syntactic literature review meaning in research

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Synthetic literature reviews: An introduction

By Steve Wallis and Bernadette Wright 26/05/2020

Whether you are writing a funding proposal or an academic paper, you will most likely be required to start with a literature review of some kind. Despite (or because of) the work involved, a literature review is a great opportunity to showcase your knowledge on a topic. In this post, we’re going to take it one step further. We’re going to tell you a very practical approach to conducting literature reviews that allows you to show that you are advancing scientific knowledge before your project even begins. Also – and this is no small bonus – this approach lets you show how your literature review will lead to a more successful project.

Literature review – start with the basics

A literature review helps you shape effective solutions to the problems you (and your organisation) are facing. A literature review also helps you demonstrate the value of your activities. You can show how much you add to the process before you spend any money collecting new data. Finally, your literature review helps you avoid reinventing the wheel by showing you what relevant research already exists, so that you can target your new research more efficiently and more effectively.

We all want to conduct good research and have a meaningful impact on people’s lives. To do this, a literature review is a critical step. For funders, a literature review is especially important because it shows how much useful knowledge the writer already has.

Past methods of literature reviews tend to be focused on ‘muscle power’, that is spending more time and more effort to review more papers and adhering more closely to accepted standards. Examples of standards for conducting literature reviews include the PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions and the guidelines for assessing the quality and applicability of systematic reviews developed by the Task Force on Systematic Review and Guidelines . Given the untold millions of papers in many disciplines, even a large literature review that adheres to the best guidelines does little to move us toward integrated knowledge in and across disciplines.

In short, we need we need to work smarter, not harder!

Synthetic literature reviews

One approach that can provide more benefit is the synthetic literature review. Synthetic meaning synthesised or integrated, not artificial. Rather than explaining and reflecting on the results of previous studies (as is typically done in literature reviews), a synthetic literature review strives to create a new and more useful theoretical perspective by rigorously integrating the results of previous studies.

Many people find the process of synthesis difficult, elusive, or mysterious. When presenting their views and making recommendations for research, they tend to fall back on intuition (which is neither harder nor smarter).

After defining your research topic (‘poverty’ for example), the next step is to search the literature for existing theories or models of poverty that have been developed from research. You can use Google Scholar or your institutional database, or the assistance of a research librarian. A broad topic such as ‘poverty’, however, will lead you to millions of articles. You’ll narrow that field by focusing more closely on your topic and adding search terms. For example, you might be more interested in poverty among Latino communities in central California. You might also focus your search according to the date of the study (often, but not always, more recent results are preferred), or by geographic location. Continue refining and focusing your search until you have a workable number of papers (depending on your available time and resources). You might also take this time to throw out the papers that seem to be less relevant.

Skim those papers to be sure that they are really relevant to your topic. Once you have chosen a workable number of relevant papers, it is time to start integrating them.

Next, sort them according to the quality of their data.

Next, read the theory presented in each paper and create a diagram of the theory. The theory may be found in a section called ‘theory’ or sometimes in the ‘introduction’. For research papers, that presented theory may have changed during the research process, so you should look for the theory in the ‘findings’, ‘results’, or ‘discussion’ sections.

That diagram should include all relevant concepts from the theory and show the causal connections between the concepts that have been supported by research (some papers will present two theories, one before and one after the research – use the second one – only the hypotheses that have been supported by the research).

For a couple of brief and partial example from a recent interdisciplinary research paper, one theory of poverty might say ‘Having more education will help people to stay out of poverty’, while another might say ‘The more that the economy develops, the less poverty there will be’.

We then use those statements to create a diagram as we have in Figure 1.

Figure 1. Two (simple, partial) theories of poverty. (We like to use dashed lines to indicate ’causes less’, and solid lines to indicate ’causes more’)

When you have completed a diagram for each theory, the next step is to synthesise (integrate) them where the concepts are the same (or substantively similar) between two or more theories. With causal diagrams such as these, the process of synthesis becomes pretty direct. We simply combine the two (or more) theories to create a synthesised theory, such as in Figure 2.

Figure 2. Two theories synthesised where they overlap (in this case theories of poverty)

Much like a road map, a causal diagram of a theory with more concepts and more connecting arrows is more useful for navigation. You can show that your literature review is better than previous reviews by showing that you have taken a number of fragmented theories (as in Figure 1) and synthesised them to create a more coherent theory (as in Figure 2).

To go a step further, you may use Integrative Propositional Analysis (IPA) to quantify the extent to which your research has improved the structure and potential usefulness of your knowledge through the synthesis. Another source is our new book from Practical Mapping for Applied Research and Program Evaluation (see especially Chapter 5). (For the basics, you can look at Chapter One for free on the publisher’s site by clicking on the ‘Preview’ tab here. )

Once you become comfortable with the process, you will certainly be working ‘smarter’ and showcasing your knowledge to funders!

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Literature Reviews

• What is a literature review?
• Steps in the Literature Review Process
• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
• Synthesize Results
• Analyze Results
• Librarian Support

What is a Literature Review?

A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important past and current research and practices. It provides background and context, and shows how your research will contribute to the field. 

A literature review should: 

• Provide a comprehensive and updated review of the literature;
• Explain why this review has taken place;
• Articulate a position or hypothesis;
• Acknowledge and account for conflicting and corroborating points of view

From  S age Research Methods

Purpose of a Literature Review

A literature review can be written as an introduction to a study to:

• Demonstrate how a study fills a gap in research
• Compare a study with other research that's been done

Or it can be a separate work (a research article on its own) which:

• Organizes or describes a topic
• Describes variables within a particular issue/problem

Limitations of a Literature Review

Some of the limitations of a literature review are:

• It's a snapshot in time. Unlike other reviews, this one has beginning, a middle and an end. There may be future developments that could make your work less relevant.
• It may be too focused. Some niche studies may miss the bigger picture.
• It can be difficult to be comprehensive. There is no way to make sure all the literature on a topic was considered.
• It is easy to be biased if you stick to top tier journals. There may be other places where people are publishing exemplary research. Look to open access publications and conferences to reflect a more inclusive collection. Also, make sure to include opposing views (and not just supporting evidence).

Source: Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal, vol. 26, no. 2, June 2009, pp. 91–108. Wiley Online Library, doi:10.1111/j.1471-1842.2009.00848.x.

Meryl Brodsky : Communication and Information Studies

Hannah Chapman Tripp : Biology, Neuroscience

Carolyn Cunningham : Human Development & Family Sciences, Psychology, Sociology

Larayne Dallas : Engineering

Janelle Hedstrom : Special Education, Curriculum & Instruction, Ed Leadership & Policy ​

Susan Macicak : Linguistics

Imelda Vetter : Dell Medical School

For help in other subject areas, please see the guide to library specialists by subject .

Periodically, UT Libraries runs a workshop covering the basics and library support for literature reviews. While we try to offer these once per academic year, we find providing the recording to be helpful to community members who have missed the session. Following is the most recent recording of the workshop, Conducting a Literature Review. To view the recording, a UT login is required.

