what is gap analysis in clinical research

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How To Perform A Gap Analysis In Healthcare

Laurel Miyake

Laurel is our organization's HR business partner, fostering a positive work environment and contributing to the growth of employees.

Learn how to perform a gap analysis in healthcare. Identify where your organization stands and the steps needed to reach your strategic goals.

Table of Contents

The optimal use of time, money, materials, and human resources is perhaps more important in healthcare than in any other industry. With a myriad of fast-changing regulatory requirements and a high-stakes mission of saving lives, healthcare organizations must constantly strive to deliver the highest quality service while controlling costs and managing resources as efficiently as possible.

To do this, many are adopting methodologies used by for-profit businesses to help analyze their performance and make targeted process improvements.

One of those methodologies is a gap analysis. A gap analysis in healthcare is intended to identify gaps in services or processes—instances in which what is happening is falling short of what should be happening—and shine a light on why these gaps exist. Such an analysis is crucial for improving care delivery and outcomes.

In this article, we’ll delve into the importance of a healthcare gap analysis and offer a step-by-step guide on how to perform one.

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What is a gap analysis in healthcare.

A gap analysis is an examination and assessment of your performance for the purpose of identifying the differences between your current state of operation and where you’d like to be.

It helps clarify the difference between reality and the ideal for your organization—for instance, on measures where you’re falling short of a target—so you can better focus your resources and energy on those identified areas in order to improve them.

For each process or business unit being examined, a gap analysis can be boiled down into three questions:

• Where are we now?
• Where do we want to be?
• What can we do to close the gap in performance?

For example, you might have this organizational objective:

Offer industry-best medical treatments that lead to measurable improvement in patient well-being.

A measure and target for this objective might be:

Reduce the percentage of hospital readmission rates, with a target of 15% reduction over the course of a year.

If your organization is not on track to meet this target halfway through the year, a gap analysis will help uncover organizational practices and/or processes that may be preventing you from achieving it. Such an analysis can be done within a department, for an entire business, or even just for a single process.

The gap analysis is not new; for-profit businesses have long used it to explain deficits between actual and real performance. And even though the concept of performance analysis is becoming a more common practice among healthcare organizations, many still don’t use it intentionally, nor do they have a consistent process they follow for carrying it out.

It’s beneficial to perform gap analyses regularly (depending on the size of your organization, annually is a good guideline) so you can stay on top of performance, particularly if you’re pursuing PHAB accreditation or something similar. But it’s also an excellent problem-solving tool to apply whenever problems crop up, like:

• Patient complaints
• Underperformance on a specific metric or on the part of a certain department
• Issues meeting healthcare compliance requirements
• Noticeable differences in the current vs. desired states

Identifying process and service gaps can also be helpful during your annual strategic planning activities. Knowing the specific issues that need to be solved in order to meet your targets may inform the projects you choose to take on in the year ahead. Are there projects or actions you could implement to help you change course and close a gap? Being strategic about the allocation of your time and resources on projects that will move the dial one way or the other is key to improving performance. Using strategic planning software like ClearPoint, you can easily align all your activities to goals and evaluate performance simply.

Note that gaps in care are different than gaps in processes and services. What are gaps in care? They refer to gaps between the recommended best practices for medical care and the care that’s actually provided—for instance, if patients aren’t utilizing annual screenings that would be considered a gap in care. Performance improvement in this area requires a different strategy.

How to Perform a Gap Analysis in 5 Steps

Below are the steps to perform a typical gap analysis in healthcare. For organizations that have many divisions, facilities, programs, etc., these may be performed on a smaller scale, as processes/services differ across these categories.

1. Identify the current state of the program or process

The first step is identifying or realizing the gap. Look at your measures (an example would be the one noted above) and their RAG statuses —are they yellow, red, or green? Also look at the trend over time—has it been red for a while, or just the past few months? The answer will likely inform the factors that may be influencing it as well as the cause.

2. Identify and define the desired state as well as the best practice needed to reach the desired state

If you are doing a gap analysis within the context of your strategic plan, take a look at the targets on your plan. These targets may be three to five years out, which is ideal. Where are you with them? To answer that, go back to your current state areas of focus. You likely have an idea about best practices around how to reach the desired state—document those for further analysis.

3. Figure out the nature of the gap

If you’re concerned with low patient satisfaction rates, for example, what’s the difference between where they are and what you would like them to be? Whether it’s large or small, this is the gap. This is also a good time to figure out why there is a gap. To do that, ask questions—and question the answers to those questions—of everyone involved in the process, at all levels. This may involve in-person, facilitated focus groups or discussions with key stakeholders (clinicians, administration, execs, etc.). What are people (both leaders and workers) noticing day to day surrounding this issue? Also, review your documentation around organizational policies and procedures, which may be a contributing factor. You’ll have to dig deep to find these answers.

4. Develop and implement a plan to achieve the desired results

Now that you’ve discovered why the gap is taking place, it’s time to figure out the proper course of action to close it. As you’re doing so, take into consideration the cost of implementation for each solution—do you have enough resources to allocate to it? If it’s a main contributor to realizing an organizational objective, you may need to rethink existing projects and their allocated resources to accommodate the new action plan.

Once you’ve read through this gap analysis template and created your own, be sure to follow up on the improvements. Otherwise, there’s a risk that the solutions you’ve so carefully engineered will fall through the cracks. Periodically review the results of your gap analysis and continue to define next steps in the implementation process.

Gap Analysis Examples In Healthcare

A hospital gap analysis: patient safety.

Patient safety is of prime concern to hospitals. Falls that occur on hospital grounds are particularly troublesome because they impact patients’ quality of life and are extremely costly for organizations.

So when one medical care center was experiencing fall rates that exceeded national benchmarks by more than 40%, it initiated a gap analysis to assess current practices and improve.

The medical center’s analysis of current practices as compared to industry best practices shed light on gaps in its current level of care. Among other things, it noticed that nurses were not consistently proactive with patient check-ins, and there was a lack of readily available fall prevention equipment. To address these issues, the center implemented the following:

• Educational sessions for nurses around check-ins, including how best to provide mobilization assistance and address patient needs. Unit leaders also began doing daily rounds to evaluate critical factors in fall prevention for patients.
• Additional fall prevention equipment, and provided staff education on the correct use and transfer of mobility equipment.

Once these and other corrective actions were taken to address all the identified gaps, the medical center began tracking the change to falls and injury rates, and regularly reviewed the results. Since the project began, the center has seen a steady decrease in falls.

Albany Medical Center Hospital: A Workforce Gap Analysis

After the introduction of New York State’s Medicaid redesign program, Albany Medical Center Hospital (AMCH) anticipated a change in demand for healthcare workers within its provider network. To prepare for the impact, AMCH conducted a gap analysis to identify discrepancies between the state of the current and target workforce.

The analysis projected an increase in the need for primary care providers, medical assistants and administrative support staff, nurses, and the care management workforce; it also anticipated a significant increase in demand for patient navigators, community health workers, and care coordinators. Findings from the gap analysis were used to develop a workforce transition roadmap that would help reach the target workforce state.

The gap analysis also helped identify challenges in managing the workforce impacts, including redeploying, retraining, and hiring for positions as needed.

Gap Analysis As Part Of Healthcare Performance Management

For healthcare organizations, gap analysis is a key part of performance management, which is vital for delivering the highest quality care and outcomes. Healthcare performance management software like ClearPoint helps ensure you are continually improving processes to achieve organizational objectives.

ClearPoint allows you to house all information relevant to your gap analysis in one place, and see the results in relation to your overall organizational strategy. It allows you to:

• Examine and assess current performance using measures. You can track different areas of performance, whether it’s patient care, provider reviews, or anything else. Our RAG status feature makes it easy to see how you’re doing with all your healthcare metrics (using visual red, amber/yellow, or green indicators), and you can quickly view trends over time.

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• Link process improvements to relevant goals and objectives. Our software allows you to link any proposed improvements to organizational goals, so you can see the impact your gap plan is having; it also helps inform what you may want to do going forward. Our clients frequently use summary reports for gap analyses data related to current and desired states.

• Define initiatives and action items for improvement. Our software allows you to track action items and link them to goals or measures. You can track who is accountable, and they can give regular updates on their progress. Depending on the project, users can even attach files and images to help provide context for the work they are doing.

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ClearPoint Strategy Software empowers your organization to identify gaps, optimize resources, and achieve strategic goals with precision. Experience firsthand how ClearPoint can transform your operations. Book your FREE demo today and see ClearPoint in action!

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How do you perform a gap analysis in healthcare.

To perform a gap analysis in healthcare:

- Define Objectives: Clearly outline the desired goals or standards you want to achieve. - Assess Current Performance: Collect and analyze data on current performance levels using metrics and benchmarks. - Identify Gaps: Compare the current performance with the desired goals to identify gaps. - Analyze Root Causes: Determine the underlying causes of the gaps by examining processes, resources, and workflows. - Develop Action Plans: Create targeted action plans to address the identified gaps and improve performance. - Implement Changes: Implement the action plans, ensuring resources and support are in place. - Monitor Progress: Continuously monitor and evaluate the progress to ensure the gaps are being effectively closed.

Why perform a gap analysis in healthcare?

Performing a gap analysis in healthcare is important because it:

- Identifies Improvement Areas: Pinpoints specific areas where performance does not meet desired standards. - Enhances Patient Care: Helps improve the quality of patient care by addressing deficiencies. - Supports Strategic Planning: Provides valuable insights for strategic planning and resource allocation. - Ensures Compliance: Ensures compliance with healthcare regulations and standards by identifying and addressing gaps. - Increases Efficiency: Identifies inefficiencies in processes and workflows, leading to better resource utilization.

What are the benefits of performing a gap analysis in healthcare?

The benefits of performing a gap analysis in healthcare include:

- Improved Patient Outcomes: Enhances the quality of care provided to patients by addressing gaps in service delivery. - Informed Decision-Making: Provides data-driven insights that inform strategic decisions and planning. - Resource Optimization: Helps allocate resources more effectively to areas that need improvement. - Regulatory Compliance: Ensures that the organization meets all regulatory requirements and standards. - Enhanced Efficiency: Streamlines processes and eliminates inefficiencies, leading to better operational performance.

When should you perform a gap analysis in healthcare?

You should perform a gap analysis in healthcare:

- During Strategic Planning: To inform and guide the development of strategic plans and initiatives. - Before Implementing New Programs: To ensure that new programs are designed to address existing gaps and meet organizational goals. - After Performance Reviews: Following regular performance reviews or audits to identify areas needing improvement. - In Response to Regulatory Changes: When new regulations or standards are introduced to ensure compliance. - Periodically: As part of ongoing efforts to continuously improve healthcare delivery and patient outcomes.

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Cochrane Colloquium Abstracts

Methods for identifying and displaying gaps in clinical research.

Article type Oral Year 2018 Edinburgh Authors Nyanchoka L 1 , Tudur-Smith C 2 , Nguyen VT 1 , Porcher R 1 1 Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS-UMR1153) Inserm/Université Paris Descartes 2 University of Liverpool, Institute of Translational Medicine Abstract Background: The current body of clinical research is growing, with over one million research papers published from clinical trials alone. This volume of health research demonstrates the importance of conducting knowledge syntheses to provide an evidence base and identify gaps, which can inform further research, policy-making and practice. Objectives: This study aims to describe methods for identifying and displaying research gaps. Methods: We conducted a scoping review using the Arksey and O'Malley methodological framework. We searched MEDLINE, PubMed, Embase, the Cochrane Library, Scopus, Web of Science, the PROSPERO register, TRIP, Google Scholar and Google. The searches were limited to studies in English, conducted in humans and published in the last 10 years for database searches and unrestricted for handsearch and expert suggestion articles. Results: The literature search retrieved 1938 references, of which we included 139 for data synthesis. Of the 139 studies, 91 (65%) aimed to identify gaps, 22 (16%) to determine research priorities and 26 (19%) had both aims. A total of 13 different definitions of research gaps were identified. The methods for identifying gaps included different study designs: some examples included primary research methods (quantitative surveys, interviews and focus groups), secondary research methods (systematic reviews, overviews of reviews, scoping reviews, evidence mapping and bibliometric analysis), and primary and secondary research methods (James Lind Alliance Priority Setting Partnerships (JLA PSP) and Global Evidence Mapping (GEM)). Conclusions: This study provides an overview of the different methods used to identify and/or report on gaps, to determine research priorities and to display both gaps and research priorities. These study findings can be adapted to inform the development of methodological guidance on ways to advance methods to identify, prioritise and display gaps to inform research and evidence-based decision-making. Patient or healthcare consumer involvement: Of the 139 articles included in the scoping review only 20 articles described the involvement and/or participation of patients and consumers in their studies; this was primarily in determining research priorities versus identifying gaps.

Article | April 11, 2022

Gap analysis and collaboration lead to clinical success.

By Dawn Niccum, inSeption Group

Gap analysis targeting a life science organization’s quality management system (QMS) evaluates whether the organization has adequate processes, personnel, documentation, and systems in place to properly conduct its clinical trial. Its key deliverable is a “report card” — an independent analysis that can bolster the organization’s confidence in its position moving forward, as well as help to identify, prioritize, and remediate potentially problematic areas. Gap analysis often is considered when an organization already is in a later phase of clinical development, such as Phase 3, but initial entry into the clinic is the ideal time to conduct gap analysis. The later a gap analysis takes place, the more remediation becomes necessary to address issues that have been identified. And, the farther along a product is in development, the more difficult it becomes to make changes. Not only must procedures potentially be changed, so must personnel training and day-to-day behaviors that have been ingrained. A truly comprehensive gap analysis also loops the client into findings as the exercise is ongoing, rather than simply producing a one-off report at the end. What should the client focus on first? Which compliance issues must be solved immediately and what needs to be in place when going to market? Biopharma organizations should seek a partner agile enough to tweak the analysis based on their feedback regarding what might be helpful. A one-size-fits-all approach is ill-suited to an undertaking designed to provide client- and situation-specific guidance, particularly since the gap analysis is a start, not the end, to QMS and process optimization.

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The Research Gap (Literature Gap)

I f you’re just starting out in research, chances are you’ve heard about the elusive research gap (also called a literature gap). In this post, we’ll explore the tricky topic of research gaps. We’ll explain what a research gap is, look at the four most common types of research gaps, and unpack how you can go about finding a suitable research gap for your dissertation, thesis or research project.

Overview: Research Gap 101

• What is a research gap
• Four common types of research gaps
• Practical examples
• How to find research gaps
• Recap & key takeaways

What (exactly) is a research gap?

Well, at the simplest level, a research gap is essentially an unanswered question or unresolved problem in a field, which reflects a lack of existing research in that space. Alternatively, a research gap can also exist when there’s already a fair deal of existing research, but where the findings of the studies pull in different directions , making it difficult to draw firm conclusions.

For example, let’s say your research aims to identify the cause (or causes) of a particular disease. Upon reviewing the literature, you may find that there’s a body of research that points toward cigarette smoking as a key factor – but at the same time, a large body of research that finds no link between smoking and the disease. In that case, you may have something of a research gap that warrants further investigation.

Now that we’ve defined what a research gap is – an unanswered question or unresolved problem – let’s look at a few different types of research gaps.

Types of research gaps

While there are many different types of research gaps, the four most common ones we encounter when helping students at Grad Coach are as follows:

• The classic literature gap
• The disagreement gap
• The contextual gap, and
• The methodological gap

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1. The Classic Literature Gap

First up is the classic literature gap. This type of research gap emerges when there’s a new concept or phenomenon that hasn’t been studied much, or at all. For example, when a social media platform is launched, there’s an opportunity to explore its impacts on users, how it could be leveraged for marketing, its impact on society, and so on. The same applies for new technologies, new modes of communication, transportation, etc.

Classic literature gaps can present exciting research opportunities , but a drawback you need to be aware of is that with this type of research gap, you’ll be exploring completely new territory . This means you’ll have to draw on adjacent literature (that is, research in adjacent fields) to build your literature review, as there naturally won’t be very many existing studies that directly relate to the topic. While this is manageable, it can be challenging for first-time researchers, so be careful not to bite off more than you can chew.

2. The Disagreement Gap

As the name suggests, the disagreement gap emerges when there are contrasting or contradictory findings in the existing research regarding a specific research question (or set of questions). The hypothetical example we looked at earlier regarding the causes of a disease reflects a disagreement gap.

