19 ]. Recent work has also focused on short-term VWs for acute care: a rapid evidence synthesis of systematic reviews of acute VWs, hospital-at-home and remote monitoring, across all countries [ 57 ], and the British Geriatrics Society’s position paper on VWs for older people with frailty [ 58 ]. Our review included a broader range of VW models and was not limited to the more topical short-term VWs, which allowed us to draw on evidence that transcends the type of model.

This rapid realist review is the first to explore how, why, and for whom VWs may deliver effective frailty interventions. The findings show similarities with that of a larger realist synthesis on inter-organisational healthcare, which reports that collaborative leadership ‘works’ when there is trust between the parties involved, faith in the proposed model of care, and confidence in its ways of working [ 59 ].

Strength and limitations of our work

The review explores underlying mechanisms for VWs. We followed RAMESES standards and involved clinicians and PPI stakeholders. However, we were unable to recruit patients with lived experience of a frailty VW, and perspectives on the caregiver experience were limited. Our original intention to hold a large stakeholder consultation exercise was impeded by the COVID-19 pandemic and scheduling limitations.

This rapid realist review focused primarily on the UK, so is directly relevant to current NHS practice, but does draw on evidence from Ireland that has a similar, but slightly different healthcare system to the UK, alongside a similar demographic. This Irish evidence usefully clarified some operational details of frailty VWs, but no aspects of the CMOCs were solely reliant on evidence from Ireland.

Most evidence came from before the COVID-19 pandemic and periods when the UK health system was different in structures and pressures. The role and expectation of technology has changed rapidly but this was not captured in most included documents.

We did not formally appraise the rigour of included documents. We consider its impact on our findings minimal as the synthesis generates hypotheses rather than evaluating effectiveness or testing theories.

This rapid realist review outlines different VW models for people with frailty. Some findings can be applied to multidisciplinary VWs for other complex conditions. Our work could inform future decisions regarding service planning, evaluation and implementation of multidisciplinary VWs. There is currently insufficient evidence on the sustainability of VW models, experiences of caregivers, or the impact of social inequalities, all of which should be examined further.

Establishing a VW should involve formal collaboration agreements and starting small when adopting new ways of working. Perceptions of patient safety and benefit are important to maintain professionals’ ‘buy-in’ to the VW model. Time and resource should be planned into professionals’ work schedules.

The risk of caregiver stress, anxiety or burnout in some situations should be considered, especially after hours when VWs may not provide support. For some patients, hospital with 24-hour care could remain the most appropriate setting.

Sustainable frailty management requires that people with frailty are identified before reaching a crisis, and receive proactive care, monitoring, and support to self-manage, thereby preventing crisis situations and associated negative outcomes for patient, caregiver and the healthcare system. Reactive short-term VWs may be useful as a safety net for people who do fall into crisis. A whole system approach to effective frailty management is necessary, with attention to continuity of care including VW referral and discharge experiences. Our findings indicate a possible role for a combination of VW models.

Optimal implementation and delivery of multidisciplinary VWs could potentially improve quality of life for patients and caregivers, whilst alleviating resource demands of frailty management for the healthcare system.

We wish to acknowledge the helpful contributions from their experience and perspective, of the eight public contributors and one additional clinician, alongside support from our PPI coordinator in finding suitable contributors.

TW is the Research and Medical Director of The Research Institute for the Care of Older People (RICE), which runs a mixture of commercial and non-commercial research activity.

This research was funded by the National Institute for Health and Care Research Applied Research Collaboration West (grant number: NIHR200181). The funders played no role in the design, execution, analysis, interpretation of data or writing of the study. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. One author is the Research and Medical Director of an Institute that runs some commercial research projects funded by Roche, Biogen, Janssen, AC Immune, Novo Nordisk and Julius Clinical.