• October 26, 2022 recording
• Last Updated: Oct 26, 2022 2:49 PM
• URL: https://guides.lib.utexas.edu/literaturereviews

Libraries | Research Guides

Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
• Choosing Where to Search
• Organizing the Review
• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

• Sage Research Methods Core Collection This link opens in a new window SAGE Research Methods supports research at all levels by providing material to guide users through every step of the research process. SAGE Research Methods is the ultimate methods library with more than 1000 books, reference works, journal articles, and instructional videos by world-leading academics from across the social sciences, including the largest collection of qualitative methods books available online from any scholarly publisher. – Publisher

• Next: Planning the Review >>
• Last Updated: Jan 17, 2024 10:05 AM
• URL: https://libguides.northwestern.edu/literaturereviews

Literature Reviews: Systematic, Scoping, Integrative

Characteristics of review types, choosing a review type.

Steps in a Systematic/Scoping/Integrative Review

Confirming the Knowledge Gap

Standards and reporting guidelines.

• Creating a Search Strategy
• Limits and Inclusion Criteria
• Review Protocols
• Elements of a Systematic Review
• Review Tools and Applications

Additional Resources

• JBI Manual for Evidence Synthesis Process outlines for multiple types of evidence reviews. A great source to cite in your methods section.
• PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Guidance for authors and peer reviewers on best practices in reporting for evidence reviews. Includes extensions for different types of reviews, including scoping reviews

Not sure which review type is right for your research question? Check out the links below for help choosing.

• What Review is Right for You? v2 14 page PDF survey to help you determine which review type might work best for you. Very thorough!
• Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x

Creating an effective search for a systematic review means walking a tightrope between comprehensiveness and managability. You want to try to include all of the studies that could possibly be relevant while simultaneously getting your search results down to a number of articles that you can realistically review. 

The Basic Process:

• Develop a research question.
• Search databases to see if a review has already been published on your topic. 
• Select the type of review (systematic, scoping, integrative)
• Select databases.
• Select grey literature sources (if applicable). Read this article for helpful suggestions on systematically searching for grey literature.
• Formulate an initial search for one of your selected databases. For tips on searching, consult our Mastering Keyword Searching guide.
• Review results from initial search, scanning titles, abstracts, and subject headings to identify additional terms. You may also want to use the subject heading database you can find within each database.
• Run the search again. Continue to add relevant terms and adjust the scope of your question (which may require eliminating terms) until results are a reasonable size and predominantly relevant to your question.
• When you think your search is nearly final, gather 2-3 of your most relevant articles and test their reference lists against your search results. If your search contains a large majority of the relevant articles from those reference lists, your have your final search (remember no search is ever perfect, and you will nearly always add articles you find via reference lists, recommendations, etc. that did not appear in your search results). 
• Translate your search to your other databases. Generally your keywords will stay the same across databases, but you will most likely need to adjust your subject headings, because those can vary from database to database.
• Ask a librarian to peer review your search. Try the PRESS checklist . 
• Develop inclusion and exclusion criteria in preparation for reviewing articles (this step may come later for a scoping review)
• Write a protocol .
• Database name (be as specific as possible, including the full title, especially for databases that are offered in multiple formats, e.g. Ovid Medline) and dates of coverage.
• Search terms, including indicating which are subject headings and which are keywords plus any limitations to where the keywords were search if relevant.
• Database limits/filters applied to the results (e.g. publication year, language, etc.).
• Date of your search.
• Number of results.
• Begin title/abstract screening. Two reviewers for each item is best practice.
• Begin full-text review of the articles still remaining. Again, two reviewers for each item is best practice. 
• Conduct citation mining for the articles that make it through full-text review. That means looking at reference lists (backwards searching) and searching for articles that cite back to the article you have (forward searching). You might also consider setting aside all of the systematic and scoping reviews that came up with your search (generally those are excluded from your review) and mining their reference lists as well. Repeat the title/abstract screening and full-text reviews for the articles identified through citation mining.
• Check all articles that made it through the full-text review for retractions, and remove any articles that have been retracted. 
• If doing a systematic review, conduct a critical appraisal of included articles (aka Risk of Bias Assessment).
• Covidence. (2024). A practical guide to data extraction for intervention systematic reviews .
• Pollock et al. (2023). Recommendations for the extraction, analysis, and presentation of results in scoping reviews . JBI Evidence Synthesis, 21 (3), 520-532. 
• Prepare your manuscript (for information on writing each section of your manuscript, see our guide to Writing up Your Own Research ). 

Before beginning your review, you need to be sure that no other reviews with the same research question as yours already exist or are in progress. This is easily done by searching research databases and protocol registries.

Databases to Check

Protocol Registries

• PROSPERO PROSPERO accepts registrations for systematic reviews, rapid reviews and umbrella reviews. PROSPERO does not accept scoping reviews or literature scans. Sibling PROSPERO sites register systematic reviews of human studies and systematic reviews of animal studies.

It is a good idea to familiarize yourself with the standards and reporting guidelines for the type of review you are planning to do. Following the standards/guidelines as you plan and execute your review will help ensure that you minimize bias and maximize your chances of getting published.

Systematic Reviews

• PRISMA Statement The PRISMA statement is currently the standards and guidelines of choice for systematic reviews. At the link you will find the statement as well as explanations of each element, a checklist of elements, a PRISMA flow diagram template, and more.

• IOM Finding What Works in Healthcare: Standards for Systematic Reviews Standards from the National Academy of Medicine and National Academies Press. The free download link is all the way over on the right.

Scoping Reviews

• PRISMA-SCR Extension for Scoping Reviews A PRISMA statement, explanation and checklist specifically for scoping reviews.
• Updated methodological guidance for the conduct of scoping reviews While the PRISMA-SCR provides reporting guidelines, these guidelines from JBI are for how to actually plan and do your review. This is the explanation for updates made to the manual linked below. You can skip this article and go directly to the JBI manual if you prefer.

Integrative Reviews

• Whittemore, R., & Knafl, K. (2005). The integrative review: updated methodology. Journal of Advanced Nursing, 52 (5), 546–553. This article is the current standard for designing an integrative review. more... less... https://doi.org/10.1111/j.1365-2648.2005.03621.x
• Tavares de Souza, M., Dias da Silva, M., & de Carvalho, R. (2010). Integrative review: What is it? How to do it? Einstein, 8 (1). https://doi.org/10.1590/s1679-45082010rw1134
• Next: Creating a Search Strategy >>
• Last Updated: Mar 26, 2024 6:07 PM
• URL: https://libguides.massgeneral.org/reviews

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• Knowledge Base

Methodology

• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
• << Previous: Getting Started
• Next: How to Pick a Topic >>
• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

A lexical and syntactic study of research article titles in Library Science and Scientometrics

• Published: 16 May 2021
• Volume 126 , pages 6041–6058, ( 2021 )

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• Junli Diao   ORCID: orcid.org/0000-0002-9603-8278 1  

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Title of a research article is an abstract of the abstract. Titles play a decisive role in convincing readers at first sight whether articles are worth reading or not. Not only do research article titles show how carefully words are chosen by authors, but also reflect disciplinary differences in terms of title words and structure between hard sciences and soft sciences. This study examined the lexical density and syntactic structure of 690 research article titles chosen from five Library Science and Scientometrics journals, aiming to reveal disciplinary differences. The result suggested both Library Science and Scientometrics have almost the same title length and the prevalent usage of Nominal Phrase (NP) to govern the title structure. The result also stated some disciplinary differences: Library Science demonstrates more punctuation complexity, particularly a greater frequency in using colons; but Scientometrics shows more involvement of words related to research methods, which is an indicator to papers’ scientific value, and more usage of declarative Full Sentence (FS) structure, which were mostly discovered in the research articles in hard sciences.