Importantly, for this type of research gap, there needs to be a relatively balanced set of opposing findings . In other words, a situation where 95% of studies find one result and 5% find the opposite result wouldn’t quite constitute a disagreement in the literature. Of course, it’s hard to quantify exactly how much weight to give to each study, but you’ll need to at least show that the opposing findings aren’t simply a corner-case anomaly .

3. The Contextual Gap

The third type of research gap is the contextual gap. Simply put, a contextual gap exists when there’s already a decent body of existing research on a particular topic, but an absence of research in specific contexts .

For example, there could be a lack of research on:

• A specific population – perhaps a certain age group, gender or ethnicity
• A geographic area – for example, a city, country or region
• A certain time period – perhaps the bulk of the studies took place many years or even decades ago and the landscape has changed.

The contextual gap is a popular option for dissertations and theses, especially for first-time researchers, as it allows you to develop your research on a solid foundation of existing literature and potentially even use existing survey measures.

Importantly, if you’re gonna go this route, you need to ensure that there’s a plausible reason why you’d expect potential differences in the specific context you choose. If there’s no reason to expect different results between existing and new contexts, the research gap wouldn’t be well justified. So, make sure that you can clearly articulate why your chosen context is “different” from existing studies and why that might reasonably result in different findings.

4. The Methodological Gap

Last but not least, we have the methodological gap. As the name suggests, this type of research gap emerges as a result of the research methodology or design of existing studies. With this approach, you’d argue that the methodology of existing studies is lacking in some way , or that they’re missing a certain perspective.

For example, you might argue that the bulk of the existing research has taken a quantitative approach, and therefore there is a lack of rich insight and texture that a qualitative study could provide. Similarly, you might argue that existing studies have primarily taken a cross-sectional approach , and as a result, have only provided a snapshot view of the situation – whereas a longitudinal approach could help uncover how constructs or variables have evolved over time.

Practical Examples

Let’s take a look at some practical examples so that you can see how research gaps are typically expressed in written form. Keep in mind that these are just examples – not actual current gaps (we’ll show you how to find these a little later!).

Context: Healthcare

Despite extensive research on diabetes management, there’s a research gap in terms of understanding the effectiveness of digital health interventions in rural populations (compared to urban ones) within Eastern Europe.

Context: Environmental Science

While a wealth of research exists regarding plastic pollution in oceans, there is significantly less understanding of microplastic accumulation in freshwater ecosystems like rivers and lakes, particularly within Southern Africa.

Context: Education

While empirical research surrounding online learning has grown over the past five years, there remains a lack of comprehensive studies regarding the effectiveness of online learning for students with special educational needs.

As you can see in each of these examples, the author begins by clearly acknowledging the existing research and then proceeds to explain where the current area of lack (i.e., the research gap) exists.

How To Find A Research Gap

Now that you’ve got a clearer picture of the different types of research gaps, the next question is of course, “how do you find these research gaps?” .

Well, we cover the process of how to find original, high-value research gaps in a separate post . But, for now, I’ll share a basic two-step strategy here to help you find potential research gaps.

As a starting point, you should find as many literature reviews, systematic reviews and meta-analyses as you can, covering your area of interest. Additionally, you should dig into the most recent journal articles to wrap your head around the current state of knowledge. It’s also a good idea to look at recent dissertations and theses (especially doctoral-level ones). Dissertation databases such as ProQuest, EBSCO and Open Access are a goldmine for this sort of thing. Importantly, make sure that you’re looking at recent resources (ideally those published in the last year or two), or the gaps you find might have already been plugged by other researchers.

Once you’ve gathered a meaty collection of resources, the section that you really want to focus on is the one titled “ further research opportunities ” or “further research is needed”. In this section, the researchers will explicitly state where more studies are required – in other words, where potential research gaps may exist. You can also look at the “ limitations ” section of the studies, as this will often spur ideas for methodology-based research gaps.

By following this process, you’ll orient yourself with the current state of research , which will lay the foundation for you to identify potential research gaps. You can then start drawing up a shortlist of ideas and evaluating them as candidate topics . But remember, make sure you’re looking at recent articles – there’s no use going down a rabbit hole only to find that someone’s already filled the gap 🙂

Let’s Recap

We’ve covered a lot of ground in this post. Here are the key takeaways:

• A research gap is an unanswered question or unresolved problem in a field, which reflects a lack of existing research in that space.
• The four most common types of research gaps are the classic literature gap, the disagreement gap, the contextual gap and the methodological gap.
• To find potential research gaps, start by reviewing recent journal articles in your area of interest, paying particular attention to the FRIN section .

If you’re keen to learn more about research gaps and research topic ideation in general, be sure to check out the rest of the Grad Coach Blog . Alternatively, if you’re looking for 1-on-1 support with your dissertation, thesis or research project, be sure to check out our private coaching service .

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42 Comments

This post is REALLY more than useful, Thank you very very much

Very helpful specialy, for those who are new for writing a research! So thank you very much!!

I found it very helpful article. Thank you.

it very good but what need to be clear with the concept is when di we use research gap before we conduct aresearch or after we finished it ,or are we propose it to be solved or studied or to show that we are unable to cover so that we let it to be studied by other researchers ?

Just at the time when I needed it, really helpful.

Very helpful and well-explained. Thank you

VERY HELPFUL

We’re very grateful for your guidance, indeed we have been learning a lot from you , so thank you abundantly once again.

hello brother could you explain to me this question explain the gaps that researchers are coming up with ?

Am just starting to write my research paper. your publication is very helpful. Thanks so much

How to cite the author of this?

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Kindly explain to me how to generate good research objectives.

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How to tabulate research gap

Very helpful, thank you.

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This is really helpful indeed!

This article is really helpfull in discussing how will we be able to define better a research problem of our interest. Thanks so much.

Reading this just in good time as i prepare the proposal for my PhD topic defense.

Very helpful Thanks a lot.

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This was very timely. Kudos

Great one! Thank you all.

Thank you very much.

This is so enlightening. Disagreement gap. Thanks for the insight.

How do I Cite this document please?

Research gap about career choice given me Example bro?

I found this information so relevant as I am embarking on a Masters Degree. Thank you for this eye opener. It make me feel I can work diligently and smart on my research proposal.

This is very helpful to beginners of research. You have good teaching strategy that use favorable language that limit everyone from being bored. Kudos!!!!!

This plat form is very useful under academic arena therefore im stil learning a lot of informations that will help to reduce the burden during development of my PhD thesis

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Insightful…

I have found this quite helpful. I will continue using gradcoach for research assistance

Doing research in PhD accounting, my research topic is: Business Environment and Small Business Performance: The Moderating Effect of Financial Literacy in Eastern Uganda. I am failing to focus the idea in the accounting areas. my supervisor tells me my research is more of in the business field. the literature i have surveyed has used financial literacy as an independent variable and not as a moderator. Kindly give me some guidance here. the core problem is that despite the various studies, small businesses continue to collapse in the region. my vision is that financial literacy is still one of the major challenges hence the need for this topic.

An excellent work, it’s really helpful

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Clinical Data Gap Analysis ­Uncovering Hidden Opportunities

David Fishman, MBA President Snowfish LLC

The practice of medicine relies on clinical evidence. From a product’s conception through approval, launch, and post-launch activities, clinical evidence is the rationale for a product’s overall existence. Clinical data is critical for differentiation and ultimately, market acceptance. A solid grasp of the clinical data for a product and its competitors can open up new opportunities and markets.

The starting point to assessing the market opportunity, setting product direction, recommending clinical trials, and building differentiation is systematic analysis of the available data. This may be accomplished through a process known as clinical data gap analysis.

Gaining a solid grasp of the clinical data can be a significant challenge. For a given therapeutic class or disease state, there are often hundreds and, in some cases, thousands of clinical trials.

Furthermore, individual studies will likely measure a number of heterogeneous trial elements such as study population, multiple primary and secondary endpoints, product claims, and attributes. There is clearly a significant amount of data to capture, organize, and analyze.

Why Perform a Clinical Data Gap Analysis?

Clinical data impacts a whole range of activities across multiple departments in a pharmaceutical or medical device company. Clinical data gap analysis provides a systematic review of a product’s data, compares it with that of its competitors, and then assesses how uncovered gaps fit within contemporary clinical practice. Undergoing this systematic review helps to set a product’s course and uncover hidden opportunities.

A clinical data gap analysis is typically used by various areas within life sciences companies including: » New Product Development

» Medical Affairs » Marketing » Research & Development

The clinical data gap analysis is most often used for:

» Clinical trial planning » Product development » Brand positioning » Product planning

What is the Process of Clinical Data Gap Analysis?

Our approach to performing a clinical data gap analysis captures all the available clinical information on a given disease state utilizing information from multiple disparate sources and compares the available data for a particular product with that of its competitors. These “data gaps" are identified through the review of hundreds to thousands of published and unpublished clinical studies. Not all of the identified gaps are worth pursuing. To maximize efficiency the top clinical gaps are tested with clinical stakeholders. To facilitate decision-making, the gaps are placed into an overall analytic framework to further refine the analysis and determine the impact of a particular direction.

The strength of clinical data gap analysis is that all relevant information is captured, analyzed, and tested. The value of clinical data gap analysis is in evaluating the individual gaps and determining their relative weighting based on individual company circumstances and resources. These weightings provide insight on the areas with the highest potential to impact a product’s commercial success.

Benefits of a Clinical Data Gap Analysis

Through clinical data gap analysis, a company can determine if it is truly necessary to follow the common paths being taken by the competition, jump on the next important clinical use, and uncover very useful data that already exists and can be instrumental to further product growth.

Clinical data gap analysis is an integral tool for life sciences companies to maximize the market opportunities for their products. This type of analysis allows companies to make the soundest decisions in planning for their products and use their precious resources most wisely.

For more information on uncovering hidden opportunities for your product(s), access the complete white paper below.

Snowfish LLC provides strategic market analysis and insights to healthcare, life-­sciences, and biotechnology companies. To contact Mr. Fishman directly, please call 1-800-Too-Snow-Guy (866-766-9489).

For more information, visit snowfish.net The Power of Clinical Data Gap Analysis Provided by: Snowfish

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ICH Gap Analysis

This assessment tool has been specifically designed to test knowledge of ich guidelines, while assessing your ability to analyze and apply the principles in common clinical research settings..

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) provides clinical researchers with valuable guidelines to promote the safety, conduct, design, and reporting of clinical trials.

ICH categories covered in this assessment include:

• E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting
• E6(R2) – Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice
• E8 – General Considerations for Clinical Trials
• E9 – Statistical Principles for Clinical Trials
• E11 – Clinical Investigation of Medicinal Products in the Pediatric Population

Upon completion of this activity, participants should be able to:

• Assess basic knowledge of the ICH Guidelines related to clinical research.
• Assess ability to analyze principals of these ICH Guidelines.
• Assess ability to apply the principals of these ICH Guidelines to your work setting.

Approved for 2.0 ACRP Contact Hours | Available 1 Year from Enrollment Date

The Academy of Clinical Research Professionals, the independent affiliate responsible for developing and administering ACRP’s certification programs, does not require, endorse, recommend, sponsor, nor participate in the development of any exam preparation resources. The exam preparation materials available on this website have been independently developed by the Association of Clinical Research Professionals (ACRP). ACRP strongly discourages the use of any exam preparation resources from external sources.

Mind the Gap: Best Practices in Clinical Pharmacology Gap Analysis

Do you get anxious about taking tests? Many people do because they want to show their best efforts.

Submitting your New Drug Application (NDA) to the FDA can be thought of as the ultimate test of a drug program. Are you confident that you’ll have robust answers to the 40 different questions that the agency will ask about your clinical pharmacology data package at the time of a NDA submission? If the thought gives you “pre-test jitters,” you might want to invest in clinical pharmacology gap analysis—a tool that can help you evaluate and address any potential gaps in your program before the FDA does .

What is gap analysis?

Creating a clinical pharmacology strategy involves assessing a sponsors’ development program across multiple domains to craft a strategy to address each. For a target product or program, the strategy includes the following elements:

• Identifying potential R&D or regulatory challenges, custom to the molecule, therapeutic area, and competitive landscape,
• Ensuring integration of pre-clinical findings with planned clinical programs,
• Creating a clinical pharmacology development program in line with anticipated regulatory filing strategy,
• Identifying and leveraging pharmacometrics and other model-informed drug development technologies that will increase speed and efficiency,
• Guiding interactions with regulatory agencies for research programs and submittals.

The first step in a strategic assessment is a gap analysis. In conducting a program gap analysis, we consider the 40 different questions that the agency will ask about your clinical pharmacology data package at the time of a NDA submission. This allows one to evaluate and address any potential gaps before the FDA does at critical milestones such as End of Phase 1 (EOP1), EOP2 or Pre-NDA while ensuring that your NDA will contain all the elements needed to support review and informative actionable labeling for your product. In addition to identifying gaps and hot spots, a clinical pharmacology development strategy is created to ensure each of the relevant domains are covered, that gaps are properly addressed, and that data is gathered at meaningful times to enhance decision-making during development. While best conducted early, a gap analysis provides unquestionable ROI at any stage of development.

Reducing the uncertainty of drug development

A group from the US FDA, academia, and industry recently wrote a paper articulating how clinical pharmacology methods and quantitative frameworks can improve the efficiency of drug development and evaluation. 1 That 2017 Clinical Pharmacology and Therapeutics paper, “Improving the Tools of Clinical Pharmacology: Goals for 2017 and Beyond,” attributes the limitations in drug development to scientific challenges in predicting efficacy and safety or characterizing sources of response variability for a drug compound at early, less expensive stages of discovery. 1

The field of clinical pharmacology can help stakeholders address these challenges and improve decision-making at critical milestones, whether early in proof-of-concept phases (pre-clinical through 2a) or in the later stages where a more robust risk and efficacy profile is established (2b through 3). The tools, methods, and frameworks (e.g., mechanistic or quantitative) of clinical pharmacology span distinct sub-specialties and can significantly impact these pre-clinical and clinical phases. They can greatly reduce uncertainty related to therapeutic targets, dosing, and patient populations in which the novel compound may have the most efficacy. 1

Clinical pharmacology comprises about 50% of a drug label . Its importance in drug development and clinical decision-making is undisputed. These principles guide our approach to gap analysis.

The clinical pharmacology review process

FDA’s Center for Drug Evaluation and Research, Office of Clinical Pharmacology (OCP) recently updated its Manual of Policies and Procedures (MAPP) Good Review Practices for New Molecular Entities (NME), New Drug Applications (NDAs), and Original Biologics License Applications (BLAs). The MAPP includes guiding principles for the OCP integrated review, specific templates and sections for review, a guide for labeling issue identification, and a clinical pharmacology and pharmacometric summary table. OCP reviewers use the Question Based Review (QBR) outlined in the MAPP to guide NDA and BLA reviews.

Clinical pharmacology is a multidisciplinary science. Thus, OCP reviews of NME NDAs and original BLAs synthesize information from relevant areas including drug disposition , pharmacology and biomarkers , quantitative methods , drug safety , drug efficacy, pharmacotherapy , and clinical trial methods to inform regulatory decisions (e.g., approvability, labeling, post-approval requirements, and product lifecycle management). Pharmacometric analyses are a key component of each question in the OCP QBR and are used to provide:

• Support of drug activity
• Identify subsets of patients with notably large treatment benefits or favorable risk/benefit balance or a drug with significant toxicity or otherwise marginal average treatment effects
• Support of a single adequate and well-controlled clinical trial using dose-response and/or exposure-response trends
• Support the dosing regimen
• Identify intrinsic factors that influence exposure and/or PD of the drug
• Support dosing strategy based on modeling and simulation
• Justify dosing for subgroups and specific covariates (age, weight, renal/hepatic)

The OCP review is issue-driven and assesses information in the applicant’s submission with established knowledge to address dose selection and optimization, therapeutic individualization, and benefit/risk balance for the general population and for subpopulations. The OCP review also identifies critical gaps in the understanding of conditions for optimal therapeutic use and recommends studies that can address those gaps. Established and evolving regulatory policies and practices guide OCP recommendations. 2

The purpose of gap analysis

We help position sponsors for successful interactions with regulators and other partners by creating for them a clinical pharmacology and pharmacometrics roadmap that prioritizes needs, provides strategic direction, identifies gaps, and assesses risk/benefits. The strategic plan will be harmonized with the sponsor’s overall clinical development plan and considers strategies to support breakthrough therapy applications and accelerated versus regular approval pathways. In all scenarios, the gap analysis and strategic plan identifies and mitigates risks which could become either decision-making hurdles during development or regulatory hurdles at the time of approval.