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Broadcast Systems Engineer

  • Madison, Wisconsin
  • Information Technology
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  • Staff-Full Time
  • Opening at: Mar 13 2024 at 15:25 CDT
  • Closing at: Mar 29 2024 at 23:55 CDT

Job Summary:

Wisconsin Public Radio (WPR) (, within the division of Wisconsin Public Media ( ), is seeking full-time Broadcast Systems Engineers to join our talented and self-motivated team of radio engineering professionals that are responsible for all of the infrastructure and systems that enable WPR's content to reach listeners across Wisconsin and beyond. This position will be primarily focused on responding to and resolving staff issues with the systems, working with the team to maintain the systems, and performing proactive and reactive quality control, like reviewing automation logs for errors or writing documentation. This role will report to the Manager of Radio Engineering, and will work closely with a variety of groups throughout the organization. This position will require being on-site in Vilas Hall most of the time, however, there are opportunities for a hybrid remote work arrangement. The WPR Engineering team manages all of the infrastructure that supports the state-wide network, which includes physical servers, storage, networking, applications, and security. The infrastructure includes a combination of Windows and Linux platforms, Windows and Apple computers, audio software, custom applications, secure remote access services, virtual desktops, security systems, and endpoint management systems. The environment is fast paced and leverages multiple support delivery methods including face-to-face and remote troubleshooting. Our ideal candidates will have a technical background, enjoy problem solving, have a passion for music, and embrace working collaboratively with a variety of staff, from hosts to producers to engineers. We are committed to equitable, unbiased hiring processes, flexible work environments, and ongoing, open conversations and reviews of diversity, equity and inclusion in our content and workplace.


  • 40% Identifies, troubleshoots, and resolves routine issues on systems, platforms, networks, and applications to ensure system security, confidentiality, compatibility, and functionality according to policies, procedures, and regulations
  • 30% Assists with the design, installation, configuration, and maintenance of routine automation, hardware, and associated equipment according to customer specifications to meet unit objectives
  • 5% Assists with the planning and coordinating of system logistics, upgrades, and system security
  • 5% Installs firewalls, host and client access mechanisms, permissions, and user accounts
  • 10% Performs quality control procedures related to the radio automation system, including log creation and management, and audio file and asset management
  • 5% Develops and facilitates end user trainings, answers questions, and provides information specific to the systems used to deliver WPR's content
  • 5% Develops documentation to aid in knowledge-sharing and process improvement

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Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world. For more information on diversity and inclusion on campus, please visit: Diversity and Inclusion

Preferred Associate's Degree Emphasis or focus in IT, Engineering, or related field preferred.


REQUIRED QUALIFICATIONS: - Knowledge of and/or experience with server management, permissions, network infrastructure, information security, secure access / VPNs, and endpoint management - Strong technical and problem solving skills including the ability to perform with a high degree of accuracy on technical tasks - Excellent verbal and written communication skills - Ability to work with a wide variety of colleagues with collegiality, patience, and curiosity - Demonstrated ability to learn new systems PREFERRED QUALIFICATIONS: - Experience with broadcast radio systems (e.g., MusicMaster, WideOrbit, Livewire, Audition, etc.) - Knowledge of music and music recording - Experience with virtualization technologies (e.g., VMware, Horizon, etc.) - Experience with communicating technical information to non-technical users - Experience with physical IT infrastructure (e.g., racking servers, networking switching, etc.) We recognize that qualified applicants come from a variety of backgrounds, life experiences, and levels of educational access. We encourage you to apply even if you don't match all of the preferred qualifications listed above.

Full Time: 100% This position is primarily onsite with occasional flexibility for remote work.

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Minimum $50,000 ANNUAL (12 months) Depending on Qualifications We expect to pay between $50-$60,000. Actual pay will be determined based on experience and skills.

Additional Information:

WPR has bureaus all across the state and this position would be expected to occasionally travel to the bureaus for on-site work. Also, given the critical nature of the systems the WPR Engineering team is required to be on-call and this position would be a part of that on-call rotation. The person in this position will be required to comply with the Ethical Guidelines for All Staff of Wisconsin Public Radio and Television located here: , in addition to the UW-Madison code of ethics. Please note that successful applicants are responsible for ensuring their eligibility to work in the United States (i.e. a citizen or national of the United States, a lawful permanent resident, a foreign national authorized to work in the United States without need of employer sponsorship) on or before the effective date of appointment. #WPR

How to Apply:

The following must be received for your application to be complete: 1) Resume; 2) A cover letter describing how your experience relates to the listed required and preferred job qualifications. Finalists will be asked to provide a list of at least three professional references with titles, emails, and phone numbers (including at least one supervisory reference). Note that references will not be contacted without your prior knowledge.

Fred Schulze [email protected] 608-262-4722 Relay Access (WTRS): 7-1-1. See RELAY_SERVICE for further information.

Official Title:

System Engineer I(IT037)



Employment Class:

Academic Staff-Renewable

Job Number:

The university of wisconsin-madison is an equal opportunity and affirmative action employer..

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