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Diao, J. A lexical and syntactic study of research article titles in Library Science and Scientometrics. Scientometrics 126 , 6041–6058 (2021). https://doi.org/10.1007/s11192-021-04018-6

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Semantic interoperability in health records standards: a systematic literature review

Blanda helena de mello.

Software Innovation Laboratory, SOFTWARELAB Universidade Do Vale Do Rio Dos Sinos - Unisinos, São Leopoldo, Brazil

Sandro José Rigo

Cristiano andré da costa, rodrigo da rosa righi, bruna donida, marta rosecler bez, luana carina schunke, associated data.

Not applicable.

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Introduction

A semantically integrated health system allows sharing data among organizations and their internal ecosystem without missing the meaning. The search for semantic interoperability in health records and different clinical annotations is one of the main challenges for systems, being a constant objective of studies in the last years, such as [ 1 – 3 ]. Implementing interoperability can allow healthcare professionals to manage the complete electronic patient record, regardless of the organization that generated the clinical session entries [ 4 ]. As reinforced by Kim and Joshi [ 5 ], health record interoperability became a crucial issue in the healthcare scenario, especially in the COVID-19 pandemic, to further disease control.

Semantic interoperability (SI) aims to share data among organizations or systems and ensure they understand and interpret data regardless of who is involved, using domain concepts, context knowledge, and formal data representation [ 6 ]. On the other hand, semantic interoperability also can be understood by taking a step back: semantics is the study of meaning, focused on the relationship between people and their words, which is essential to help people understand each other despite different experiences or viewpoints [ 7 ]. Interoperability is the ability of two or more systems to work together, regardless of its different interfaces, platforms, and technologies adopted [ 8 ].

The HIMSS [ 9 ] 1 – a global advisor supporting the transformation of the health ecosystem, with 480 provider organizations and more than 450 non-profit partners – had designed defined in three levels the interoperability technology: 1) Foundation; 2) Structural and 3) Semantic. The Foundational level defined requirements to connect different systems and securely exchange data. The Structural level defines the format, syntax, and data to interpret at the field level; the Semantic level allows one to work with terminologies, vocabularies, standardized values publicly defined and makes a complete understanding meaning.

According to Gancel et al.. [ 10 ], there are many challenges to overcome to achieve an electronic health record, such as understanding the wide variety of terms, addressing disambiguation, identifying and updating the concepts. That scenario is reinforced by [ 11 , 12 ], where the authors had discussed the three fundamental levels of interoperability to improve the workflows across health information systems to allow true interoperability. To the healthcare area, interoperability means different systems, applications, and devices share, use, and process data from any place and keep the real meaning.

Using standards allows sharing data between clinicians, lab, hospital and, pharmacies regardless of vendor, achieving semantic interoperability. In other words, interoperability allows health information systems to work across organizational boundaries.

From the above, the main contributions of this paper are as follows:

• discussion about the state of the art of semantic interoperability in health records.
• introduce a taxonomy in semantic interoperability in electronic health records.
• recognize the main approaches commonly used to achieve semantic interoperability in EHR (Electronic Health Record) systems.
• expose the grown adoption using semantic web technologies combining international standards to solve semantic interoperability problems.

Thus, this systematic review aims to answer the following central question: what is the state of the art of semantic interoperability in health records in the sense of approaches and international health standards?

We organized the systematic review as follows. Section 0 presents a background of semantic interoperability and electronic health records. Follow, Sect. 0 shows the protocol defined, describing the inclusion/exclusion criteria and the quality assessment. Section 0 presents the results of the conducted review and the research questions answered, and Sect. 0 discusses some open questions and the main approaches used by the selected studies. Lastly, we discuss future directions to work in Sect. 0.

According to ISO/TS18308 [ 13 ], an EHR aims to integrate health records in a processable format, securely storing and communicating. Using an information model commonly accepted to exchange data, making it accessible to authorized users. That aims to ensure the patient’s life-long integrated healthcare, efficiently with high-quality and security. The EHR structure holds the patient’s health status, and that format must be processed digitally [ 14 ], maintaining patients’ data throughout their life and stored accurately in a repository.

There are different data formats in EHR systems, such as structured data and non-structured textual data. EHR covers an extensive part of the medical histories, includes more patients’ complete information and potential risk factors. In addition, it maintains patient health records and supports the provision of daily care in hospitals and primary care clinics [ 15 ]. Furthermore, it allows the reuse of patient data for many purposes, including managing individual patients, medical and health services research, and management of health care facilities [ 16 ].

Adopting an EHR is essential to manage healthcare and exchange data between healthcare organizations. An EHR allows communication among clinicians, nurses, laboratories, and hospitals despite different systems. Sharing data between health organizations and health agents must foster the correct interpretation, with the same precision and meaning adopted from the sender [ 17 ], achieving semantic interoperability. Therefore, semantic interoperability is the ability to share data between systems and ensure understanding at the concept’s level of the domain [ 13 ].

Different health standards aim to enable data sharing among healthcare organizations. However, the adoption of standards still presents several challenges to achieving interoperability at the semantic level. The semantic web focuses on sharing data, integration, and reuse through ontologies, linked open data, and knowledge graphs to ensure the correct meaning of shared data. The semantic web is also known as one of the fundamental technologies to achieve semantic interoperability in health information systems [ 4 ], often using ontologies, a well-established technology to support knowledge-intensive tasks related to EHR systems [ 3 ].

Related work

Recently, the demand for systems that allow data interoperability at a semantic level has been an object of interest, mainly in health system providers. Different studies explore approaches to solving interoperability problems. However, there are difficulties in adopting health standards and tools to adequate data representation (ontologies, databases, clinical models) that ensure healthcare professionals efficiently manage the data.

The authors in [ 18 ] discuss interoperability in electronic health records from the management and business perspective. In this way, they highlight how data integration and exchange across organizational boundaries can improve quality of care, work processes, and effectiveness to reduce costs and improve efficiency. Also, the authors had shown how related healthcare with other areas such as Telemedicine, Big Data, and Business Intelligence. On the technology side, it helps eliminate rework, reduce errors, and promote individualized patient care. On the citizens’ side, it highlighted the support to create the primary public health initiatives, control, monitor diseases, reduce costs, and increase the effectiveness.

On the other hand, [ 19 , 20 ] analyzed technological aspects regarding health integration, interoperability, and data exchange. The first work describes the advantages of implementing integrated data repositories, a clinical data warehouse that allows clinical research, specialized analysis, and advanced data processing. The second paper proposes a transnational model integrating health records. The study suggests adopting a widely used health standard called HL7 FHIR, 2 vocabularies, and terminologies.

The systematic review [ 21 ] discusses the strong trend in adopting standards. The authors showed interest in analyzing the literature according to ontologies, specifically fuzzy ontology (Fuzzy ontology). The study presents a comprehensive background to context for the leading health standards and their different structures, highlighting characteristics they may have in common. The article also named the adopted standards using the “e-Health Standard” term, whereas we keep the broad term as a health standard. Also, the authors had highlighted trends of semantic interoperability in four categories that contribute to identifying challenges and research opportunities: a) frameworks to solve SI problems, b) using ontologies to achieve interoperability issues, c) standards in an Interoperable EHR, d) barriers and the heterogeneous problem EHR semantic interoperability.