A gap analysis begins with evaluating all available data and information on the compound, including the Target Product Profile (TPP), Investigator’s Brochure, clinical study plans, any regulatory meeting minutes, and all available pre-clinical and clinical technical data. A gap analysis report will outline the clinical pharmacology program needs, assess which dedicated studies are needed and why, and recommends the use of pharmacometrics and other quantitative methods to expedite timelines, reduce cost, and minimize clinical studies wherever possible.

Questions asked and answered in a gap analysis include:

• Will the completed or planned studies support the OCP question-based review (QBR) and labeling?
• Are the data collected sufficient to support planned analyses?
• Does the quality of existing data, analyses, study designs, and overall clinical approach support the desired regulatory strategy?
• Are we leveraging the ‘best’ science and technology available?
• Does the data support the goals of the TPP?
• Is more evidence needed? If so, is it better to obtain this evidence through standalone studies or through quantitative analyses?

The gap analysis summary report will provide the sponsor with a plan to address any clinical pharmacology gaps and recommend strategies for submitting a data package for regulatory approval. Gap analysis can be performed in early development, in advance of the IND submittal, in mid-development, either for the End of Phase 1 or End of Phase 2 meeting, or later in development, as a company prepares the NDA or BLA submission.

The return on Investment (ROI) of gap analysis

A gap analysis provides a roadmap for success, translates model-informed drug development (MIDD) into the decision-making process, and identifies ways to either support or supplant clinical studies. The areas for which MIDD can be leveraged include drug-drug interaction (DDI) strategy, the approach to support dose justification based on pharmacokinetic/pharmacodynamic (PK/PD) and exposure response, the strategy to meet evolving requirements for QTc assessment, the plan for addressing special populations (renal/hepatic impairment), and opportunities for pharmacogenomics. Our staff of 550 professionals has years of development experience in FDA and in both large and small pharma. They are eminently capable of performing these analyses. While maintaining regulatory standards, we create efficiencies through better study designs and integrating of MIDD and other technologies. Because we’ve sat on both sides of the table at critical regulatory meetings, we are confident in our recommendations. Typically, the ROI for this analysis is 10-20x, and frequently 50-100x or more, depending on the program. The ROI includes reduced study size, expedited timelines, and studies that can be replaced by MIDD. For example, our work in physiologically-based pharmacokinetics (PBPK) has achieved more than 100 label claims without the need for clinical studies.

Modeling & simulation: a “useful predictive tool”

Understanding and selecting the correct tool to answer key drug development questions and optimize decision-making is key. Our portfolio of tools in performing a gap analysis and recommending a strategic roadmap include:

• Drug Development and Regulatory Strategy Consulting – As the industry migrates from a ‘best in class’ to a ‘best in value’ perspective, sponsors’ scientific, regulatory and commercial strategies must be well-aligned. An integrated decision support system focuses on increasing confidence, understanding all aspects of safety and efficacy, optimizing cost and development time, and guiding development using model-informed drug discovery and development (MID3).
• Pharmacometrics Modeling – Population PK, exposure-response and disease-state modeling are used to predict clinical outcomes, provide support for dose recommendations, justification and modification, assess trends for safety and efficacy across exposure ranges, and inform ‘go/no go’ decisions.
• PBPK – PBPK technology informs key R&D decisions related to clinical trial design, informs first in-human dosing, formulation design, dosing in special populations, and predicts the likelihood of DDIs.
• Clinical Pharmacology – Accounting for about 50% of a drug label, clinical pharmacology approaches can reduce late-stage attrition and increase pharma R&D productivity. Expertise in this discipline allows drug developers to reduce uncertainty related to therapeutic targets, dosing, and the patient populations in which the novel compound may have the most efficacy.
• Quantitative Systems Pharmacology (QSP) – This emerging mechanistic modeling approach focuses on target exposure, binding and expression. It is employed to identify biological pathways and disease determinants.
• Quantitative Systems Toxicology (QST) –  QST modeling combines toxicity and ‘omics’ data to focus on modes of action and adverse outcome pathways.
• Model-based Meta-analysis (MBMA) – Proprietary, curated databases of publicly-available trial information are used to develop models that compare a drug’s effectiveness against competitor products, optimize clinical trials, scale from biomarker to endpoint and inform marketing decisions.
• Strategic Regulatory Writing and Communications – A rigorous, quality-driven process of regulatory documentation and communications support is employed from discovery through life-cycle management.

You should now have a better understanding of what gap analysis is and how it can benefit your drug program. To learn more about using a strategic, programmatic approach to drug development, please watch this webinar by my colleagues, Drs. Craig Rayner and Patrick Smith.

[1] Zineh, et al. “Improving the Tools of Clinical Pharmacology: Goals for 2017 and Beyond,” Clinical Pharmacology and  Therapeutics , January 2017

[2] FDA Office of Clinical Pharmacology, Manual of Policies and Procedures, Good Review Practices: Clinical Pharmacology Review of New Molecular Entity (NME), New Drug Applications (NDA), and Original Biologics License Applications (BLAs), September 2016

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What is Gap Analysis in Healthcare?

As the healthcare industry evolves, quality of care is a top priority for both patients and providers. However, despite advancements in medicine and technology, gaps in care continue to exist. To address these gaps, healthcare providers can use gap analysis as a tool to identify areas of improvement and implement targeted solutions. 

What is Gap Analysis?

Gap analysis is the process of identifying gaps between current and desired performance in healthcare delivery, and developing strategies to bridge those gaps. This involves examining data to determine where gaps in care exist, and then developing targeted interventions to improve the quality of care. 

The Purpose of Gap Analysis in Healthcare:

The purpose of gap analysis is to improve the quality of care and patient outcomes. By identifying gaps in care, healthcare providers can develop strategies to close those gaps and improve the quality of care. This can lead to better patient outcomes, increased patient satisfaction, and improved financial performance for healthcare organizations. 

How to Conduct a Gap Analysis in Healthcare:

Examples of Gaps in Healthcare:

Some examples of gaps in healthcare include: 

Disparities in healthcare access and outcomes among different populations.

Variation in care delivery for a specific disease or treatment.

Lack of adherence to clinical guidelines or best practices.

Inefficient communication and coordination between healthcare providers.

Incomplete documentation and lack of data sharing among healthcare providers.

Benefits of Gap Analysis in Healthcare:

Gap analysis can provide several benefits to healthcare providers, including: 

Improved quality of care and patient outcomes.

Increased patient satisfaction and loyalty. 

Reduced healthcare costs and improved financial performance.

Enhanced communication and collaboration among healthcare providers.

Improved adherence to clinical guidelines and best practices 

Types of Gap Analysis in Healthcare:

There are several types of gap analysis in healthcare, including: 

Clinical gap analysis: Identifying gaps in clinical processes and practices to improve patient outcomes.

Operational gap analysis: Identifying gaps in operational processes and practices to improve efficiency and reduce costs. 

Compliance gap analysis: Identifying gaps in compliance with regulatory requirements and standards. 

Quality gap analysis: Identifying gaps in quality of care and patient satisfaction. 

To summarize, gap analysis is a powerful tool for improving the quality of care in healthcare. By identifying gaps in care, healthcare providers can develop targeted interventions to improve patient outcomes, increase patient satisfaction, and enhance financial performance. With the implementation of gap analysis, healthcare organizations can make strides toward providing equitable, high-quality care to all patients. 

Syra Health is a leading provider of intelligent gap analysis services in healthcare. Their solution utilizes artificial intelligence and machine learning to analyze data and identify gaps in care. 

With their solution, Syra Health can provide targeted interventions to improve the quality of care and patient outcomes. Their platform is designed to work with a wide range of data sources, including electronic health records (EHRs), claims data, and patient feedback. 

Syra Health's intelligent gap analysis services can help healthcare organizations improve their performance in several areas, such as reducing readmissions, improving medication adherence, and enhancing clinical decision-making. 

By leveraging the power of AI and machine learning, Syra Health is helping healthcare organizations close the gaps in care and improve the quality of care for all patients. 

Gap Analysis in Healthcare FAQ’s:-

What is a healthcare gap analysis?  

A healthcare gap analysis is a process of identifying areas where healthcare services fall short of expectations or standards. It involves identifying gaps between the actual healthcare services provided and the healthcare services that should be provided based on established standards and best practices. 

What is an example of gaps in healthcare?  

An example of a gap in healthcare could be the lack of access to healthcare services in rural or underserved areas, leading to a disparity in healthcare outcomes. Another example could be the failure to adhere to recommended healthcare guidelines, leading to suboptimal patient outcomes. 

What are the types of gap analysis in healthcare? 

The types of gap analysis in healthcare include clinical gap analysis, operational gap analysis, financial gap analysis, and regulatory gap analysis. Each type of gap analysis focuses on a different aspect of healthcare, such as clinical quality, operational efficiency, financial performance, or compliance with regulatory requirements. 

What is a gap analysis in nursing?  

A gap analysis in nursing is a process of identifying gaps in nursing practice or patient care. It involves identifying areas where nursing practices fall short of established standards or best practices, and developing plans to address these gaps and improve patient outcomes. 

How do you conduct a gap analysis in health and safety?  

To conduct a gap analysis in health and safety, you need to identify the health and safety standards or regulations that apply to your organization or industry, and compare your organization's current health and safety practices with these standards or regulations. You can then identify areas where your organization falls short of the established standards, and develop plans to address these gaps and improve health and safety outcomes. 

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How Identifying Gaps in Clinical Data Can Ease the Transition to New Regulations

• Does the clinical data cover the stated device lifetime?
• Are there sufficient data that support the full intended use(s) of the device?
• Does the clinical data support device claims/benefits?
• Do the data appraisal results satisfy requirements?
• Are the data from studies conducted in compliance with current quality and ethical standards such as, Good Clinical Practices (GCP), EN ISO 14155, and the Declaration of Helsinki?
• The general methods and procedures of the PMCF to be applied, such as gathering of clinical experience gained, feedback from users, screening of scientific literature and of other sources of clinical data;
• The specific methods and procedures of PMCF to be applied, such as evaluation of suitable registers or PMCF studies;
• A rationale for the appropriateness of the methods and procedures
• A reference to the relevant parts of the clinical evaluation report and to the risk management
• The specific objectives to be addressed by the PMCF;
• An evaluation of the clinical data relating to equivalent or similar devices;
• Reference to any relevant harmonized standards when used by the manufacturer, and relevant guidance on PMCF; and
• A detailed and adequately justified time schedule for PMCF activities (e.g. analysis of PMCF data and reporting) to be undertaken by the manufacturer.”
• Innovation, e.g., where device design, the materials, substances, principles of operation, technology or the medical indications are novel;
• Significant changes to the product or to its intended use for which premarket clinical evaluation and re-certification has been completed;
• High product related risk based on design, materials, components, invasiveness, clinical procedures;
• High risk anatomical locations;
• High risk target populations e.g. pediatrics, elderly;
• Severity of disease/treatment challenges;
• Questions of ability to generalize clinical investigation results;
• Unanswered questions of long-term safety and performance;
• Results from any previous clinical investigation, including adverse events or from post-market surveillance activities;
• Verification of safety and device performance when exposed to a larger and more varied clinical user population;
• Emergence of new information on safety or performance;
• Where CE marking was based on equivalence.
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Identifying Research Gaps and Prioritizing Psychological Health Evidence Synthesis Needs

Susanne hempel.

* RAND Corporation, Evidence-based Practice Center (EPC), Santa Monica

† University of Southern California, Keck School of Medicine, Los Angeles, CA

Kristie Gore

‡ RAND, National Security Research Division, Arlington

Bradley Belsher

§ Defense Health Agency, Psychological Health Center of Excellence (PHCoE), Falls Church, VA

Associated Data

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website, www.lww-medicalcare.com .

Supplemental Digital Content is available in the text.

Background:

Evidence synthesis is key in promoting evidence-based health care, but it is resource-intense. Methods are needed to identify and prioritize evidence synthesis needs within health care systems. We describe a collaboration between an agency charged with facilitating the implementation of evidence-based research and practices across the Military Health System and a research center specializing in evidence synthesis.

Scoping searches targeted 15 sources, including the Veterans Affairs/Department of Defense Guidelines and National Defense Authorization Acts. We screened for evidence gaps in psychological health management approaches relevant to the target population. We translated gaps into potential topics for evidence maps and/or systematic reviews. Gaps amenable to evidence synthesis format provided the basis for stakeholder input. Stakeholders rated topics for their potential to inform psychological health care in the military health system. Feasibility scans determined whether topics were ready to be pursued, that is, sufficient literature exists, and duplicative efforts are avoided.

We identified 58 intervention, 9 diagnostics, 12 outcome, 19 population, and 24 health services evidence synthesis gaps. Areas included: posttraumatic stress disorder (PTSD) (19), suicide prevention (14), depression (9), bipolar disorder (9), substance use (24), traumatic brain injury (20), anxiety (1), and cross-cutting (14) synthesis topics. Stakeholder input helped prioritize 19 potential PTSD topics and 22 other psychological health topics. To date, 46 topics have undergone feasibility scans. We document lessons learned across clinical topics and research methods.

Conclusion:

We describe a transparent and structured approach to evidence synthesis topic selection for a health care system using scoping searches, translation into evidence synthesis format, stakeholder input, and feasibility scans.

Evidence synthesis is an essential step in promoting evidence-based medicine across health systems; it facilitates the translation of research to practice. A systematic review of the research literature on focused review questions is a key evidence synthesis approach that can inform practice and policy decisions. 1 However, systematic reviews are resource-intense undertakings. In a resource-constrained environment, before an evidence review is commissioned, the need and the feasibility of the review must be established.

Establishing the need for the review can be achieved through a research gap analysis or needs assessment. Identification of a gap serves as the first step in developing a new research question. 2 Research gaps in health care do not necessarily align directly with research needs. Research gaps are only critical where knowledge gaps substantially inhibit the decision-making ability of stakeholders such as patients, health care providers, and policymakers, thus creating a need to fill the knowledge gap. Evidence synthesis enables the assessment of whether a research gap continues to exist or whether there is adequate evidence to close the knowledge gap.

Furthermore, a gap analysis often identifies multiple, competing gaps that are worthwhile to be pursued. Given the resource requirements of formal evidence reviews, topic prioritization is needed to best allocate resources to those areas deemed the most relevant for the health system. Regardless of the topic, the prioritization process is likely to be stakeholder-dependent. Priorities for evidence synthesis will vary depending on the mission of the health care system and the local needs of the health care stakeholders. A process of stakeholder input is an important mechanism to ensure that the evidence review will meet local needs as well to identify a receptive audience of the review findings.

In addition to establishing the need for an evidence review, the feasibility of conducting the review must also be established. In conducting primary research, feasibility is often mainly a question of available resources. For evidence reviews, the resources, the availability of primary research, and the presence of existing evidence reviews on the topic need to be explored. Not all topics are amenable for a systematic review which focus on a specific range of research questions and rely heavily on published literature. Furthermore, evidence review synthesizes the existing evidence; hence, if there is insufficient evidence in the primary research literature, an evidence review is not useful. Establishing a lack of evidence is a worthwhile exercise since it identifies the need for further research. However, most health care delivery organizations will be keen to prioritize areas that can be synthesized, that is, investing in synthesizing a body of research sizable enough to derive meaningful results. For evidence reviews, the presence of existing evidence syntheses is also an important consideration, in particular, to determine the incremental validity of a new review. Although primary research benefits profoundly by replication, secondary literature, in particular in the context of existing high-quality reviews and/or limited evidence, may not add anything to our knowledge base. 3

This work describes a structured and transparent approach to identify and prioritize areas of psychological health that are important and that can be feasibly addressed by a synthesis of the research literature. It describes a collaboration between an agency charged with facilitating the implementation of evidence-based research and practices across the Military Health System (MHS) and a research center specializing in evidence synthesis.

