Epic EHR in Clinical Research

• Electronic Health Records (EHRs) facilitate health data exchange across interconnected systems.
• Epic is a leading EHR provider, widely adopted globally, yet literature lacks detail on its use in healthcare research.
• A scoping review aimed to synthesize available literature on the Epic EHR's research applications in clinical and health sciences.

Methods

• Nine major information repositories were searched, including databases and gray literature.
• Detailed criteria were developed to categorize research data into five major domains for result presentation.

Results

• The review identified a mixture of method types across five research domains.
• 4669 articles screened, 206 abstracted by two independent reviewers at each stage.
• The majority of studies originated from the United States, with a notable increase in volume from 2015 onwards.
• Articles frequently focused on clinical care, health services research, and clinical decision support.
• Longitudinal designs and single-site interventional studies in adult populations were common research designs.
• Significant facilitators and barriers were identified of Epic and other EHRs' use.
• Lessons were synthesized regarding the utilization of Epic and other EHRs for research.

Conclusions

• The Epic EHR offers a range of functions beneficial for research.
• Domains include clinical and population health, quality improvement, and clinical decision support tool development.
• Researchers can leverage features like pooled data, module integration, and decision support tools.
• The system's capabilities can enhance care quality, health system efficiency, and population-level studies.
• While limited to the Epic EHR system, broader lessons apply to other EHRs.

Background on EHR and EMR Systems

• Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems are increasingly important for storing, retrieving, and managing patient health records.
• This is crucial for data-driven initiatives including clinical research, quality improvement (QI), and decision support tools.
• EMR refers to patient health data accumulated during patient encounters, limited to specific care delivery settings, and enabled by the interoperability of the system by multiple providers
• Data often includes demographics, health notes, history, problems, immunizations, treatments, laboratory reports and radiology reports.
• EHR refers to larger, interconnected systems that integrate EMRs from multiple care sites and settings, enabling robust information exchange.
• EHRs are designed for varied audiences and capture broad clinical and non-clinical information to support continuity, integrated and efficient health care.

Significance of EMR and EHR Systems

• EMR and EHR systems serve as vital sources of research data that can be used to improve patient care journeys, healthcare transitions, and service delivery.
• EHR systems have evolved substantially since their introduction in the 1960s through the use of digital health Computer systems.
• The global market of EHR systems is expected to reach US $33 billion by 2025.
• Epic Systems Corporation was an early innovator in this area and is currently among the 5 leading EHR providers with the most widely adopted EHR.
• Epic was independently rated as the best EHR suite for 13 consecutive years and was used in 89% of acute care hospitals in the United States in early 2023.
• Many organizations (e.g. Johns Hopkins University) have adopted Epic across their care delivery systems, and the province of Alberta, Canada, transitioned to a unified Epic-based system called Connect Care.

Objective of Scoping Review

• A critical gap in the available literature as to how the system has been used to facilitate clinical and health research, despite the global reach of the Epic EHR.
• Literature syntheses describe EHR implementation issues, the transition to the Epic EHR, and its integration for personalized care.
• Two reviews on EHR/EMR use in research (narrative review from 2009 and review from 2015) neither focus on a single EHR, nor capture the breadth of health care research areas.
• These reviews also do not follow methods for systematic literature syntheses.
• The review aims to address this critical gap by synthesizing literature on Epic EHR use for various areas of clinical and health sciences research.

Selection Methodology

• Scoping review is used to provide a basis for synthesizing and capturing peer-reviewed and gray literature
• Researchers can completely asses the breadth and depth of a topic and find the strengths, weaknesses, and gaps in existing research
• Existing research applications of Epic will be explored and that a scoping review methodology is best for reaching this objective

Review Procedure

• Joana Briggs Institute guidance and principles of Arksey and O'Malley's 2005 framework will be followed to conduct scoping review [21]
• An unpacking details of Levac el al. and Peters et al will be used as the approach has been supported by them
• Review was conceptualized in consultation with a multidisciplinary team with experts from health informatics, data scientists etc and a brief review protocol was develope

JBI Guidelines Outline

• The JBI guidelines outline population, concepts, and context framework for study's research question
• The population included individuals with any condition of interest
• Concepts included extraction, Integration, or analysis of Data or Epic EHR tools that build to support research
• The Epic EHR could be in use in any available setting
• Review steps include: determining the research question, the identification of relevant studies, the title, abstract, full-text screening and data charting
• Synthesis and reporting of results are used PRISMA-SCR (Preferred Reporting items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [25]

Eligibility Metrics

• Literature on OVID MEDLINE was first searched in the first step to develop the eligibility criteria and then reviewed the first 50 articles for research in EHR
• These 50 pilot articles were individually screened by JC, NS, NW, LR, and DR and followed by a meeting to identify conflicts and eligibility criteria was the first step
• This search defined parameters and ways to use Epic for research
• The focus was to focus use Epic EHR for search from research on Epic EHR

Categorizing Research

• The consensus of team (multidisciplinary) was to exclude literature related to research as it diverged from the main objective
• Searches were also reveal to domains: health services research (HSR, population health, clinical decision support (CDS) tools , initiatives and QI
• This was to keep focus on team's methods use cases of epic and determined by purpose and method of each study
• Primary aim is to identify most of study fits in more than one domain
• Eligibility criteria in textbox1 include factors or barriers to using Epic EHR
• Includes other details of software and ease

Information Sources

• Strategy used formulated by information specialist. Did place a time limit while searching for all source until April 25, 2023
• Previous publication was search by using Cochrane Database of Systematic Reviews and first 250 of Google Scholar and Search Engines.
• More in depth search by using databases, platforms, and the results presented in the prisma diagram

Search Keyword Development

• A detailed list of the search keywords are made based on seminal from National Library of Medicine [30,31]
• Searches resulted on “Epic” search w terms relating to electronic health record and Keywords in title and abstract word.
• Used conference proceedings for grey literature search exclude Conference abstracts for short/full papers only because of detail
• Other resources include google scholar/search, and Epic resource website for grey
• Covidence web platforms was duplicate citations and facilitation of screening process.