Furthermore, the studies developed by [ 20 , 21 ] richly exposed the growth and interest of the healthcare sector in using standards for electronic medical records, breaking organizational barriers, and achieving interoperability among healthcare providers. Given this scenario, we conduct this review by scrutinizing the evolutions in adopting standards in recent years and the tools that eventually make up the environment for a semantically interoperable EHR.

Therefore, we consider it more coherent with the selected studies to observe the ecosystem involved in the construction and development of an EHR. Furthermore, understanding how secondary artifacts – semantic web tools, databases, terminologies – interact and impact project definition. Besides the standards and semantic web tools, we highlight ontologies not exclusively used to represent raw data but to represent clinical model structures.

Material and methods

This section describes the protocol used for the systematic literature review and shows the research questions designed to extract the information of interest from the selected studies. The research strategy for filtering and selecting the studies involved the adoption of inclusion and exclusion criteria. Finally, we show aspects of the quality assessment for the selected articles, later applied to answer the research questions.

Study design

The protocol followed the steps from previous works of [ 22 , 23 ] and aimed to map relevant and recent research in semantic interoperability. A systematic literature review is a research method that allows the identification, evaluation, and interpretation of the studies without bias in the process.

• Research questions: introduce the research questions investigated.
• Search strategy: outline the strategy and libraries explored to collect data.
• Article selection: explain the criteria for selecting the studies.
• Quality assessment: describe the quality assessment applied to the selected studies.
• Data extraction: compare the selected studies and research questions.

Research questions

The research questions represent a fundamental part of the systematic review [ 22 ], as they allow directing the research and extracting information as needed, as they follow the topic of interest. Table ​ Table1 1 presents the research questions of interest in the systematic review. There are two sets of questions, the first global context questions, including an overview of all studies. The latter are specific questions to answer about each article, as shown below.

Research questions to extract information of interest from the articles selected in this review

Search strategy

The search strategy aims to find studies to answer the research questions through the definition of keywords and the scope of the research. The correct selection of keywords ensures adequate research results on the databases. As guided by [ 22 ], we used the five PICOC questions (Population, Intervention, Comparison, Outcome, and Context) to define the scope of the research, as explained below.

Population involves keywords, related terms, and variants around the interest area. We used “semantic interoperability” as the central term in the search string shown in Frame 1. In the Intervention, being “semantic interoperability” a general term, we used some other keywords to filter: health record, medical record, patient record, and hospital record, aiming to allow filtering semantic interoperability standards applied in health records. Comparison, on this research, aims to find different health standards allowing semantic interoperability in health records. The Outcomes determine relevant studies that can answer the research questions.

Also, we evaluated studies that explore the limitations of applying health standards. We define Context as the concern to identify the different application scenarios and data used on the proposal. This research focused on selecting studies concerned with proposing solutions to semantic interoperability problems in health records. In addition, we notice that different kinds of data come from the real world, and most come from a controlled environment (case study) or simulated data.

The research string formatted as shown in Frame 1 demonstrates the mandatory terms, such as semantic interoperability and his acronyms; health record and his acronyms; the term standard. At the selection step, the terms applied aim to filter the articles by title, abstract, and keywords.

Frame 1  This frame shows the search string defined to research. 

Article selection

Next, the studies with neither a key term addressed nor a PICOC definition aligned, called impurities, are removed. Finally, qualify our results, and for that, we defined the exclusion criteria based on the research question:

• Exclusion criteria 1: the article is not in the range (search date 2010 to September 24, 2020).
• Exclusion criteria 2: the article does not address semantic interoperability or related acronyms (population criteria I).
• Exclusion criteria 3: the article does not address the health record, medical record, patient record, hospital record, and related acronyms (intervention criteria II).
• Exclusion criteria 4: the article does not address standard or related acronyms (comparison criteria III).
• Exclusion criteria 5: the article has less than six pages.
• Exclusion criteria 6: impurities articles (e.g., duplicate papers and non-English studies).

Quality assessment

The quality assessment consists of a filter focused on the quality and relevance of studies related to the interest area. The quality assessment ensures that selected articles have a relevant impact in the research area. As a criterion to filter the rest studies, we defined the h5-index score, equal to or higher than 28. If the article passes the cutoff, it can be accepted in this review..

Data extraction

After selecting articles to satisfy the inclusion and exclusion criteria and the quality assessment, the data extraction step occurs. The other articles represent the corpus qualified for full reading and analysis. The research questions target to extract information from selected studies according to the topic of interest, and data extraction aims to correlate the area of interest with the defined research questions. The studies propose different solutions to the research problem presented: what health standards approaches can solve semantic interoperability problems in health records? The questions have a limited response context to avoid bias, to respond objectively.

The methodology to answer the research questions proposes to extract the results as presented in Table ​ Table2, 2 , where the General Question (GQ) and Specific Question (SQ) have an expected location to search for data.

Follows we shows the quality assessment of article and related questions

Results and discussion

The discussion of these different results extracted through the research questions defined, such as shown in Table ​ Table1, 1 , allows understanding the semantic interoperability scenario and the solutions usually applied in this scenario.

Conducting the search strategy

We chose seven different research bases to cover studies in the health and technology field, such as ACM Digital Library, IEEE Xplore Library, Science Direct, Springer Link. Moreover, we added Google Scholar to cover studies outside those bases. Our criterion was the relevance of these databases concerning the health and information technology literature. The search step in the databases, as mentioned above, aimed to index the search for studies published in the last ten years. Each database presents a way of formatting the survey, which we respect and modify to suit, but we kept all the mandatory terms defined in the PICOC strategy.

Finally, after applying the queries to the search bases, we had 6,032 articles. The initial filter aims to remove patents and citations, non-English studies, which resulted in around 783, roughly because some patents also appeared as citations.

As shown in Fig.  1 , by year, the published articles in this area have been an interest constant in recent years. For this systematic review, the cutoff was September 22nd, 2020.

This graph presents, distributed by year of publication, the corpus of articles published during the range 2010 to September 2020

Figure  2 shows the selection steps, removing impurity studies unrelated to the area of interest. Usually, these impurities studies had references related to the research area or citations of related works, however, without directly informing the area of interest. In addition, articles with less than six pages and no abstract, about 735 impurities, were excluded. We removed non-primary studies, such as editorials, chapters, thesis, reviews, and reports, approximately 1195 studies.

The figure presents the entire selection process of the studies across the inclusion/exclusion criteria and quality assessment to conduct this systematic review

Then, we applied the inclusion and exclusion criteria based on the PICOC strategy: semantic interoperability, medical record (variants such as health record, medical record, patient record, hospital record), and standard. Finally, the inclusion and exclusion criteria are applied to the remaining studies to filter articles directly related to the research topic, totaling 2,288 excluded studies. The filters allow selecting studies according to defined terms.

The last step had two parts; first, we filtered the studies by main interest area and evaluated the remaining corpus about its objectives. Many studies satisfied the inclusion and exclusion criteria but differed from the review’s interest topics. Thus, the quality assessment, the last step of the protocol, provides a cutoff parameter, where we look for studies published in relevant journals for the area of interest. Among these, we selected the highlighted studies. For this, we use the h5-index metric, which quantifies the relevance of newspapers and conferences in the last five years, a Google metric and works with the highest number H [ 24 ]. Some studies that presented relevant discussions but did not appear in this index were separated to contribute to our discussion section.