This project is anchored in the relationship between the Defense Health Agency Psychological Health Center of Excellence (PHCoE) and the RAND Corporation’s National Defense Research Institute (NDRI), one of the Federally Funded Research and Development Centers (FFRDC) dedicated to providing long-term analytic support to the Defense Health Agency. PHCoE, an agency charged with facilitating the implementation of evidence-based research and practices across the Military Health System funded a series of systematic reviews and evidence maps synthesizing psychological research. The project draws on the expertise of the Southern California Evidence-based Practice Center (EPC) located at RAND, a center specializing in evidence synthesis. The project included scoping searches, stakeholder input, and feasibility scans. The project is ongoing; this manuscript describes methods and results from June 2016 to September 2018. The project was assessed by our Human Subject Protection staff and determined to be exempt (date July 7, 2016, ID ND3621; August 6, 2017, ID ND3714).

The following describes the process, Figure ​ Figure1 1 provides a visual overview.

Process of identifying research gaps and prioritizing psychological health evidence synthesis needs.

Scoping Searches to Identify Evidence Synthesis Gaps

Scoping searches targeted pertinent sources for evidence gaps. The searches focused on clinical conditions and interventions relevant to psychological health, including biological psychiatry, health care services research, and mental health comorbidity. Proposed topics and study populations were not limited by deployment status or deployment eligibility, but the topic section considered the prevalence of clinical conditions among Department of Defense active duty military personnel managed by the MHS. The scoping searches excluded evidence gaps addressing children and adolescents and clinical conditions exclusively relevant to veterans managed by the Department of Veterans Affairs.

Scoping Search Sources

We screened 15 sources in total for evidence synthesis gaps.

Veterans Affairs/Department of Defense clinical practice guidelines were a key source for documented evidence gaps. 4 – 9 Recently updated guidelines were screened only for evidence gaps that indicated a lack of synthesis of existing research or content areas that were outside the scope of that guideline (guidelines rely primarily on published systematic reviews and can only review a limited number of topic areas).

We consulted the current report of the committee on armed services of the House of Representatives regarding the proposed National Defense Authorization Act (NDAA) and the report for the upcoming fiscal year. 10 , 11 We specifically screened the report for research priorities identified for psychological health. We also screened the published National Research Action Plan designed to improve access to mental health services for veterans, service members, and military families. 12

We conducted a literature search for publications dedicated to identifying evidence gaps and research needs for psychological health and traumatic brain injury. We searched for publications published since 2000–2016 in the most relevant databases, PubMed and PsycINFO, that had the words research gap, knowledge gap, or research priority in the title and addressed psychological health (Supplemental Digital Content, http://links.lww.com/MLR/B836 ). The search retrieved 203 citations. Six publications were considered potentially relevant and obtained as full text, 1 source was subsequently excluded because the authors conducted a literature search <3 years ago and it was deemed unlikely that a new review would identify substantially more eligible studies. 13 – 19

We also used an analysis of the utilization of complementary and alternative medicine in the MHS 20 to identify interventions that were popular with patients but for which potentially little evidence-based guidance exists. We focused our scoping efforts on complementary approaches such as stress management, hypnotherapy, massage, biofeedback, chiropractic, and music therapy to align with the funding scope. In the next step, we reviewed the existing clinical practice guidelines to determine whether clinicians have guidance regarding these approaches. The Department of Defense Health Related Behaviors Survey of Active Duty Military Personnel 21 is an anonymous survey conducted every 3 years on service members with the aim of identifying interventions or health behaviors patients currently use. To address evidence gaps most relevant to patients, we screened the survey results, and then matched the more prevalent needs identified with guidance provided in relevant clinical practice guidelines.

We consulted the priority review list assembled by the Cochrane group to identify research needs for systematic reviews. We screened the 2015–2017 lists for mental health topics that are open to new authors, that is, those that do not have an author team currently dedicated to the topic. None of the currently available topics appeared relevant to psychological health and no topics were added to the table. We also consulted with ongoing federally funded projects to identify evidence gaps that were beyond the scope of the other projects. In addition, we screened a list of psychological health research priorities developed at PHCoE for knowledge gaps that could be addressed in systematic reviews or evidence maps. Finally, we screened resources available on MHS web sites for evidence gaps.

Gap Analysis Procedure and Approach to Translating Gaps into Evidence Review Format

We first screened these sources for knowledge gaps, regardless of considerations of whether the gap is amenable to evidence review. However, we did not include research gaps where the source explicitly indicated that the knowledge gap is due to the lack of primary research. We distinguished 5 evidence gap domains and abstracted gaps across pertinent areas: interventions or diagnostic questions, treatment outcomes or specific populations, and health services research and health care delivery models.

We then translated the evidence gaps into potential topics for evidence maps and/or systematic reviews. Evidence maps provide a broad overview of large research areas using data visualizations to document the presence and absence of evidence. 22 Similar to scoping reviews, evidence maps do not necessarily address the effects of interventions but can be broader in scope. Systematic reviews are a standardized research methodology designed to answer clinical and policy questions with published research using meta-analysis to estimate effect sizes and formal grading of the quality of evidence. We considered systematic reviews for effectiveness and comparative effectiveness questions regarding specific intervention and diagnostic approaches.

Stakeholder Input

Evidence synthesis gaps that were determined to be amenable to systematic review or evidence map methods provided the basis for stakeholder input. Although all topics were reviewed by project personnel, we also identified psychological health service leads for Army, Navy, Air Force, and Marines within the Defense Health Agency as key stakeholders to be included in the topic selection process. To date, 2 rounds of formal ratings by stakeholders have been undertaken.

The first round focused on the need for systematic review covering issues related to posttraumatic stress disorder (PTSD). The second round focused on other potential psychological health topics determined to be compatible with the MHS mission. Represented clinical areas were suicide prevention and aftercare, depressive disorders, anxiety disorders, traumatic brain injury, substance use disorder including alcohol and opioid use disorder, and chronic pain. All of the potential topics addressed either the effects of clinical interventions or health service research questions.

Stakeholders rated the topics based on their potential to inform psychological health care in the military health system. The raters used a scale 5-point rating scale ranging from “No impact” to “Very high impact.” In addition, stakeholders were able to add additional suggestions for evidence review. We analyzed the mean, the mode, and individual stakeholder rating indicating “high impact” for individual topics.

Feasibility Scans

Feasibility scans provided an estimate of the volume and the type of existing research literature which is informative for 3 reasons. First, this process determined whether sufficient research was available to inform a systematic review or an evidence map. Second, feasibility scans can provide an estimate of the required resources for an evidence review by establishing whether only a small literature base or a large number of research studies exists. Finally, feasibility scans identify existing high-profile evidence synthesis reports that could make a new synthesis obsolete.

Feasibility scans for potential evidence maps concentrated on the size of the body of research that would need to be screened and the relevant synthesis questions that can inform how this research should be organized in the evidence map. Feasibility scans for systematic reviews aimed to determine the number of relevant studies, existing high-quality reviews, and the number of studies not covered in existing reviews. Randomized controlled trials (RCTs) are the focus of most of the systematic review topics, that is, strong research evidence that could inform clinical practice guideline committees to recommend either for or against interventions. An experienced systematic reviewer used PubMed, a very well-maintained and user-friendly database for biomedical literature, developed preliminary search strategies, and applied database search filters (eg, for RCTs or systematic reviews) in preliminary literature searches to estimate the research volume for each topic.

Scans also identified any existing high-quality evidence review published by agencies specializing in unbiased evidence syntheses such as the Agency for Healthcare Research and Quality (AHRQ)’s Evidence-based Practice Center program, the Cochrane Collaboration, the Campbell Collaboration, the Evidence Synthesis Program of the Department of Veterans Affairs, and the Federal Health Technology Assessment program. We used the databases PubMed and PubMed Health to identify reports. We appraised the scope, relevance and publication year of the existing high-profile evidence reviews. The research base for psychological health develops rapidly and evidence syntheses need to ensure that current clinical policies reflect the best available evidence. When determining the feasibility and appropriateness of a new systematic review, we took the results of the original review and any new studies that had been published subsequent to the systematic review on the same topic into account.

The following results are described: the results of the scoping searches and gap analysis, the translation of gaps into evidence synthesis format, the stakeholder input ratings, and the feasibility scans.

Scoping Searches and Gap Analysis Results

The scoping search and gap analysis identified a large number of evidence gaps as documented in the gap analysis table in the Appendix (Supplemental Digital Content, http://links.lww.com/MLR/B836 ). Across sources, we identified 58 intervention, 9 diagnostics, 12 outcome, 19 population, and 24 health services evidence synthesis gaps. The evidence gaps varied considerably with regard to scope and specificity, for example, highlighting knowledge gaps in recommendations for medications for specific clinical indications or treatment combinations 4 to pointing out to gaps in supporting caregivers. 11 The largest group of evidence gaps were documented for interventions. This included open questions for individual interventions (eg, ketamine) 12 as well as the best format and modality within an intervention domain (eg, use of telehealth). 6 Diagnostic evidence gaps included open questions regarding predictive risk factors that could be used in suicide prevention 8 and the need for personalized treatments. 12 Outcome evidence gaps often pointed to the lack of measured outcomes to include cost-effectiveness as well as the lack of knowledge on hypothesized effects, such as increased access or decreased stigma associated with technology-based modalities. 23 Population evidence gaps addressed specific patient populations such as complex patients 5 and family members of service members. 11 The health services evidence gaps addressed care support through technology (eg, videoconferencing 23 ) as well as treatment coordination within health care organizations such as how treatment for substance use disorder should be coordinated with treatment for co-occurring conditions. 4

Potential Evidence Synthesis Topics

The gaps were translated into potential evidence map or systematic review topics. This translation process took into account that some topics cannot easily be operationalized as an evidence review. For example, knowledge gaps regarding prevalence or utilization estimates were hindered by the lack of publicly available data. In addition, we noted that some review questions may require an exhaustive search and a full-text review of the literature because the information cannot be searched for directly, and hence were outside the budget restraints.

The clinical areas and number of topics were: PTSD (n=19), suicide prevention (n=14), depression (n=9), bipolar disorder (n=9), substance use (n=24), traumatic brain injury (n=20), anxiety (n=1), and cross-cutting (n=14) evidence synthesis topics. All topic areas are documented in the Appendix (Supplemental Digital Content, http://links.lww.com/MLR/B836 ).

Stakeholder Input Results

Stakeholders rated 19 PTSD-related research gaps and suggested an additional 5 topics for evidence review, addressing both preventions as well as treatment topics. Mean ratings for topics ranged from 1.75 to 3.5 on a scale from 0 (no impact potential) to 4 (high potential for impact). Thus, although identified as research gaps, the potential of an evidence review to have an important impact on the MHS varied across the topics. Only 2 topics received a mean score of ≥3 (high potential), including predictors of PTSD treatment retention and response and PTSD treatment dosing, duration, and sequencing . In addition, raters’ opinions varied considerably across some topics with SDs ranging from 0.5 to 1.5 across all topics.

The stakeholders rated 22 other psychological health topics, suggested 2 additional topics for evidence review, and revised 2 original topics indicating which aspect of the research gap would be most important to address. Mean scores for the rated topics ranged from 0.25 to 3.75, with the SDs for each item ranging from 0 to 1.4. Six topics received an average score of ≥3, primarily focused on the topics of suicide prevention, substance use disorders, and telehealth interventions. Opinions on other topics varied widely across service leads.

Feasibility Scan Results

Evidence review topics that were rated by stakeholders as having some potential for impact (using a rating cutoff score>1) within the MHS were selected for formal feasibility scans. To date, 46 topics have been subjected to feasibility scans. Of these, 11 were evaluated as potential evidence map, 17 as a systematic review, and 18 as either at the time of the topic suggestion. The results of the feasibility scans are documented in the table in the Appendix (Supplemental Digital Content, http://links.lww.com/MLR/B836 ).

The feasibility scan result table shows the topic, topic modification suggestions based on literature reviews, and the mean stakeholder impact rating. The table shows the employed search strategy to determine the feasibility; the estimated number of RCTs in the database PubMed; the number and citation of Cochrane, Evidence Synthesis Program, and Health Technology Assessment reviews, that is, high-quality syntheses; and the estimated number of RCTs published after the latest existing systematic review that had been published on the topic.

Each potential evidence review topic was discussed in a narrative review report that documented the reason for determining the topic to be feasible or not feasible. Reasons for determining the topic to be not feasible included the lack of primary research for an evidence map or systematic review, the presence of an ongoing research project that may influence the evidence review scope, and the presence of an existing high-quality evidence review. Some topics were shown to be feasible upon further modification; this included topics that were partially addressed in existing reviews or topics where the review scope would need to be substantially changed to result in a high-impact evidence review. Topics to be judged feasible met all outlined criteria, that is, the topic could be addressed in a systematic review or evidence map, there were sufficient studies to justify a review, and the review would not merely replicate an existing review but make a novel contribution to the evidence base.

The project describes a transparent and structured approach to identify and prioritize evidence synthesis topics using scoping reviews, stakeholder input, and feasibility scans.

The work demonstrates an approach to establishing and evaluating evidence synthesis gaps. It has been repeatedly noted that research gap analyses often lack transparency with little information on analytic criteria and selection processes. 24 , 25 In addition, research need identification may not be informed by systematic literature searches documenting gaps but primarily rely on often unstructured content expert input. 26 , 27 Evidence synthesis needs assessment is a new field that to date has received very little attention. However, as health care delivery organizations move towards providing evidence-based treatments and the existing research continue to grow, both evidence reviews and evidence review gap identification and prioritization will become more prominent.

One of the lessons learned is that the topic selection process added to the timeline and required additional resources. The scoping searches, translation into evidence synthesis topics, stakeholder input, and feasibility scans each added time and the project required a longer period of performance compared to previous evidence synthesis projects. The project components were undertaken sequentially and had to be divided into topic areas. For example, it was deemed too much to ask for stakeholder input for all 122 topics identified as potential evidence review topics. Furthermore, we needed to be flexible to be able to respond to unanticipated congressional requests for evidence reviews. However, our process of identifying synthesis gaps, checking whether topics can be translated into syntheses, obtaining stakeholder input to ensure that the gaps are meaningful and need filling, and estimating the feasibility and avoiding duplicative efforts, has merit considering the alternative. More targeted funding of evidence syntheses ensures relevance and while resources need to be spent on the steps we are describing, these are small investments compared to the resources required for a full systematic review or evidence map.

The documented stakeholder engagement approach was useful for many reasons, not just for ensuring that the selection of evidence synthesis topics was transparent and structured. The stakeholders were alerted to the evidence synthesis project and provided input for further topic refinement. This process also supported the identification of a ‘customer’ after the review was completed, that is, a stakeholder who is keen on using the evidence review is likely to take action on its results and ready to translate the findings into clinical practice. The research to practice gap is substantial and the challenges of translating research to practice are widely documented. 28 – 30 Inefficient research translation delays delivery of proven clinical practices and can lead to wasteful research and practice investments.

The project had several strengths and limitations. The project describes a successful, transparent, and structured process to engage stakeholders and identifies important and feasible evidence review topics. However, the approach was developed to address the specific military psychological health care system needs, and therefore the process may not be generalizable to all other health care delivery organizations. Source selection was tailored to psychological health synthesis needs and process modifications (ie, sources to identify gaps) are needed for organizations aiming to establish a similar procedure. To keep the approach manageable, feasibility scans used only 1 database and we developed only preliminary, not comprehensive searches. Hence, some uncertainty about the true evidence base for the different topics remained; feasibility scans can only estimate the available research. Furthermore, the selected stakeholders were limited to a small number of service leads. A broader panel of stakeholders would have likely provided additional input. In addition, all evaluations of the literature relied on the expertise of experienced systematic reviewers; any replication of the process will require some staff with expertise in the evidence review. Finally, as outlined, all described processes added to the project timeline compounding the challenges of providing timely systematic reviews for practitioners and policymakers. 31 , 32

We have described a transparent and structured approach to identify and prioritize areas of evidence synthesis for a health care system. Scoping searches and feasibility scans identified gaps in the literature that would benefit from evidence review. Stakeholder input helped ensure the relevance of review topics and created a receptive audience for targeted evidence synthesis. The approach aims to advance the field of evidence synthesis needs assessment.

Supplementary Material

Acknowledgments.

The authors thank Laura Raaen, Margaret Maglione, Gulrez Azhar, Margie Danz, and Thomas Concannon for content input and Aneesa Motala and Naemma Golshan for administrative assistance.

Supported by the Office of the Secretary of Defense, Psychological Health Center of Excellence. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the Psychological Health Center of Excellence, the Office of the Secretary of Defense, or the United States government.

The authors declare no conflict of interest.

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What Is a Healthcare Gap Analysis (or Gap Assessment)?

By Elizabeth Snively , on December 8, 2022

A gap analysis , or gap assessment , is a staple tool of business management that applies to all types of businesses, including healthcare organizations. The “gap” refers to the difference between business goals and actual performance. Without a gap analysis, it can be difficult to identify the specific problems that are causing your organization to fall short of a goal. Perhaps more importantly, healthcare gap analyses can help you pinpoint the areas that will yield the greatest improvement.

Forbes defines four types of gap analyses: strategic, skills, market , and profit . Here, we’ll look at the first two, which are interrelated: strategic gaps and skills gaps. An understanding of these two types of gaps is fundamental to any organization and will accelerate improvement in the other two.

Hypothetical scenario: slow pandemic recovery

Imagine the following scenario. You are a mid-level leader at “Friendly Hospital,” a large healthcare organization. Friendly’s executive team has just held an internal quarterly meeting and announced that the organization is aiming to meet pre-pandemic performance benchmarks. The reality is that the organization is struggling to regain stability in several areas, and improvement will not be easy.

At Friendly, staffing is an ongoing problem. Vacancies have led to staff burnout, turnover, and lagging performance. Your organization formerly achieved improvement year after year along with high ratings, but it has been challenging since the pandemic to maintain the top-tier service levels it has been known for. How do you proceed?

A gap analysis identifies specific problems and causes

Conducting a strategic gap analysis can reveal issues affecting performance. In our example, Friendly Hospital had previously achieved and maintained a five-star HCAHPS rating for several years, both overall and in patient surveys. These excellent ratings were a point of pride for the organization.

During the pandemic, the hospital was hit hard by regional staffing and supply shortages. The use of travel nurses cut deeply into the operating budget, leading to the elimination of some administrative positions. Ratings fell to four stars.

Now, the executive team is pressing to regain the higher ratings, but leaders lack the data and strategic insight to ascertain which areas could yield the most effective and impactful improvement.

A strategic gap analysis guides targeted performance improvement

As Friendly Hospital begins its strategic gap analysis, it starts by reporting on the status of every major organizational process and the current people, tools, and resources being used for each. The key to a successful gap analysis is to quantify both the shortfall and the improvement needed.

Goals should fulfill the S.M.A.R.T. framework (specific, measurable, achievable, relevant, and time-bound). Once each process has sufficient data to show the difference between the current state and the desired state, it is easier to see where the greatest needs are and the options for moving forward.

Questions to ask for a strategic gap analysis

For a strategic gap analysis, ask the following:

• What are the major organizational processes?
• What is the status of each vs. the targeted levels?
• What tools and resources are being used and by whom for each?
• What evidence-based best practices should be used going forward?
• What barriers exist for closing the gaps?

This systematic, data-backed approach takes guesswork and speculation out of forecasting future performance. In addition to identifying specific areas or processes that need improvement, a gap analysis can also indicate how much improvement you might realistically achieve in each area. The drivers behind a gap assessment methodology are more than economic or performance-based, however.

“Conducting gap analyses is a best practice for identifying, reducing, or mitigating risk to your organization. Results from an analysis can systematically uncover where the greatest gaps are occurring — which oftentimes can be the areas of greatest risk, whether they are in patient safety, care quality, patient satisfaction, organizational reputation, or other major pain point.” — John Harrington, CMI, Director of Clinical Solutions at Relias.

For example, one study documented how Johns Hopkins Medicine’s Armstrong Institute for Patient Safety and Quality used a gap analysis to evaluate their current state of inpatient diabetes care. Clinicians collected process data and found inconsistencies that resulted in measurable gaps. They later addressed these with new policies, improved infrastructure, education, automation, and standardization.

Using results to target improvement

In our scenario, Friendly Hospital leaders conducted a gap analysis of its major operational processes. Despite less-than-optimal staffing projections for the coming year, leaders concluded that areas such as patient safety and compliance were on track with only minor variances from targets.

However, the analysis also showed that Friendly’s goal of achieving higher patient survey ratings had previously depended on a small but well-trained team of patient care coordinators. Because of recent budget pressures, the hospital had eliminated most of these positions, resulting in longer wait times, patient processing problems, and higher numbers of patient complaints.

Based on the results, hospital leaders decided to reinstate two of their patient care coordinator roles. In doing so, they significantly improved their level of service, provided better support for the care teams, and improved continuity among the many travel nurses, new grad nurses, and other new clinicians on staff. Without examining its major operations through a gap analysis, the hospital could have overlooked this significant win with system-wide impact.

A skills gap analysis helps evaluate and improve staff competencies

A skills gap analysis for healthcare looks specifically at the skills of your workforce. It differs from a strategic gap analysis, which looks at processes and services. Analyzing the skills of your staff is a critical part of performance management because a highly competent staff enables your organization to deliver on its performance targets to ultimately ensure the best patient outcomes.

McKinsey and Company reported that using skills gap assessments can boost productivity by as much as 40% and raise employee engagement by 50%. That much improvement is possible because most organizations have a poor understanding of their existing skills base and needs.

Like a strategic gap analysis, a skills gap analysis begins with a thorough assessment of your existing state. You will assess employees’ existing skills against the competencies needed for high performance to find where gaps exist. Because employee roles can be complex, McKinsey recommends defining each role and the associated skill taxonomies that align with them. Some skills apply across roles, and some apply only to specific roles. Ideally, your organization’s learning experience platform will provide this functionality as part of a robust staff assessment solution .

By analyzing the competencies of your staff, you can both improve individual performance and identify missing skills within teams . Competency assessment is also critical for compliance with regulatory requirements from accrediting bodies such as The Joint Commission and OSHA.

Questions to ask for a skills gap analysis

For a skills gap analysis, ask the following:

• What is your organizational process for evaluating skills and competencies during recruitment and the pre-hire stage ?
• How does your organization evaluate and prepare new hires to ensure readiness to practice ?
• How does your organization administer and assess required training and certifications ?
• How does your organization develop its staff to foster employee growth and provide opportunities for career advancement through continuing education and professional development ?
• What are the current skill levels vs. desired benchmarks for the full range of staff competencies?
• What educational initiatives can close the gaps?

Having good data to assess your staff’s current competencies across skill sets is the foundation for achieving improvement in targeted areas. Assessing the knowledge, skills, and abilities of staff members — both individuals and teams — can illuminate where they are and where they need to be.

A skills gap analysis drives upskilling and reskilling

The ability to assess staff competencies thoroughly and accurately gives organizations an advantage in a difficult labor market. International business educator Emeritus suggests that optimizing your existing staff is more important than ever when finding and hiring new talent is a challenge.

In addition to knowing what may be missing from your staff members’ individual learning plans to do their jobs better, you should also identify opportunities for reskilling and upskilling .

• Reskilling is the practice of identifying and developing staff members who have the potential to take on different responsibilities where a gap may exist within your organization. Employees may already have a skill set or partial skill set that your team could leverage with additional training.
• Upskilling enables employees to advance by expanding their skill sets with competencies that fulfill needs within your organization.

Being proactive and innovative with your talent not only benefits your organization in the short term, it could also increase retention, save money, provide recruiting incentives, promote a culture of learning, and raise the quality of your teams overall. Your staff will benefit as they advance in their careers, become more versatile, and gain in confidence and morale.

Undertake a skills gap analysis on a regular basis by building it into your workforce development process and employee performance plans. By doing so, you’ll operationalize the gains from this systemic approach and optimize the most important resource you have — your people.

Elizabeth Snively

Content Marketing Manager, Relias

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Gap analysis: synergies and opportunities for effective nursing leadership

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Gap analysis encompasses a comprehensive process to identify, understand, address, and bridge gaps in service delivery and nursing practice. onducting gap analysis provides structure to information gathering and the process of finding sustainable solutions to important deficiencies. Nursing leaders need to recognize, measure, monitor, and execute on feasible actionable solutions to help organizations make adjustments to address gaps between what is desired and the actual real-world conditions contributing to the quality chasm in health care. Gap analysis represents a functional and comprehensive tool to address organizational deficiencies. Using gap analysis proactively helps organizations map out and sustain corrective efforts to close the quality chasm. Gaining facility in gap analysis should help the nursing profession's contribution to narrowing the quality chasm.

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Methods: mind the gap, webinar series, implications of informative cluster size for the design and analysis of cluster randomized trials.

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Cluster randomized trials involve randomizing groups of participants, such as schools, hospitals, or villages, between different interventions. Because participants belonging to the same cluster are often correlated, statistical methods, such as mixed-effects models or generalized estimating equations, are required to account for this correlation during analysis. However, it is being increasingly recognized that these methods may be biased when outcomes or treatment effects differ between larger and smaller clusters (i.e., informative cluster size). This talk focuses on the implications of informative cluster size for cluster randomized trials, including choice of estimand, choice of analysis method, and ways to evaluate assumptions. 

About Brennan Kahan

Dr. Brennan Kahan is a Principal Research Fellow at the Medical Research Council Clinical Trials Unit at  University College London . Dr. Kahan’s research program is focused on developing methods to improve the design, analysis, and reporting of randomized trials. His major research interests include using estimands to improve the interpretability and validity of randomized trials; evaluating statistical methods for complex trial designs, such as cluster, non-inferiority, factorial, and rerandomization trials; and  improving the reporting of randomized trials. His recent methods research includes the development of the Consolidated Standards of Reporting Trials extension for factorial trials, developing estimands and robust estimators for cluster randomized trials, and evaluating and comparing estimation methods for different intercurrent event strategies.

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AI-driven insights: SOMS boosts clinical trial success

05-Sep-2024 - Last updated on 05-Sep-2024 at 10:58 GMT

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Kizewski delves into how SOMS is changing trial design, optimizing patient safety, and enhancing the chances of FDA approval, providing critical insights into the future of AI-driven clinical trial optimization.

Impact of Sub-population Optimization & Modeling Solution (SOMS) ​

How has SOMS transformed the landscape of clinical trials since its implementation? ​

The Sub-population Optimization & Modeling Solution (SOMS) has made a significant impact on clinical trials by enabling the identification of biomarkers associated with specific patient subgroups that can predict treatment response. This has allowed researchers to find patient subgroups with higher efficacy and identify signals indicating a patient's risk for adverse events, thus increasing trial success rates.

SOMS provides ongoing optimization by tracking subgroups throughout the trial, validating initial hypotheses, and continuously running biomarker analyses to uncover new subgroups. Even for trials that do not initially leverage SOMS, it can be implemented later, particularly in cases of slow patient recruitment or when efficacy comparisons between treatment and placebo groups yield no significant differences.

Considering the high costs and failure rates associated with clinical trials, with over a billion dollars often invested and only 12% of therapies successfully gaining FDA approval, SOMS proves invaluable in optimizing trials and controlling for variables that might otherwise go unnoticed or take too long to identify.

AI integration and functionality ​

How does the integrated AI in SOMS enhance the precision of inclusion and exclusion criteria in clinical trials? What unique AI capabilities does SOMS offer that differentiate it from other trial optimization technologies? ​

SOMS leverages AI to enhance the precision of inclusion and exclusion criteria in clinical trials. Unlike traditional methods that rely on preselected variables, SOMS analyzes multiple variables simultaneously, allowing for a comprehensive analysis of all potential patient subgroups.

The efficiency of SOMS is striking. Once the data set is prepared, SOMS can deliver results within 30 seconds for phase 1 and 2 data and within a few hours for larger phase 3 datasets. This efficiency allows continuous optimization throughout the trial lifecycle, reducing process times by up to 20x compared to traditional methods.

Using validated, open-source algorithms, SOMS offers credibility when presenting results to health authorities and flexibility in modifying criteria as needed. Its repeatability across trials within a portfolio and effectiveness across multiple therapeutic areas set it apart from other trial optimization technologies.

Rescue strategies and trial failures ​

What are the common indicators that a trial is struggling, and how does SOMS intervene with rescue strategies? Can you share a case study where SOMS successfully implemented a rescue strategy to prevent a trial from failing? ​

SOMS plays a crucial role in rescuing struggling clinical trials. Indicators of a trial in distress include unexpected adverse events, lack of efficacy in the treatment group, and patient retention issues. SOMS intervenes by quickly analyzing data and predicting which subgroups are more susceptible to these events, allowing for targeted interventions.

A real-world example involves a Phase 3 trial in multiple myeloma, where SOMS identified two biomarkers indicating increased risk of cardiac failure. This information enabled sponsors to target high-risk groups, implement safeguards, and introduce interventions to reduce cardiac failure events, ultimately preventing the trial from failing.

Identification of efficacious subgroups ​

How does SOMS identify and optimize efficacious subgroups within a trial? What methodologies or algorithms are used to ensure these subgroups are accurately identified? ​

SOMS employs a sophisticated algorithm called Subgroup Identification Based on Differential Effect Search (SIDES) to identify and optimize efficacious subgroups within a trial. This algorithm, along with its variations (basic SIDES, fixed SIDES, and adaptive SIDES), allows for high accuracy and configurability in analyzing trial data to identify patient subgroups most likely to respond positively to the treatment.

Patient safety and adverse events ​

How does SOMS pinpoint subgroups prone to adverse events, and what steps are taken to ensure patient safety? Can you discuss the impact of these safety measures on overall trial integrity and success rates? ​

While not every trial will reveal clear subgroup distinctions related to adverse events, SOMS is highly effective at identifying patterns when they exist. The impact of SOMS-driven safety measures on trial integrity and success rates depends on the specific findings for each trial, the identified risks, and how researchers act on the information provided by SOMS.

Challenges in patient recruitment ​

How does SOMS address and overcome patient recruitment challenges in clinical trials? What are the key factors that SOMS considers to enhance patient recruitment and retention? ​

SOMS addresses patient recruitment challenges by providing a targeted approach to identifying suitable candidates for clinical trials. While it may not directly improve recruitment numbers, SOMS enhances the quality and relevance of recruited patients, potentially leading to better trial outcomes and faster recruitment due to a more focused pool of candidates.

Financial and therapeutic outcomes ​

In what ways does SOMS contribute to improving both financial and therapeutic outcomes for sponsors? Can you provide data or metrics that illustrate the financial benefits of using SOMS in clinical trials? ​

SOMS contributes to improving therapeutic and financial outcomes for sponsors through its data-driven, targeted approach to trial optimization. In one case, a Phase 3 trial for an antibacterial treatment initially showed no overall treatment effect. SOMS identified a responsive subpopulation, leading to FDA approval and avoiding a late-stage failure, which can cost companies hundreds of millions of dollars.

Implementation across trial phases ​

How does SOMS support sponsors across different phases of a clinical trial, from design to closeout? Are there specific phases where SOMS is particularly impactful or beneficial? ​

SOMS offers support across all phases of clinical trials, with its impact most pronounced between phase 2 and phase 3. In phase 1, SOMS uses existing data to simulate patient responses, helping to predict outcomes. During phase 2, it identifies subgroups of interest, enhancing trial design and patient selection. The transition from phase 2 to phase 3 sees the greatest impact, as SOMS combines data from previous phases to optimize the phase 3 trial design, increasing the chances of success and regulatory approval.

SOMS can also be used post-approval for benchmarking against standards of care or other therapies, potentially securing higher reimbursement rates from insurance companies.

Success rate of FDA approvals

Given that only 12% of trials result in FDA-approved therapies, how has SOMS contributed to improving this success rate? Are there any examples where SOMS has directly influenced a trial’s path to FDA approval?

SOMS has significantly contributed to improving the success rate of clinical trials. A notable example involves a Phase 3 trial for bacterial infections where SOMS identified a subgroup of patients with a positive treatment outcome, leading to FDA approval for a therapy that initially showed no overall effect.

Future of clinical trial optimization ​

What advancements or enhancements are being developed for SOMS to further optimize clinical trials? How do you see the role of AI and advanced analytics evolving in the future of clinical trial optimization? ​

The future of clinical trial optimization, particularly with AI-driven tools like SOMS, is on the cusp of significant advancements. Upcoming enhancements aim to integrate SOMS seamlessly with other technologies, such as data management systems, enabling real-time analysis and dynamic trial management. The incorporation of SOMS into risk-based quality management tools could further optimize trial conduct and patient safety.

The evolution of algorithms is also critical to SOMS's future, with a focus on developing specialized algorithms for specific therapeutic areas, potentially leading to more nuanced and accurate insights. The broader role of AI and advanced analytics is expected to lead to more integrated, intelligent, and responsive systems, revolutionizing how new therapies are developed and brought to market.