In the final selection, some bases showed a predominance of accepted articles. For example, ScienceDirect with eight studies followed for PubMed Medline with seven and Google Scholar with six studies. Next was SpringerLink with four and IEEE Explore with two, while ACM Digital Library and Web of Science had one from each research base. Figure  3 present the final corpus articles accepted in this systematic review.

This graph presents the articles accepted after the selection process, showing the number of articles by published year

Figure  3 shows the accepted articles distributed by year. Table ​ Table3 3 shows where each article was published (journal or congress), the number of articles per location, and the H5 index used in the quality assessment stage. Three times the journals had an above-average acceptance, a scenario explained by the applied area, which is related to the research question of this review. We highlight the International Journal of Medical Informatics, with five accepted studies, BMC Medical Informatics and Decision Making, and the Journal of the American Medical Informatics Association, with four accepted studies for each, a relevant number compared to the rest of the studies shown in Table ​ Table3 3 below.

This graph presents the number of papers by year of publication after finishing the selection and filtering steps

The information extracted from the articles aims to answer the question of interest in the review and identify approaches and solutions developed that allow achieving semantic interoperability in health records. Table ​ Table4 4 presents the final list of articles selected for this systematic review.

This table presents the first author and respective studies by year, relating the publisher and the kind of place it was published

Follows the analysis of the selected studies against the questions of interest, and each other answered individually.

SQ1 – What are the health standards adopted in the studies?

There is no consensus on a global standard for electronic health records, and the studies selected for this review reinforced this scenario. However, the extracted data shows a trend in the standard choice with a multilevel approach, as such openEHR, ISO/CEN 13,606, and HL7 formats. That dual model approach allows specialists in health and technology to perform in a joint work. Most of the studies had related advances toward a semantic dataset choosing standards to achieve semantic interoperability, as shown in Table ​ Table5 5 .

Health standards used in the selected studies

http://www.clinicalelement.com/

https://www.dicomstandard.org/current

http://www.hl7.org/implement/standards/

https://www.ihe.net/

http://www.en13606.org/information.html

https://www.openehr.org/

https://ckm.openehr.org/ckm/

The information extracted from studies demonstrates the open health standards as a trend, especially towards the two-level—openEHR and ISO 13606. Besides exchanging data and ensuring semantic interoperability concerns, as in [ 46 ], the authors had developed a framework combining ontology resources to predict high-risk situations in pregnancy. Additionally, the article contributed a summary analysis of 3 open standards, openEHR, ISO 13606, and HL7 CDA, showing their advantages and disadvantages. On the other hand, the studies [ 3 , 4 , 42 ] seek solutions for using openEHR and ISO13606 jointly, two open standards and similar definitions. This approach to approximation of the standards would allow the normalization of data. Although both standards use the ADL (Architecture Description Language), several differences in their types and definitions still need harmonizing.

In [ 16 , 31 , 43 ], the authors had explored slightly different opportunities from the known target of standards. For instance, [ 16 ] presented a methodology to represent the dependencies among data elements, concepts, and archetypes on a three-level Bayesian network and used the inference process to discover relevant archetypes—with promising results against the traditional search platform CKM. On the other hand, the authors in [ 43 ] improved patients’ current post-sale drug surveillance process. The data come from voluntary reports (spontaneous reports, yet just about adverse incidents). The EHR adoption would allow tracing a complete patient medical history and to predict potential risk factors.

The authors in [ 31 ] also explored a different opportunity. They developed a federated Metadata Registry/Repository (MDR) – a metadata database of data combining Common Data Elements (CDE) and HL7 CCD (Continuity of Care Document) models, proposing extensions to ISO 11179. Moreover, it was implemented in [ 36 ] the Health Level 7 (HL7) Virtual Medical Record (vMR) as a component service-based, that aims to collect patient data from different databases to allow the use in EHR data clinical decision support as a gateway between data sources and components.

SQ2 – What are the terminologies or health repositories used?

Terminologies and vocabularies can be understood as extensive collections of terms for a knowledge domain, making the language common. Terminologies aim to prevent local expressions, neologisms, and human typing from entering the EHR, thereby adopting formal classifications, e.g., diseases, events, procedures, specimens. Healthcare institutions share sensitive information, and systems must convey and not miss the meaning. One way possible is to build a local repository and manage an environment that applies proprietary concepts. However, the adoption of international terminologies ensures that using global terms and other classifications will have the same meaning on the other side – to any receiver. Table ​ Table6 6 present the terminologies usually adopted by the studies.

The following are the international terminologies and classifications applied by the studies aiming at a shared vocabulary focusing on keeping the real meaning

Health standards generally easily adhere to different terminology as they are sets of data that can be entered into the repository and accessed and updated. Table ​ Table6 6 shows some terminologies most used by the selected studies. We highlight SNOMED-CT, the most cited terminology, bringing a clinically validated, semantically rich, controlled vocabulary. That facilitates evolutionary growth in expressivity to meet emerging requirements [ 50 ]. Also was often quoted and discussed, ICD is a diagnostic classification standard for clinical and research purposes. It defines a universe of diseases, disorders, injuries, and related health conditions [ 51 ], and in 2022 will publish the new version, the ICD-11.

While the adoption of standards is essential to achieve interoperability in EHR and effectively exchange information between different providers, the next level requires sharing knowledge and adding semantic value. A common language makes information comprehensible among who sent and received, allowing inferences in data and creating new connections from existing data. According to [ 49 ], semantic modeling essentially means linking words and terms to their senses, which is your main challenge.

Adopting a terminology engages more than one HIMSS level, as it condenses structural decisions regarding the system design, team, and organizational choices. The institution’s medical staff must accept adherence, use the terminologies, and publicly reflect that choice. After compliance around the organization and team, the information conforms to a standard and shares it with other providers.

SQ3 – What are the approaches used?

The selected studies reinforce the advantages of adherence to standards for achieving semantic interoperability. Exploring new approaches involving technologies and health standards to extract better results from consolidated standards has shown growth. Likewise, some studies using the use of semantic web technologies to meet information extraction and harmonization demands. Table ​ Table7 7 present the principal purpose of the selected studies.

The studies had more than one objective, so this table separates them into applications and proposed approaches to meet interoperability demands in health records, categorizing them according to the principal approach to developing the contribution

Standards to allow semantic interoperability have been a common choice in electronic health records systems. The papers selected for this review strengthen that scenario and justify solving problems by developing solutions for this purpose. An initiative regarding making health records available for secondary use in [ 42 , 43 ], also reinforces a consequent advantage of implementing semantic interoperability since the improved data quality is part of the process. These works are also strongly related to the application of representations of clinical models [ 15 , 27 , 42 ], OWL semantic structures, where they also serve as semantic mediators [ 15 , 26 , 31 ], whereas [ 30 ] also added an agent-based system to coordinate the community IHE.

Hundreds of biomedical ontologies are available in OWL format in the BioPortal repository, including many medical terminologies, which justified using ontologies in some studies to convert clinical models, data, and other terminologies to this representation format natural. According to the authors in [ 42 ], exploring data semantic representation in ontologies is justified because other structures usually have explicit connections between data, and ontological systems allow reasoning to build different meanings. For this reason, to represent EHR metadata into ontologies typically appear like a good choice for semantic goals.