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• Published: 08 September 2024

Association of leucine and other branched chain amino acids with clinical outcomes in malnourished inpatients: a secondary analysis of the randomized clinical trial EFFORT

• Carla Wunderle   ORCID: orcid.org/0000-0003-1720-6462 1   na1 ,
• Claudia Ciobanu 1 , 2   na1 ,
• Jacqueline Ritz 1 , 2   na1 ,
• Pascal Tribolet 1 , 3 , 4 ,
• Peter Neyer   ORCID: orcid.org/0000-0002-8682-9578 5 ,
• Luca Bernasconi 5 ,
• Zeno Stanga 6 ,
• Beat Mueller 1 , 2 &
• Philipp Schuetz 1 , 2  

European Journal of Clinical Nutrition ( 2024 ) Cite this article

Metrics details

The essential branched-chain amino acids leucine, isoleucine and valine are considered anabolic and stimulate protein synthesis in the muscles as well in the liver. They also promote muscle recovery and contribute to glucose homeostasis. Recent studies in critically ill patients have demonstrated that depletion of plasma leucine is associated with increased mortality, but data in the non-critical care setting is lacking.

This secondary analysis of the randomized controlled Effect of early nutritional support on Frailty, Functional Outcomes, and Recovery of malnourished medical inpatients Trial (EFFORT), investigated the impact of leucine, isoleucine, and valine metabolism on clinical outcomes. The primary endpoint was 180-day all-cause mortality.

Among 238 polymorbid patients with available metabolite measurements, low serum leucin levels were associated with a doubled risk of 180-day all-cause mortality in a fully adjusted regression model (adjusted HR 2.20 [95% CI 1.46–3.30], p  < 0.001). There was also an association with mortality for isoleucine (1.56 [95% CI 1.03–2.35], p  = 0.035) and valine (1.69 [95% CI 1.13–2.53], p  = 0.011). When comparing effects of nutritional support on mortality in patients with high and low levels of leucine, there was no evidence of significant differences in effectiveness of the intervention. The same was true for isoleucine and valine.

Our data suggest that depletion of leucine, isoleucine, and valine among malnourished polymorbid patients is associated with increases in long-term mortality. However, patients with low metabolite levels did not show a pronounced benefit from nutritional support. Further research should focus on the clinical effects of nutritional support in patients with depleted stores of essential branched-chain amino acids.

Clinical trial registration

clinicaltrials.gov as NCT02517476 (registered 7 August 2015).

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Data availability.

Our data will be made available to others with the publication of this manuscript, as already outlined in the primary EFFORT publication, on receipt of a letter of intention detailing the study hypothesis and statistical analysis plan. A signed data access agreement is required from all applicants. Please send requests to the principal investigator of this trial.

Tsou YL, Wang CH, Chen WS, Wu HP, Liu MH, Lin HC, et al. Combining phenylalanine and leucine levels predicts 30-day mortality in critically Ill patients better than traditional risk factors with multicenter validation. Nutrients 2023;15:649.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Holeček M. Branched-chain amino acids in health and disease: metabolism, alterations in blood plasma, and as supplements. Nutr Metab. 2018;15:33.

Article   Google Scholar  

Wang MY, Wang CH, Chen WS, Chu CM, Wu HP, Liu MH, et al. U-shape relationship between plasma leucine level and mortality in the intensive care unit. Dis Mark. 2022;2022:7389258.

Google Scholar  

Mann G, Mora S, Madu G, Adegoke OAJ. Branched-chain amino acids: catabolism in skeletal muscle and implications for muscle and whole-body metabolism. Front Physiol. 2021;2021:12

Nie C, He T, Zhang W, Zhang G, Ma X. Branched chain amino acids: beyond nutrition metabolism. Int J Mol Sci. 2018;19:954.

Article   PubMed   PubMed Central   Google Scholar  

Atherton PJ, Smith K, Etheridge T, Rankin D, Rennie MJ. Distinct anabolic signalling responses to amino acids in C2C12 skeletal muscle cells. Amino Acids. 2010;38:1533–9.

Article   CAS   PubMed   Google Scholar  

Deutz NE, Matheson EM, Matarese LE, Luo M, Baggs GE, Nelson JL, et al. Readmission and mortality in malnourished, older, hospitalized adults treated with a specialized oral nutritional supplement: a randomized clinical trial. Clin Nutr. 2016;35:18–26.

Article   PubMed   Google Scholar  

Komar B, Schwingshackl L, Hoffmann G. Effects of leucine-rich protein supplements on anthropometric parameter and muscle strength in the elderly: a systematic review and meta-analysis. J Nutr Health Aging. 2015;19:437–46.

Schuetz P, Seres D, Lobo DN, Gomes F, Kaegi-Braun N, Stanga Z. Management of disease-related malnutrition for patients being treated in hospital. Lancet 2021;398:1927–38.

Imoberdorf R, Meier R, Krebs P, Hangartner PJ, Hess B, Staubli M, et al. Prevalence of undernutrition on admission to Swiss hospitals. Clin Nutr. 2010;29:38–41.

Reisinger AC, Posch F, Hackl G, Marsche G, Sourij H, Bourgeois B, et al. Branched-chain amino acids can predict mortality in ICU sepsis patients. Nutrients 2021;13:3106.

Schuetz P, Fehr R, Baechli V, Geiser M, Deiss M, Gomes F, et al. Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial. Lancet 2019;393:2312–21.

Schuetz P, Fehr R, Baechli V, Geiser M, Gomes F, Kutz A, et al. Design and rationale of the effect of early nutritional therapy on frailty, functional outcomes and recovery of malnourished medical inpatients trial (EFFORT): a pragmatic, multicenter, randomized-controlled trial. Int J Clin Trials. 2018;5:142–50.

Wunderle C, von Arx D, Mueller SC, Bernasconi L, Neyer P, Tribolet P, et al. Association of glutamine and glutamate metabolism with mortality among patients at nutritional risk-a secondary analysis of the randomized clinical trial EFFORT. Nutrients 2024;16:222.

Wunderle C, Siegenthaler J, Seres D, Owen-Michaane M, Tribolet P, Stanga Z, et al. Adaptation of nutritional risk screening tools may better predict response to nutritional treatment: a secondary analysis of the randomized controlled trial Effect of early nutritional therapy on Frailty, Functional Outcomes, and Recovery of malnourished medical inpatients Trial (EFFORT). Am J Clin Nutr. 2024;119:800–8.

Stumpf F, Wunderle C, Ritz J, Bernasconi L, Neyer P, Tribolet P, et al. Prognostic implications of the arginine metabolism in patients at nutritional risk: A secondary analysis of the randomized EFFORT trial. Clin Nutr. 2024;43:660–73.

Ritz J, Wunderle C, Stumpf F, Laager R, Tribolet P, Neyer P, et al. Association of tryptophan pathway metabolites with mortality and effectiveness of nutritional support among patients at nutritional risk: secondary analysis of a randomized clinical trial. Front Nutr. 2024;11:1335242.

Haenggi E, Kaegi-Braun N, Wunderle C, Tribolet P, Mueller B, Stanga Z, et al. Red blood cell distribution width (RDW) - A new nutritional biomarker to assess nutritional risk and response to nutritional therapy? Clin Nutr. 2024;43:575–85.

Kaegi-Braun N, Tribolet P, Baumgartner A, Fehr R, Baechli V, Geiser M, et al. Value of handgrip strength to predict clinical outcomes and therapeutic response in malnourished medical inpatients: secondary analysis of a randomized controlled trial. Am J Clin Nutr. 2021;114:731–40.

Bounoure L, Gomes F, Stanga Z, Keller U, Meier R, Ballmer P, et al. Detection and treatment of medical inpatients with or at-risk of malnutrition: suggested procedures based on validated guidelines. Nutrition 2016;32:790–8.

Kondrup J, Rasmussen HH, Hamberg O, Stanga Z, Ad Hoc EWG. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003;22:321–36.

Weinberger KM. Einsatz von Metabolomics zur Diagnose von Stoffwechselkrankheiten. Ther Umsch. 2008;65:487–91.

Yet I, Menni C, Shin SY, Mangino M, Soranzo N, Adamski J, et al. Genetic influences on metabolite levels: a comparison across metabolomic platforms. PLoS ONE. 2016;11:e0153672.

Illig T, Gieger C, Zhai G, Romisch-Margl W, Wang-Sattler R, Prehn C, et al. A genome-wide perspective of genetic variation in human metabolism. Nat Genet. 2010;42:137–41.

Trabado S, Al-Salameh A, Croixmarie V, Masson P, Corruble E, Feve B, et al. The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. PLoS ONE. 2017;12:e0173615.

Lau C-HE, Siskos AP, Maitre L, Robinson O, Athersuch TJ, Want EJ, et al. Determinants of the urinary and serum metabolome in children from six European populations. BMC Med. 2018;16:202.

Jacob M, Lopata AL, Dasouki M, Abdel Rahman AM. Metabolomics toward personalized medicine. Mass Spectrom Rev. 2019;38:221–38.

Mahoney FI, Barthel DW. Functional evaluation: the Barthel index. Md State Med J. 1965;14:61–5.

CAS   PubMed   Google Scholar  

Liu X. Classification accuracy and cut point selection. Stat Med. 2012;31:2676–86.

Petrie A, Sabin C. Medical statistics at a glance. 4th ed. Hoboken, NJ: Wiley-Blackwell; 2020.

Holecek M. Why are branched-chain amino acids increased in starvation and diabetes? Nutrients 2020;12:3087.

Huang SS, Lin JY, Chen WS, Liu MH, Cheng CW, Cheng ML, et al. Phenylalanine- and leucine-defined metabolic types identify high mortality risk in patients with severe infection. Int J Infect Dis. 2019;85:143–9.

Chen L, Chen Y, Wang X, Li H, Zhang H, Gong J, et al. Efficacy and safety of oral branched-chain amino acid supplementation in patients undergoing interventions for hepatocellular carcinoma: a meta-analysis. Nutr J. 2015;14:67.

Teymoori F, Ahmadirad H, Jahromi MK, Mokhtari E, Farhadnejad H, Mohammadzadeh M, et al. Serum branched amino acids and the risk of all-cause mortality: a meta-analysis and systematic review. Amino Acids. 2023;55:1475–86.

Lee SY, Lee HJ, Lim JY. Effects of leucine-rich protein supplements in older adults with sarcopenia: a systematic review and meta-analysis of randomized controlled trials. Arch Gerontol Geriatr. 2022;102:104758.

Wunderle C, Gomes F, Schuetz P, Stumpf F, Austin P, Ballesteros-Pomar MD, et al. ESPEN practical guideline: Nutritional support for polymorbid medical inpatients. Clin Nutr. 2024;43:674–91.

Marik PE. Feeding critically ill patients the right ‘whey’: thinking outside of the box. A personal view. Ann Intens Care. 2015;5:51.

Marik PE. Nutritional support among medical inpatients-feed the cold (and malnourished) and starve the febrile. JAMA Netw Open. 2019;2:e1915707.

Cardenas D, Ochoa JB. A paradigm shift in clinical nutrition. Clin Nutr. 2023;42:380–3.

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Acknowledgements

We thank all the contributors to the EFFORT trial for their valuable support.

This trial was supported by grants from the Swiss National Science Foundation (PP00P3_150531), and from the Research Council of the Kantonsspital Aarau (1410.000.058 and 1410.000.044). The funders had no influence over the design and conduct of this study, the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

These authors contributed equally: Carla Wunderle, Claudia Ciobanu, Jacqueline Ritz.

Authors and Affiliations

Medical University Department, Division of General Internal and Emergency Medicine, Division of Endocrinology, Diabetes and Metabolism, Kantonsspital Aarau, Aarau, Switzerland

Carla Wunderle, Claudia Ciobanu, Jacqueline Ritz, Pascal Tribolet, Beat Mueller & Philipp Schuetz

Medical Faculty of the University of Basel, Basel, Switzerland

Claudia Ciobanu, Jacqueline Ritz, Beat Mueller & Philipp Schuetz

Department of Health Professions, Bern University of Applied Sciences, Bern, Switzerland

Pascal Tribolet

Department of Nutritional Sciences and Research Platform Active Ageing, University of Vienna, Vienna, Austria

Institute of Laboratory Medicine, Kantonsspital Aarau, Aarau, Switzerland

Peter Neyer & Luca Bernasconi

Division of Diabetes, Endocrinology, Nutritional Medicine, and Metabolism, Bern University Hospital and University of Bern, Bern, Switzerland

Zeno Stanga

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Contributions

Claudia Ciobanu, Jacqueline Ritz, Carla Wunderle, and Philipp Schuetz : conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing - original draft, writing - review & editing. Beat Mueller, Zeno Stanga, and Pascal Tribolet : conceptualization. Philipp Schuetz and the EFFORT Team : investigation. Philipp Schuetz, Zeno Stanga, and Beat Mueller : resources, supervision, project administration, funding acquisition. All authors read and approved the final version of the manuscript. All authors confirm they had full access to all data in this secondary analysis. All authors accept responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Philipp Schuetz .

Ethics declarations

Competing interests.

Philipp Schuetz and Beat Mueller reports grants from Nestlé Health Science, Thermo Fisher, bioMérieux, Abbott Nutrition and Roche Diagnostics, not related to this project. No other disclosures are reported.

Ethics approval and consent to participate

The Ethics Committee of Northwestern/Central Switzerland (EKNZ; 2014_001) approved the study protocol. All participants or their authorized representatives provided written informed consent. The trial was registered at ClinicalTrials.gov ( https://clinicaltrials.gov/ct2/show/ NCT02517476).

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Wunderle, C., Ciobanu, C., Ritz, J. et al. Association of leucine and other branched chain amino acids with clinical outcomes in malnourished inpatients: a secondary analysis of the randomized clinical trial EFFORT. Eur J Clin Nutr (2024). https://doi.org/10.1038/s41430-024-01507-8

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Received : 12 June 2024

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Accepted : 02 September 2024

Published : 08 September 2024

DOI : https://doi.org/10.1038/s41430-024-01507-8

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• Published: 02 September 2024

The effectiveness and outcomes of epidural analgesia in patients undergoing open liver resection: a propensity score matching analysis

• Isarapong Pianngarn 1   na1 ,
• Worakitti Lapisatepun 2 , 3   na1 ,
• Maytinee Kulpanun 1 ,
• Anon Chotirosniramit 2 , 3 ,
• Sunhawit Junrungsee 2 , 3 &
• Warangkana Lapisatepun 1  

BMC Anesthesiology volume  24 , Article number:  305 ( 2024 ) Cite this article

1 Altmetric

Metrics details

Open liver resection necessitates a substantial upper abdominal inverted-L incision, resulting in severe pain and compromising patient recovery. Despite the efficacy of epidural analgesia in providing adequate postoperative analgesia, the potential epidural-related adverse effects should be carefully considered. This study aims to compare the efficacy and safety of continuous epidural analgesia and intravenous analgesia in open liver resection.

A retrospective study was conducted, collecting data from patients who underwent open liver resection between 2007 and 2017. Propensity score matching was implemented to mitigate confounding variables, with patients being matched in a 1:1 ratio based on propensity scores. The primary outcome was the comparison of postoperative morphine consumption at 24, 48, and 72 hours between the two groups. Secondary outcomes included pain scores, postoperative outcomes, and epidural-related adverse effects.

A total of 612 patients were included, and after matching, there were 204 patients in each group. Opioid consumption at 24, 48, and 72 hours postoperatively was statistically lower in the epidural analgesia group compared to the intravenous analgesia group ( p  < 0.001). However, there was no significant difference in pain scores ( p  = 0.422). Additionally, perioperative hypotension requiring treatment, as well as nausea and vomiting, were significantly higher in the epidural analgesia group compared to the intravenous analgesia group ( p  < 0.001).

Conclusions

Epidural analgesia is superior to intravenous morphine in terms of reducing postoperative opioid consumption within the initial 72 h after open liver resection. Nevertheless, perioperative hypotension, which necessitates management, should be approached with consideration and vigilance.

Trial registration

The study was registered in the Clinical Trials Registry at www.clinicaltrials.gov/ , NCT number: NCT06301932.

Peer Review reports

Liver resection is the surgical procedure for treating benign and malignant liver tumors, primarily performed through an open approach with a right inverted L-shaped incision or right subcostal incision. Consequently, the upper abdominal incision is associated with severe postoperative pain and delayed patient recovery [ 1 ]. Thoracic epidural analgesia (TEA) is a conventional technique commonly used to control postoperative pain after major abdominal surgery, including open liver resection [ 2 , 3 , 4 , 5 ]. This technique provides adequate postoperative pain control, reduces narcotic use without increasing the length of stay or perioperative complications, and decreases postoperative morbidities and other serious complications [ 4 , 6 , 7 , 8 , 9 ].

However, many recent studies have focused on the safety of neuraxial anesthesia and the deleterious epidural-related adverse effects in patients undergoing liver resection due to the potential risk for postoperative coagulopathy and the likelihood of developing serious complications, including epidural hematoma [ 10 , 11 ]. Furthermore, perioperative hypotension is also a common epidural catheter-related adverse effect that may require treatment, either through a significantly greater volume of intravenous fluid administration or the use of vasoactive drugs. These concerning adverse effects might be the causes of delayed removal of the epidural catheter and delayed patient ambulation during the postoperative period, which is the key to successful and enhanced recovery after liver resection [ 12 ].

This study aims to compare the efficacy of continuous thoracic epidural analgesia and intravenous morphine administration and evaluate the safety of continuous thoracic epidural analgesia in patients who underwent open liver resection.

Study population

This study was conducted as a retrospective observational cohort study by reviewing and collecting the database of all patients aged over 18 who underwent an elective open liver resection in a tertiary hospital center. The data were extracted from the electronic medical record system between January 2007 and December 2017. The study was conducted according to the guidelines of the Declaration of Helsinki and ethically approved by the Institutional Review Board of the Faculty of Medicine, Chiang Mai University (IRB number: ANE-2562-06771, approved on December 23, 2019). Patient-informed consent was waived by the Ethics Committee due to the retrospective nature of the study, and the analysis used anonymous clinical data. A total of 654 patients were initially enrolled in this study; however, those with a failure of epidural analgesia ( n  = 33) and those without documentation of numerical rating scores ( n  = 9) were excluded, resulting in a final participant of 612 patients, as shown in Fig.  1 .

Flowchart of 612 patients undergoing open liver resection

In terms of operational definitions, open major liver resection was characterized by the removal of four or more liver segments, whereas a minor liver resection was defined as the removal of no more than four segments. These definitions adhere to the anatomical classification outlined in the Brisbane 2000 Terminology of Liver Anatomy and Resections [ 13 ]. The failure of epidural analgesia was defined as inadequate analgesia, such that the analgesic level could not be tested or there was no sensory block after adequate dosing of local anesthesia following the initial placement, resulting from catheter dislodgement or any reason for early discontinuation of the epidural catheter [ 14 ].

Data collection and outcome measurements

For each patient, demographic data, including age, body mass index (BMI), gender, American Society of Anesthesiologists (ASA) classification, and preoperative laboratory investigations, were collected. Intraoperative data, including type of liver resection, intravenous fluid administration, the incidence of hypotension, the use of vasopressors or inotropic drugs, operating time, and estimated blood loss, were also assessed. Postoperative data regarding postoperative opioid consumption, the numerical rating scale (NRS), and postoperative outcomes, including surgical and non-surgical complications and epidural-related complications, were also reported.

All eligible patients were categorized into two groups: the continuous thoracic epidural analgesia group (EA group) and the intravenous morphine group (IV-MO group). Additionally, the EA group was divided into epidural analgesia with local anesthetics with opioids (EA-O group) and epidural analgesia with local anesthetics without opioids (EA-L group). The primary outcome was to compare postoperative morphine consumption at 24, 48, and 72 hours. Moreover, the numerical rating scale at 24, 48, and 72 hours and postoperative outcomes, such as length of hospital and ICU stays, the need for ventilator support, and epidural-related complications, were also reported as secondary outcomes. Postoperative pain was assessed using the numerical rating scale, in which patients reported pain on a scale from 0 to 10 at 24, 48, and 72 hours postoperatively.

In our institute, all liver resections were performed by the hepatobiliary surgeons utilizing general anesthesia, with or without continuous epidural analgesia. Most open liver resections were performed using a standard inverted-L incision. Continuous thoracic epidural analgesia was administered to patients undergoing liver resection based on the anesthesiologist’s decision and the patient’s conditions. Patients with pre-operative coagulopathy and thrombocytopenia were considered relative contraindications for implementing epidural analgesia. Experienced anesthesiologists or senior residents performed continuous thoracic epidural analgesia under supervision at mid-thoracic level (T8-T9 or T9-T10) in awake patients. The loss of resistance technique or epidural wave form was utilized to confirm the correction of the epidural placement. Continuous thoracic epidural analgesia was not routinely used during the intraoperative period, depending on the anesthesiologist’s discretion.

Continuous epidural analgesia was usually employed after liver resection in patients without contraindications for epidural catheter insertion, with 0.1-0.125% bupivacaine with or without the addition of opioid (fentanyl 1 µg/mL) at a continuous infusion rate ranging from 5 to 12 mL/hour; however, early interruption or delayed usage of the epidural catheter was necessary in some patients who developed postoperative hypotension. When the patients experienced postoperative severe pain (NRS ≥ 7), rescue analgesia comprising 3–4 mg of intravenous morphine or 25–50 µg of intravenous fentanyl was administered to those in the EA-L group and IV-MO group. Additionally, for the patients in the EA-O group, intravenous tramadol at 50 mg was administered as rescue postoperative pain relief. The calculation of morphine milligram equivalents was used to convert the dosage of other intravenous opioids into intravenous milligram morphine equivalents [ 15 ].

Statistical analysis

For the continuous variables, the differences between the two groups were compared by using the Student t -test for normal distribution data and the Mann-Whitney U test for non-normal distribution data. Continuous data with a normal distribution were reported as the mean (± standard deviation), while non-normally distributed variables were reported as the median (interquartile range). Categorical variables were analyzed by Fisher’s exact test, which is presented as numbers with percentages.

Propensity score matching was applied to adjust confounding factors by indication and contraindication and reported according to Lonjon et al. [ 16 ] The propensity scores were calculated using a multivariable logistic regression model. The epidural analgesia group (EA group) and intravenous morphine group (IV-MO group) were matched by propensity scores that were generated for each case based on the following baseline covariates: age, co-morbidity, ASA classification, the extent of the resected liver, and pre-operative white blood cell count (WBC), prothrombin time (PT), international normalized ratio (INR), albumin, and total bilirubin (TB). These variables were used to attain the similar baseline characteristics that occurred before performing epidural analgesia and had the potential to influence either the patient’s status or the attending anesthesiologist’s decision to utilize epidural analgesia. The patients with missing data in matching variables at a rate of 4.4% and those who could not be matched were excluded from the analysis. A 1:1 nearest-neighbor match with a standard caliper width of 0.2 was performed to generate a matched cohort. The analysis of the absolute standardized difference was used to evaluate the balance after propensity score matching of all pre-operative covariates between the EA group and the IV-MO group.

Postoperative morphine consumption and numerical rating scale at 24, 48, and 72 hours between the EA group and the IV-MO group were compared by using the Mann-Whitney U test and repeated measures ANOVA, respectively. Moreover, subgroup analysis was performed by using ANOVA with Bonferroni’s test that compared postoperative opioid consumption in the EA-L, EA-O, and IV-MO groups. A P -value < 0.05 was considered statistically significant. The data were analyzed using STATA version 16.0 (StataCorp LP, College Station, Texas, USA).

A total of 654 patients undergoing liver resection met the inclusion criteria. Forty-two patients were excluded from this study due to no documentation of NRS ( n  = 9), and failure of epidural analgesia was noted ( n  = 33), leaving 612 patients. Among these 612 patients, 254 received epidural analgesia (EA group) and 358 received intravenous morphine (IV-MO group) for postoperative pain control. After matching, 204 patients in the EA group were matched with 204 patients in the IV-MO group (Fig.  1 ).

Baseline variables and outcomes before matching

Epidural analgesia was utilized in 254 patients (41.5%) in the study population. The median age of the study population was 56 years old (IQR 48–63), and 56.8% of the population were males. The body mass index was similar among the groups. The majority of the indication for liver resection is primary liver tumors (75.1%), including hepatocellular carcinoma and cholangiocarcinoma, and accordingly, approximately 55.4% of the study population underwent a major liver resection. Patient characteristics were significantly different between the two groups in the type of liver resection, preoperative white blood cell count, prothrombin time, international normalized ratio, albumin, and total bilirubin. Epidural analgesia was performed more frequently in major liver resection than in minor liver resection (171 (67.3%) versus 83 (32.7%), P < 0.001). Baseline characteristics of all patients undergoing liver resection prior to match are reported in Table  1 .

Baseline variables and outcomes after matching

After matching, 204 patients were in each group, which was similarly balanced on baseline characteristics between the two groups, as shown in Table  1 . The propensity scores were calculated with mulvariable logistic analysis by using baseline covariats that showed mean propensity scores in each group were nearly equally (0.46 ± 0.15 vs. 0.45 ± 0.15, P = 0.800). Moreover, the distribution of propensity scores across the two groups before and after matching was acceptable, as shown in Fig.  2 . The graphical representation of the absolute standardized difference across covariates prior to and after propensity score matching. All covariates after matching were less than 10% of the standardized threshold, which represented an adequate balance of baseline covariates between the EA group and the IV-MO group (Fig.  3 ).

Distribution of propensity scores before matching ( A ) and after matching ( B )

Absolute standardized difference before and after propensity score matching for baseline covariates comparing between epidural group and intravenous morphine group

The EA group exhibited significantly lower morphine consumption compared to the IV-MO group at 24, 48, and 72 hours postoperatively ( P < 0.001 , Table  2 ). The EA group was further divided into two subgroups, which were EA with opioids (EA-O) and without opioids (EA-L). The morphine consumption in both subgroups was significantly lower than that of the IV-MO group, although no significant difference was observed between EA-O and EA-L (Table  3 ). The mean numerical rating scale of the three groups was assessed at 24, 48, and 72 hours postoperatively and compared by using repeated measure ANOVA, revealing no statistically significant difference ( P = 0.422 ), as illustrated in Fig.  4 .

Postoperative numerical rating scale in patients undergoing open liver resection

The incidence of intraoperative hypotension was significantly higher in the EA group (73.0% vs. 55.1%, P  <  0.001 ), which was associated with significantly higher usage of vasopressors and inotropic drugs (67.2% vs. 52.0%, P  < 0.001). However, the amounts of intravascular volume administration and estimated blood loss were not different between the two groups (2525.0 mL vs. 2500.0 mL, P  = 0.657, and 640.0 mL vs. 700.0 mL, P  = 0.818, respectively) (Table  4 ).

The length of hospital stays, incidence of ICU admission, and the length of ICU stays were also not statistically different ( P  = 0.500 , 0.424 , and 0.479 respectively) (Table  5 ). Surgically related complications were not significantly different between both groups. However, the number of non-surgical-related complications was statistically higher in the EA group compared to the IV-MO group, including postoperative hypotension (20.1% vs. 1.5%; P  < 0.001), and postoperative nausea and vomiting (4.9% vs. 0%; P  < 0.001). In addition, one patient who received epidural analgesia with opioids developed respiratory depression. Lastly, no epidural hematoma was reported in our study.

This study constitutes one of the most extensive single-center investigations on the efficacy and outcomes of epidural analgesia for open liver resection. Thoracic epidural analgesia was employed for postoperative pain management in 41.5% of the patients with a low failure rate (5%) and no serious catheter-related complications. Our results reveal that epidural analgesia significantly reduces morphine consumption within the initial 72 hours postoperatively compared to intravenous opioid administration. These findings are consistent with prior research [ 2 , 4 , 5 , 17 , 18 ]. A systematic review and meta-analysis have shown that thoracic epidural analgesia provides superior pain control for patients undergoing open liver resection, compared to patient-controlled analgesia 48 hours after surgery [ 17 ]. Nonetheless, we found that no significant differences were observed in pain scores on the numerical rating scale between the EA group and IV-MO group, which contrasts with findings from previous studies [ 3 , 4 , 5 ]. This result could be caused by the retrospective nature of our study, potentially affecting the accuracy of pain score measurements, whether before or after morphine administration, and for both resting and movement-induced pain. Additionally, the pain score is inherently subjective and can vary widely among patients.

In our center, the utilization rate of epidural analgesia in open liver resection was relatively high (41.5%) compared with other studies, where usage ranged from 5.9 to 13.9% [ 19 , 20 ]. The observed failure rate of epidural analgesia in our study was 5.0%, markedly lower than the approximately 20-30% reported in other research [ 21 , 22 ]. This difference may be due to our center’s high rate of epidural analgesia utilization and the procedures being performed or supervised by experienced anesthesiologists.

The majority of participants in our study were diagnosed with primary liver tumors and had pre-operative coagulopathy, hypoalbuminemia, and hyperbilirubinemia. These factors may introduce potential selection biases regarding the application of epidural analgesia. Additionally, the clinical decision to use TEA was based on the patient’s preoperative condition and the extent of liver resection, resulting in significant differences in baseline characteristics between the groups. These disparities make it difficult to compare and interpret the outcomes of the EA group and the IV-MO group. To mitigate these biases, propensity score matching was employed to achieve good comparability between the groups with well-balanced baseline characteristics. Our study differs from prior retrospective studies, often characterized by smaller sample sizes and unadjusted selection bias [ 23 , 24 ], by having a large sample size from a single center and a high utilization rate of epidural analgesia.

Although epidural analgesia is beneficial for various abdominal surgeries, particularly upper abdominal surgery, and is commonly performed in open liver resection [ 4 , 25 ], there are special concerns regarding the safety of epidural analgesia in liver resection due to epidural-related complications, such as epidural hematoma, spinal cord injury resulting in permanent paraplegia, epidural abscess, localized pain at the epidural site, and intrathecal catheterization [ 26 , 27 ]. Spinal cord injury can occur, particularly in patients who are extremely age, obese, or have diabetes [ 28 ]. Therefore, it is crucial to carefully weigh the risks and benefits when considering epidural analgesia in these patients.

Our study found no significant difference in postoperative complications between groups, similar to other reports [ 3 , 29 ]. However, the incidence of hypotension appeared to be higher in the EA group than in the IV-MO group, consistent with a prior study that reported 75% of patients with epidural analgesia developed perioperative hypotension [ 24 ] and required greater amounts of intravenous fluid until 72 hours postoperatively [ 30 ]. Moreover, the combination of epidural analgesia with general anesthesia is often used during the intraoperative period, which increases the risk of intraoperative hypotension and the need for vasopressors and inotropic drugs, similar to previous studies [ 21 , 24 ]. The possible causes of intraoperative hypotension are peripheral vasodilatation due to sympathetic nervous system blockade and maintaining a low central venous pressure technique during the parenchymal transection phase to reduce blood loss [ 24 , 31 ].

Perioperative hypotension in patients receiving epidural analgesia is a common adverse event related to regional anesthesia and is associated with a higher rate of postoperative nausea and vomiting in the EA group in our study. Various mechanisms have been proposed to explain postoperative nausea and vomiting in patients receiving regional anesthesia, including perioperative hypotension precipitating brain stem and gut hypoperfusion, resulting in the release of emetogenic substances, as well as the use of epidural opioids and high levels of anesthetic blockage [ 32 ].

Moreover, acute kidney injury (AKI) after liver resection is caused by maintaining low central venous pressure and the utilization of epidural analgesia, leading to hypotension and reduced renal perfusion pressure [ 33 ]. A large retrospective study reported an incidence of acute kidney injury occurring 8.2–12.0% that was associated with epidural analgesia, age ≥ 60 years old, chronic renal failure, major liver resection, and blood transfusion requirement [ 33 , 34 ]. Nevertheless, our result showed there was no significant difference in AKI incidence between the groups, consistent with previous studies that found no significant difference in renal failure and postoperative creatinine levels between patients with and without epidural analgesia [ 35 ]. This is likely due to the routine administration of adequate intravascular fluid resuscitation following parenchymal transection, guided by either central venous pressure or stroke volume variation monitoring.