Moreover, the use of ontologies has also proved promising for mapping scenarios of rules data access. As demonstrated in [ 30 ], an agent coordination infrastructure uses an OWL (Web Ontology Language) ontology to map the access rules of organizations from the community to the data. On the other hand, the authors in [ 28 ] developed an automatic extraction from semi-structured data using ontologies. They represented those concepts into ontologies of clinical vocabularies—another successful use of ontologies.

In line with the sharing and reuse of existing clinical models, some studies propose creating automated interfaces based on archetypes from the openEHR and ISO13606 standards, such as [ 29 , 34 ]. On the other hand, some studies have identified novel architectures of service involving workflows in cloud environments, aiming to enable a tool to set resource pipelines, as presented in the works [ 3 , 32 , 36 , 41 ], to access health data. Table ​ Table8 8 introduces the semantic web technologies used by the studies.

The different semantic web technologies used in studies to solve semantic problems

Despite being commonly used for semantic representation, semantic web technologies have broad applicability. Some studies have explored the representation of archetypes, rules, and relationships between different reference models (RM) of the openEHR and ISO136060 standards. The authors in [ 40 , 44 ] have represented the reference model and archetype constraints into OWL ontology, which describes the instances and allows maintaining only one information (removing duplicated cases) to keep a relationship between RM and archetype.

Exploring other advantages, [ 3 , 42 ] had presented EHR data into RDF and OWL structures – these transformations through the use of the Semantic Web Integration Tool (SWIT), as shown previously in Table ​ Table8. 8 . Additionally, they chose a graph database; respectively, the first used Neo4J Graph Database, and the last chose Virtuoso. However, both studies allow using Linked Open Data (LOD).

The studies also showed a trend by combining health standards and semantic web technologies to achieve semantic interoperability. There was no consensus about ontologies type adoption—OWL and RDF ontology. Both are used to represent the reference model and archetypes into ontologies. As reinforced by [ 4 ], the Archetype Definition Language (ADL) usually represents archetypes and has more of a syntactic orientation. Thus, it has disadvantages for achieving semantic interoperability, justifying the combining ontologies and clinical models. Also, the work presented in [ 27 ] used ontologies combining Semantic Web Rule Language (SWRL), and these rules aimed to allow applying logical deductive reasoning between archetypes and terminologies used.

This systematic review focuses on understanding the different approaches proposed to achieve semantic interoperability through health standards, and some studies have indicated promising results when selecting a specific database. Although there are unusual and justified databases in the studies as to the reason for selection, we also kept the most typical databases. Then, Table ​ Table9 9 present the database used by the studies.

Databases used on studies

https://virtuoso.openlinksw.com/

https://neo4j.com/

https://hbase.apache.org/

https://jena.apache.org/documentation/tdb/

https://basex.org/

https://www.oracle.com/index.html

http://www.marand.com/thinkehr

https://www.postgresql.org/

The selected studies the not show a clear trend as to whether the health standard influences the choice of database, although it is possible to assess some characteristics. For example, the Virtuoso multi-model bank appeared in solutions using openEHR and HL7, not showing a dependency relationship with the standard type. However, a graph database allows ontologies querying if existent, reinforcing a possible hybrid solution with semantic web tools.

On the other hand, ARM and Think!EHR presented a framework designed to support archetype storage and allow querying in ADL. Therefore, there is a dependency of choice when using ISO 13606 and openEHR standards. The other storage structures did not show a strong relationship or dependence on health standards, and few studies discussed the type of database adopted by their solutions.

SQ4 – What are the main security concerns used?

One of the concerns sharing data is security, such as using anonymized data and methods for de-identification. Furthermore, the exchange between different organizations must encompass safe policies, even to exchange among proprietary systems — safety issues such as maintaining security and integrity without breaking the quality of patient data.

The studies used several kinds of data, with different sources, such as from patients coming from the real world, such as [ 46 ] pregnancy data, [ 3 , 42 ] and [ 3 ] colorectal cancer screening protocols and lab tests, [ 43 ] pharmacoepidemiology data, [ 40 ]infants affected by Cerebral Palsy[ 45 ], coronary computed tomography angiography, [ 36 ] atrial fibrillation (AF), [ 33 ] diabetes mellitus, [ 37 ] chronic heart failure, [ 27 ] abnormal reactions and allergies patient’s data. Also, other studies used only results from lab tests as [ 26 ] radiology data, [ 35 ] histopathology reports, [ 38 ] test results of pertussis and salmonella. On the other hand, there was data synthetically generated in [ 30 – 32 ] that prevents any concerns about privacy or security.

The selected studies did not show a constant concern about data protection laws. This characteristic may be a consequence of the scientific character of the research since the application scenarios are generally academic environments and controlled. We highlight the two definitions observed regarding privacy and security concerns. In [ 32 ], HIPAA (Health Insurance Portability and Accountability Act) /HITECH and HITECH, and some directives from ISO 13606 applied on [ 3 , 46 ], to store and manage health data.

On the other hand, we can consider that by keeping the security policies independent of the interoperability solutions, the authors aim to make the solutions generic in terms of local laws. Each country has its rules regarding using, sharing, and storing personal data – identification, financial or medical history. Thus, by keeping research an independent decision, studies are concerned with carrying out experiments that reflect real needs using real-world data.

Health Insurance Portability and Accountability Act (HIPAA) aims to ensure rights to citizens in the U.S., such as access to health records and request a copy of their data. As a patient, he can ask to correct something wrong with its health data, safe strategies to share data, and much more. To achieve that, they applied some methods to ensure security as authentication (SSO), authorization (OAuth), identity management, securing data at rest (using 256-bit advanced encryption standard), data in transit (HTTP with SSL), and auditing using a log data on the access. The last security layer represents the EHR stored alone because that provides data without the patient’s identity. Below, we highlight some characteristics regarding privacy, which some studies have indicated as necessary measures for using real-world data in their studies.

Studies that indicated the use of real-world data demonstrated some data providers’ concerns to enable the use of the information without compromising the patients. Therefore, we can observe that, regardless of the study, those who used real-world data need to apply some form of data anonymization. Table ​ Table10 10 present the two ways system providers usually allow data from real-world to academic researchers.

The Table presents the usually followed guidelines inside academic and controlled environments to use data from the real world

When the responsibility for data anonymization rests with the provider, the institution usually executes in a safe zone. Data only leave the health institution after being completely de-identified. On the other hand, the provider may allow researchers to access the system internally. In that case, the electronic registration system must have access levels for users, not allowing unrestricted access.

SQ5 – What are the evaluation approaches used?

The evaluation of an EHR involves storing data with quality, the end-users experience (health care professional), keeping complex information from specialist physicians, and ensuring the exchange of information between healthcare providers. Therefore, a scenario where the system must adapt to the routine of health professionals and facilitate data collection but meet the demands of sharing data with quality.

In this way, approaches to evaluation aim to identify measures to validate the different levels of interoperability (foundation, structural and semantic levels) that an EHR achieved and map the challenges of systematically doing it. The studies presented other evaluation methods, and Table ​ Table11 11 shows the different applied approaches.

Follows we list the evaluation approaches and the types of data used in the articles

Applying questionnaires as an evaluation method allows identifying which changes – according to the user and domain expert – would be more effective in improving the final solution through constant feedback. The author in [ 43 ] had an evaluation based on ISO/IEC 25,040 (SQuaRE), System Usability Scale (SUS), and Health IT Usability Evaluation Scale (Health-ITUES). On the other hand, functional tests allow the initial assessment to establish metrics to outcomes and automated tests.

However, none of the approaches assess the extent of interoperability or validate which different interoperability levels the applications have achieved – as per the levels defined by HIMSS [ 9 ]. Therefore, evaluating interoperability, as a result, is a challenge and an opportunity for further research, looking at more effective and unique methods.

SG1 – What is the state of art in health standards applied in health records?

There are some challenges to be faced by the health specialists and IT professionals to achieve semantic interoperability in EHR. This review identified different approaches to work around known problems and showed what technologies and strategies are being got in the area. Using the two-level (also called dual model) approach is not a global view of health standards. However, it can allow a common syntax and clinical data representation between the systems [ 38 ]. Furthermore, regardless of the management system adopted, this allows of making the clinical model software-independent: the specialized health professionals model the clinical document definition and terminologies as needed. In contrast, computer professionals are concerned with the structure, architecture, and choice of technologies to enable the use of knowledge defined by the health professional.

While there is no health standards consensus, we can see a trend in several studies using the two-level standard. This preference occurs because the OpenEHR standard provides a more mature public library – tools, archetypes, community support, and guides [ 34 ]. Furthermore, ISO 13606 is not publicly available, and developing novel archetypes to satisfy a system would take a lot of time [ 25 ]. Besides, to improve the adoption of the standards, many studies combined semantic web technologies and health standards with openEHR. A common practice identified in the articles was the structure representation of archetypes and reference models in ontologies, combined with rules mapping their constraints—another use involved ontologies representing metamodels with general information and ontologies for disease classification.

The solutions proposed by the selected articles brought different approaches to achieve semantic interoperability. In this scenario, Fig.  4 presents a taxonomy proposal around semantic interoperability in health records. Although the taxonomy is organized into five categories, the articles usually combine multiple themes to achieve the semantic interoperability challenge, so they are not exclusive sections. However, in the process of analyzing the studies, we defined five main categories: 1) Health Standards, 2) Classification and Terminologies, 3) Semantic Web, 4) Data Storage, and 5) Evaluation.

Proposed taxonomy for semantic interoperability in health records

A taxonomy may allow us to assess a broader scenario—e.g., what artifacts involve building an EHR semantically interoperable. We reinforce that the development of an EHR must have semantic interoperability as a goal from early project design. The decision to adopt standards and terminologies at the beginning can facilitate the development process, once the artifacts are discussed as an integral part of the project, as mandatory.

The taxonomy scope involved the accepted articles, all technologies, standards applied to solve semantic interoperability and exchange data across organizations and health systems. The selected studies described many tools usually used to solve interoperability problems. However, we focus on solving the article research question and add that to the taxonomy list. We highlighted the taxonomy is not an exhaustive map to all semantic interoperability-related tools, only applied to the selected studies.

The first category, Health Standard, shows the standards used by the studies, with three subcategories. The dual model—openEHR and ISO 13606 inside share this architecture. Likewise, the HL7 standards have an ecosystem of standards, however, not all have the same goal. The HL7 organization also maintains a comprehensive list of terminologies 3 compatible with available standards.

The choice of health standard may be related to some implementation team characteristics. The HL7 standard prioritizes a friendly relationship with the developer, with technical documentation and structures similar to the development ecosystem. In contrast, the openEHR proposes the opposite. Healthcare professionals have a user-friendly interface to create clinical models (in archetypes), focusing on defining knowledge. On the other hand, the artifacts defined for the openEHR standard—archetypes—have a structure that allows for in-depth detailing of clinical concepts, enabling interoperability at a semantic level.

Using standards inherently raises the concern of employing international terminologies to represent clinical terms and concepts. We show the vocabularies used in the Classification and Terminologies section of the taxonomy. Some terminologies have a massive adherence of studies, such as SNOMED and Logical Observation Identifiers, Names, and Codes (LOINC). Most health standards allow using more than one terminology in the same clinical document with semantic binding once a healthcare organization has legacy data and treats data through many vocabularies—enabled in openEHR and HL7, consequently in IHE.

Adopting terminologies as an inherent part of the EHR brings benefits, such as the standardization of everyday expressions, ensuring semantic contextualization concerning diseases, adverse events, and general classifications. The semantic contextualization allows new connections to collected data once the structure—patient’s history, lab tests, exams—has a semantic binding through international vocabularies. Unfortunately, there are still challenges when discussing the patients’ history. Usually have an open and unstructured text field, a format more accessible to physicians and health professionals. However, an unstructured text field does not readable to the machines.

The studies treat the unstructured data using semantic tools to extract and structure these data. Although there is a low variety of semantic web technology, the combination with health standards was almost unanimous. The most used tools often follow the definitions established by the W3C, such as OWL, RDF, SPARQL, SKOS, as reinforced in the recent research field review [ 52 ]. The Semantic Web section of the taxonomy shows the ontologies most used, such as SKOS, OWL, OWL-DL, and RDF as final semantic resources.

Although the studies do not explore the benefits of the choice of storage solution, some storage solutions are related to the type of health standard adopted. The trend towards the use of ontologies can impact the choice of storage solution since it is interesting to allow querying the ontological structure using SPARQL and exploring reasoning. In this scenario, Virtuoso, Neo4J, and Oracle databases present solutions that adequately meet ontologies’ storage demand.

In the Data Storage section, we map the different solutions used for storage to the taxonomy and categorize them according to the structure they provide. In addition, we highlight among these solutions two that specifically implement clinical document storage—archetypes such as ARM and Think!EHR. The other solutions use web-based graphical and semantic databases (W3C compliant), similar in their storage structure.

Finally, not all papers presented an evaluation format for their experiments or results obtained. Some authors had highlighted the use of case studies in a controlled environment to apply their final experiments. Often the authors used questionnaires with end-users to evaluate the user experience. The functional tests also had a prevalence, tracking specific modules with problematic scenarios while being developed to resolve before use. In some solutions, the authors described their partial evaluation methods during the development process. They wanted to evaluate tools’ accuracy or performance and not precisely the final solution, allowing for semantic interoperability. However, those methods did not consider the taxonomy because they did not influence the final discussion of the evaluated solution.

SG2 – What are the challenge and open questions to semantic interoperability in health records?

The adoption of standards has grown one of the ways to ensure semantic interoperability in health record systems. However, there are many barriers to overcome in the health organizations, such as legacy data, semi-structured data, non-structured textual data, complex systems that are sometimes not compatible for exchanging information. Once get over those challenges yet have internal adversities as the concepts, and medical terms used across the organization must preserve that meaning externally shared.