Postoperative coagulation disturbance caused by transient liver dysfunction after liver resection should be considered due to potential bleeding complications, including catheter-related epidural hematoma, which is the most serious epidural-related complication [ 36 , 37 ]. Although the incidence of epidural hematoma is extremely rare (1:150,000) [ 26 , 31 ], spinal hematomas after epidural analgesia in cirrhotic patients have been reported [ 38 ]. Moreover, patients with pre-operative inadequate hemostasis, pre-existing liver cirrhosis, extensive liver resection, small remnant of liver volume, and significant blood loss greater than 1000 ml during surgery have increased potential risks of postoperative coagulopathy, leading to delayed removal of the epidural catheter after liver resection [ 10 , 25 , 37 , 39 , 40 ]. These factors can exacerbate postoperative coagulopathy, leading to an increased risk of bleeding and complications. In our study, precautions were taken to mitigate these risks, including careful intraoperative management of blood loss, regular monitoring of coagulation parameters, and timely administration of blood products as needed. Despite the high utilization rate of epidural analgesia in our study population, none developed epidural hematomas.

The length of hospital stays was not significantly different between the two groups, aligning with previous studies [ 3 , 4 , 17 , 36 ] due to the fact that the day of epidural catheter removal in our study was postoperative day 3 in patients without postoperative coagulopathy, in accordance with the Enhanced Recovery After Surgery (ERAS) pathway for liver resection [ 6 ]. In contrast, some studies [ 19 , 41 ] reported longer hospital stays for patients receiving epidural analgesia, which was associated with delayed epidural catheter removal and is a controversial issue in the ERAS protocol for liver resection [ 12 ]. A recent randomized controlled trial by John et al. found shorter postoperative hospital stays in patients receiving intravenous patient-controlled analgesia (IV-PCA) compared to those with epidural analgesia. The authors argued that IV-PCA is non-inferior to epidural analgesia in terms of pain relief, simplicity of use, cost-effectiveness, ease of implementation, and reduced time consumption compared to epidural analgesia [ 41 ].

Continuous thoracic epidural analgesia is an effective technique for providing excellent postoperative pain control in patients undergoing liver resection, as recommended in the PROSPECT group [ 42 ]. However, perioperative hypotension is a drawback of this technique, which could potentially delay ambulation. Accordingly, optimizing fluid administration, ensuring adequate postoperative pain control, and employing multimodal analgesia are crucial to preventing hypotension and facilitating enahanced recovery after surgery [ 12 , 43 ].

Due to the nature of a retrospective study, an important factor in this study is the typically unavoidable biases in the study. Although we utilized propensity score matching to adjust the selection bias, residual confounding factors related to indications and contraindications remained. Additionally, our institute lacked standard protocols for prescriping of local anesthetics in epidural analgesia, and we did not control the rate and concentration of local anesthetics. Lastly, the routine application of thoracic epidural analgesia in liver resection is not standard practice in some institutions due to concerns regarding the high rate of epidural failure, postoperative coagulation disturbance, and other adverse effects; accordingly, generalizations of our study should be considered.

To conclude, thoracic epidural analgesia is superior to intravenous opioid administration in reducing postoperative opioid consumption within the first 72 hours postoperatively. While perioperative hypotension requiring treatment is a consideration, the incidence of epidural-related hypotension during the postoperative period can be minimized with adequate intravascular volume replacement after liver parenchymal resection and an understanding of the physiological changes after liver surgery. Our study suggests that continuous thoracic epidural analgesia can be effectively and safely performed in patients undergoing open liver resection without contraindications. Further high-quality randomized controlled trials could be conducted to determine the effectiveness and safety of epidural analgesia in liver resection.

Data availability

The datasets supporting the conclusion of this study are available from the corresponding author upon request.

Abbreviations

Thoracic Epidural Analgesia

Institute Review Board

Body Mass Index

American Society of Anesthesiologists (ASA) classification

Numerical Rating Scale

Epidural Analgesia group

Intravenous Morphine group

Epidural Analgesia with Local Anesthetics with Opioids group

Epidural Analgesia with Local Anesthetics without Opioids group

Intensive Care Unit

Interquartile Range

Acute Kidney Injury

Enhanced Recovery After Surgery

Intravenous Patient-Controlled Analgesia

Watanabe G, Ishizawa T, Yamamoto S, Kokudo T, Nishioka Y, Ichida A, et al. Impact of abdominal incision type on Postoperative Pain and Quality of Life following Hepatectomy. World J Surg. 2021;45(6):1887–96.

Article   PubMed   Google Scholar  

Ganapathi S, Roberts G, Mogford S, Bahlmann B, Ateleanu B, Kumar N. Epidural analgesia provides effective pain relief in patients undergoing open liver surgery. Br J Pain. 2015;9(2):78–85.

Article   PubMed   PubMed Central   Google Scholar  

Revie EJ, McKeown DW, Wilson JA, Garden OJ, Wigmore SJ. Randomized clinical trial of local infiltration plus patient-controlled opiate analgesia vs. epidural analgesia following liver resection surgery. HPB (Oxford). 2012;14(9):611–8.

Aloia TA, Kim BJ, Segraves-Chun YS, Cata JP, Truty MJ, Shi Q, et al. A randomized controlled trial of postoperative thoracic epidural Analgesia Versus Intravenous patient-controlled Analgesia after Major Hepatopancreatobiliary surgery. Ann Surg. 2017;266(3):545–54.

Mallard CWB, Bauer B, Sloan PA, Rebel A. Effects of intraoperative epidural anesthesia during hepatectomy on intraoperative and post-operative patient outcomes. Int J Clin Anesth Res. 2018;2:23–30.

Article   Google Scholar  

Yip VS, Dunne DF, Samuels S, Tan CY, Lacasia C, Tang J, et al. Adherence to early mobilisation: Key for successful enhanced recovery after liver resection. Eur J Surg Oncol. 2016;42(10):1561–7.

Article   PubMed   CAS   Google Scholar  

Rodgers A, Walker N, Schug S, McKee A, Kehlet H, van Zundert A, et al. Reduction of postoperative mortality and morbidity with epidural or spinal anaesthesia: results from overview of randomised trials. BMJ. 2000;321(7275):1493.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Block BM, Liu SS, Rowlingson AJ, Cowan AR, Cowan JA Jr., Wu CL. Efficacy of postoperative epidural analgesia: a meta-analysis. JAMA. 2003;290(18):2455–63.

Qi S, Chen G, Cao P, Hu J, He G, Luo J, et al. Safety and efficacy of enhanced recovery after surgery (ERAS) programs in patients undergoing hepatectomy: a prospective randomized controlled trial. J Clin Lab Anal. 2018;32(6):e22434.

Jacquenod P, Wallon G, Gazon M, Darnis B, Pradat P, Virlogeux V, et al. Incidence and risk factors of Coagulation Profile Derangement after Liver surgery: implications for the Use of Epidural Analgesia-A Retrospective Cohort Study. Anesth Analg. 2018;126(4):1142–7.

Singh SA, Vivekananthan P, Sharma A, Sharma S, Bharathy KG. Retrospective analysis of post-operative coagulopathy after major hepatic resection at a tertiary care centre in Northern India. Indian J Anaesth. 2017;61(7):575–80.

Agarwal V, Divatia JV. Enhanced recovery after surgery in liver resection: current concepts and controversies. Korean J Anesthesiol. 2019;72(2):119–29.

Reddy SKBA, Turley RS, Steel JL, Tsung A, Marsh JW, Geller DA, Clary BM. A standard definition of major hepatectomy: resection of four or more liver segments. HPB. 2011;13(7):494–502.

Hermanides J, Hollmann MW, Stevens MF, Lirk P. Failed epidural: causes and management. Br J Anaesth. 2012;109(2):144–54.

Fine PG, Portenoy RK. Ad Hoc Expert Panel on evidence R, guidelines for opioid R. establishing best practices for opioid rotation: conclusions of an expert panel. J Pain Symptom Manage. 2009;38(3):418–25.

Lonjon G, Porcher R, Ergina P, Fouet M, Boutron I. Potential pitfalls of Reporting and Bias in Observational Studies with Propensity score analysis assessing a Surgical Procedure: a methodological systematic review. Ann Surg. 2017;265(5):901–9.

Li J, Pourrahmat MM, Vasilyeva E, Kim PT, Osborn J, Wiseman SM. Efficacy and safety of patient-controlled Analgesia compared with epidural Analgesia after Open hepatic resection: a systematic review and Meta-analysis. Ann Surg. 2019;270(2):200–8.

Knaak C, Spies C, Schneider A, Jara M, Vorderwulbecke G, Kuhlmann AD, et al. Epidural anesthesia in liver Surgery-A propensity score-matched analysis. Pain Med. 2020;21(11):2650–60.

Rosero EB, Cheng GS, Khatri KP, Joshi GP. Evaluation of epidural analgesia for open major liver resection surgery from a US inpatient sample. Proc (Bayl Univ Med Cent). 2014;27(4):305–12.

PubMed   Google Scholar  

Wong-Lun-Hing EM, van Dam RM, Welsh FK, Wells JK, John TG, Cresswell AB, et al. Postoperative pain control using continuous i.m. bupivacaine infusion plus patient-controlled analgesia compared with epidural analgesia after major hepatectomy. HPB (Oxford). 2014;16(7):601–9.

Bell R, Ward D, Jeffery J, Toogood GJ, Lodge JA, Rao K, et al. A randomized controlled trial comparing Epidural Analgesia Versus continuous local Anesthetic Infiltration Via Abdominal Wound Catheter in Open Liver Resection. Ann Surg. 2019;269(3):413–9.

McLeod G, Davies H, Munnoch N, Bannister J, MacRae W. Postoperative pain relief using thoracic epidural analgesia: outstanding success and disappointing failures. Anaesthesia. 2001;56(1):75–81.

Christopher Mallard BW, Brooke Bauer PA, Sloan. Sean Dineen and Annette Rebel. Effects of intraoperative epidural anesthesia during hepatectomy on intraoperative and post-operative patient outcomes. Int J Clin Anesth Res. 2018.

Revie EJ, Massie LJ, McNally SJ, McKeown DW, Garden OJ, Wigmore SJ. Effectiveness of epidural analgesia following open liver resection. HPB (Oxford). 2011;13(3):206–11.

Esteve N, Ferrer A, Sansaloni C, Mariscal M, Torres M, Mora C. Epidural anesthesia and analgesia in liver resection: safety and effectiveness. Rev Esp Anestesiol Reanim. 2017;64(2):86–94.

Cook TM, Counsell D, Wildsmith JA, Royal College of Anaesthetists Third National Audit P. Major complications of central neuraxial block: report on the Third National Audit Project of the Royal College of Anaesthetists. Br J Anaesth. 2009;102(2):179–90.

Shontz R, Karuparthy V, Temple R, Brennan TJ. Prevalence and risk factors predisposing to coagulopathy in patients receiving epidural analgesia for hepatic surgery. Reg Anesth Pain Med. 2009;34(4):308–11.

Pozza DH, Tavares I, Cruz CD, Fonseca S. Spinal cord Injury and complications related to Neuraxial Anaesthesia procedures: a systematic review. Int J Mol Sci. 2023;24(5).

Page A, Rostad B, Staley CA, Levy JH, Park J, Goodman M, et al. Epidural analgesia in hepatic resection. J Am Coll Surg. 2008;206(6):1184–92.

Sakowska M, Docherty E, Linscott D, Connor S. A change in practice from epidural to intrathecal morphine analgesia for hepato-pancreato-biliary surgery. World J Surg. 2009;33(9):1802–8.

Tzimas P, Prout J, Papadopoulos G, Mallett SV. Epidural anaesthesia and analgesia for liver resection. Anaesthesia. 2013;68(6):628–35.

Borgeat A, Ekatodramis G, Schenker CA. Postoperative nausea and vomiting in regional anesthesia: a review. Anesthesiology. 2003;98(2):530–47.

Kambakamba P, Slankamenac K, Tschuor C, Kron P, Wirsching A, Maurer K, et al. Epidural analgesia and perioperative kidney function after major liver resection. Br J Surg. 2015;102(7):805–12.

Tomozawa A, Ishikawa S, Shiota N, Cholvisudhi P, Makita K. Perioperative risk factors for acute kidney injury after liver resection surgery: an historical cohort study. Can J Anaesth. 2015;62(7):753–61.

Behem CR, Wegner JC, Pinnschmidt HO, Greiwe G, Graessler MF, Funcke S, et al. Effect of thoracic epidural anesthesia on postoperative outcome in major liver surgery: a retrospective cohort study. Langenbecks Arch Surg. 2023;408(1):168.

Fayed NA, Abo El-Wafa HB, Gab-Alla NM, Yassen KA, Lotfy ME. Comparison between intravenous patient controlled analgesia and patient controlled epidural analgesia in cirrhotic patients after hepatic resection. Middle East J Anaesthesiol. 2014;22(5):467–76.

Takita K, Uchida Y, Hase T, Kamiyama T, Morimoto Y. Co-existing liver disease increases the risk of postoperative thrombocytopenia in patients undergoing hepatic resection: implications for the risk of epidural hematoma associated with the removal of an epidural catheter. J Anesth. 2014;28(4):554–8.

Morisaki H, Doi J, Ochiai R, Takeda J, Fukushima K. Epidural hematoma after epidural anesthesia in a patient with hepatic cirrhosis. Anesth Analg. 1995;80(5):1033–5.

PubMed   CAS   Google Scholar  

Lim HJ, Koay CK, Lee LS. Postoperative coagulopathy after liver resection–implications for epidural analgesia. Anaesth Intensive Care. 2006;34(1):118–9.

Patel J, Jones CN, Amoako D. Perioperative management for hepatic resection surgery. BJA Educ. 2022;22(9):357–63.

Hausken J, Fretland AA, Edwin B, Andersen MH, Dagenborg VJ, Bjornelv GMW, et al. Intravenous patient-controlled Analgesia Versus thoracic epidural Analgesia after Open Liver surgery: a prospective, randomized, controlled, Noninferiority Trial. Ann Surg. 2019;270(2):193–9.

Dieu A, Huynen P, Lavand’homme P, Beloeil H, Freys SM, Pogatzki-Zahn EM, et al. Pain management after open liver resection: Procedure-Specific Postoperative Pain Management (PROSPECT) recommendations. Reg Anesth Pain Med. 2021;46(5):433–45.

Joliat GR, Kobayashi K, Hasegawa K, Thomson JE, Padbury R, Scott M, et al. Guidelines for Perioperative Care for Liver surgery: enhanced recovery after surgery (ERAS) Society recommendations 2022. World J Surg. 2023;47(1):11–34.

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Isarapong Pianngarn and Worakitti Lapisatepun contributed equally to this work as co-first authors.

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Department of Anesthesiology, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Road, T. Sriphum, A. Muang, Chiang Mai, 50200, Thailand

Isarapong Pianngarn, Maytinee Kulpanun & Warangkana Lapisatepun

Department of Surgery, Division of Hepatobilliary Pancreatic Surgery, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Road, T. Sriphum, A. Muang, Chiang Mai, 50200, Thailand

Worakitti Lapisatepun, Anon Chotirosniramit & Sunhawit Junrungsee

Clinical Surgical Research Center, Chiang Mai University, 110 Inthawarorot Road, T. Sriphum, A. Muang, Chiang Mai, 50200, Thailand

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Conceptualization, Wa. L. and I.P.; methodology, Wa.L. and Wo.L.; software, Wa.L. and M.K.; validation, I.P. and Wo.L.; formal analysis, Wa.L.; investigation Wa.L., A.C., M.K. and S.J.; resources, A.C., S.J. and Wo.L.; data curation, Wa.L and M.K.; writing—original draft preparation, Wa.L., I.P. and M.K.; writing—review and editing, Wa.L., I.P., and Wo.L.; visualization, Wo.L. and I.P.; supervision, Wa.L.; project administration, Wa.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Warangkana Lapisatepun .

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This study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University, Thailand (IRB number: ANE-2562-06771, approved on December 23, 2019) and registered at https://clinicaltrials.gov/ (NCT number: NCT06301932). In addition, the study was conducted in accordance with the Declaration of Helsinki. The requirement for written informed patient consent was waived due to the retrospective nature of the study, which involved no more than minimal risk, and the information used in the study was anonymized. The Research Ethics Committee of the Faculty of Medicine, Chiang Mai University, Thailand waived the need for informed consent.

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Pianngarn, I., Lapisatepun, W., Kulpanun, M. et al. The effectiveness and outcomes of epidural analgesia in patients undergoing open liver resection: a propensity score matching analysis. BMC Anesthesiol 24 , 305 (2024). https://doi.org/10.1186/s12871-024-02697-1

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DOI : https://doi.org/10.1186/s12871-024-02697-1

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