The search for interoperability at a semantic level involves combining different vocabularies from different areas to maintain a common meaning. For this scenario, the use of terminologies, ontologies, and global classifications plays an important role. However, since there is no worldwide consensus on health standards or unique clinical vocabularies, deciding which standard remains a technical issue when it should be an organizational one. Some research projects towards proposing vocabulary harmonization within EHR systems represent a promising alternative. These can allow the adoption of different vocabularies but with unique meanings. In other words, effective EHR communication depends on standardizing syntax, structure, and semantics (from the chosen architecture to the vocabulary used). The papers presented showed concerns about the difficulty in standardizing and normalizing data in legacy systems, as previously mentioned in [ 42 , 43 ]. Complex models and legacy data are some of the limiters in secondary use and data reuse [ 42 ] and different purposes such as clinical research and decision support systems [ 38 ]. The lack of common terminologies and the extensive use of proprietary concepts inside EHR become the interoperability complex and requires a normalization process [ 43 ].

This review aims to understand the current scenario of solutions employed to solve semantic interoperability in health records. The selected studies allowed evaluating the current strategies used to meet interoperability at a semantic level. In addition, we highlight how interoperability must happen at the organizational level since the difficulties of integrating, sharing, and exchanging health records across all the organization's sectors. Interoperability at the semantic level requires organizational involvement, ranging from institution management to the involved teams such as technology staff and healthcare professionals. Therefore, it is essential to consider the reality in health institutions, where valuing the patient in terms of quality in health is part of all processes and interactions, by delivering quality of care, economy, and efficiency.

The studies evaluated in this review proposed some alternatives involving adopting semantic web technologies combined with health standards to meet this demand. For example, the prevalence of ontologies used to represent reference models and clinical information models of the standard used, as shown in Table ​ Table7. 7 . Moreover, we observed a preference for adopting semantic web technologies recommended by World Wide Web Consortium (W3C), such as RDF, OWL, SPARQL, SKOS, as shown in Table ​ Table8 8 .

On the other hand, studies that explored the two-level standards showed OWL ontology’s predominant use, while studies that explored available HL7 standards showed RDF ontology’s predominant use. Despite this, in both scenarios (HL7 and two-levels – openEHR and ISO13606), the studies advocate adopting ontologies as a suitable format to explore the use of metadata and semantic relationships between clinical terms of the collected data. The justification for using ontologies is regarded with the possibilities of inferences and rules, as the main reason. Also is important the obtained semantic orientation since other formats have a syntactic orientation.

Also, the studies selected highlighted a scenario of fragility, exposing the difficulties for an interoperable electronic medical record when in the fragile reality of health institutions. The existing, legacy, and sometimes non-structured data do not have any standard or vocabulary. Moreover, since there is no integrated system, the patient history is fragmented, with duplicate entries. This scenario is also aggravated at the level of system suppliers, maintaining proprietary structures for storage, which makes it impossible to share data with other institutions without an extended period for adjustments.

The situation within healthcare organizations is still far from the controlled environment often found in research projects. For this reason, it is necessary to observe reality and propose possible and viable alternatives. In this fragile and restricted scenario of institutions, as approached by HIMSS [ 9 ], the systems it partially reaches the first two layers of interoperability—structural and fundamental. Moreover, it is necessary to use an international standard to guarantee a widely accepted syntax and formats even to achieve just these two layers. This is still a significant challenge.

That scenario is reinforced by [ 11 , 20 ], regarding the three fundamental levels of interoperability to improve the workflows across health information systems to allow true interoperability. Inside healthcare institutions, the collect data happens in multiple formats, tabulated and structured—when well done—however essentially as textual non-structured, free form, collected from health professionals in fragmented contexts, added of the lack terminologies and without any international denomination to concepts.

We identified some open questions from this scenario that future studies can explore. For example, open health standards and international terminology knowledge are still far from extensive use in the market and are only widespread in academia (educational institutions) and large software providers. Therefore, the opportunity arises to contribute with measures that mitigate the recurring difficulties of standardization, making knowledge about available resources accessible, easy to implement, and providing adequate materials to facilitate the study.

Since knowledge about patterns is not widespread, few systems implement terminology, classifications, and dictionaries, as they are not part of the healthcare reality in their designs. Therefore, it is crucial to work on initiatives to promote the gradual adoption of terminologies in daily use. That lack means opportunities to propose methodologies to adopt a health standard, mapping possible difficulties and how to face them, offering viable directives for the reality of health institutions.

The interoperability as a focus of integration and information exchange is part of the scope of software companies, a commonly raised concern. However, healthcare systems consider semantic interoperability a luxury when it should be a prerequisite since it directly implies the quality of the stored data, which will later use in secondary studies. Therefore, encouraging the construction of systems aiming to achieve semantic interoperability would allow exploring secondary use of EHR, data reuse, and clinical models and doing clinical research within institutions a reality.

This article presents an overview of international health standards usually applied to allow semantic interoperability in health records. We conducted a Systematic Literature Review based on the protocol proposed by Kitchenham [ 22 ]. Our research set involved seven scientific databases, from which 6032 studies were selected. After the application of the inclusion and exclusion criteria, quality assessment, the result was 28 accepted articles. These were used for complete reading and analysis according to the interest questions.

We observe that the predominance in adopting two levels (ISO13606 and openEHR) is primarily due to an open standard’s nature, extensive documentation available, and is inherently designed to make use accessible to healthcare professionals. That accessibility allows them to build the necessary clinical models without the assistance of specialized professionals. It also refers to the level of granularity that the pattern allows representing since the archetypes can include deeply detailed levels of information regarding a specific clinical concept, allowing for a contextualized semantic level. Finally, it is possible to create a collection of archetypes to construct a template.

In general, we observed studies applied in medical records of hospitals, some scenarios using laboratory data and some experiments aimed at the exploration of clinical models (ISO13606 and openEHR standard). We also analyzed options to maximize the characteristics proposed in the models, such as recovery, forms semantic representation, techniques for enriching semantic relations through metadata and ontology standards. The conducted study made it possible to identify a growing concern about the adoption of open data models to represent clinical knowledge. The main standards mapped were openEHR, ISO13606, and the HL7 framework.

This review reinforces a promising scenario for exploration since there is not an international standard or global consensus on approaches to be adopted. Recent research has shown efforts to define a universal model involving the four levels of interoperability as a guide for a definitive directive. Some studies aimed to allow secondary data exploration, such as clinical research or decision support systems. Ontologies have been widely used, and we observed good results in adopting semantic web technologies, mainly using ontologies combined with patterns, to increase data representation in formats with a semantic focus. With data represented in ontologies, this scenario encourages the exploration of linked databases (LOD) and databases based on graphics (Neo4J and Virtuoso), but without a final definition of the advantages of adopting these databases. Finally, we highlight the trend in the adoption of semantic web technologies recommended by the W3C. The advantages of health standards (clinical models and reference models) with terminologies combined with graph structures (ontologies) for representing data from electronic health records, relation, and constraints rules.

Acknowledgements

and funding

The authors would like to thank the Coordination for the Improvement of Higher Education Personnel—CAPES (Financial Code 001) and the National Council for Scientific and Technological Development—CNPq (Grant number 309537/2020–7) for supporting this work.

Author contributions

All authors have made a substantial, direct, intellectual contribution to this study.

Data availability

Code availability (software application or custom code), declarations.

1 https://www.himss.org/who-we-are/ (HIMSS) Healthcare Information and Management Systems Society.

2 https://www.hl7.org/fhir/

3 http://www.hl7.org/documentcenter/public/standards/vocabulary/vocabulary_tables/infrastructure/vocabulary/vocabulary.html

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