-
Journal of Medical Internet Research Dec 2023Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the... (Review)
Review
BACKGROUND
Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.
OBJECTIVE
The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.
METHODS
We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.
RESULTS
We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.
CONCLUSIONS
The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.
Topics: Adult; Humans; Electronic Health Records; Software; Databases, Factual; Electronics; Health Services Research
PubMed: 38100185
DOI: 10.2196/51003 -
Journal of the American Medical... Jun 2023We performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to... (Review)
Review
OBJECTIVE
We performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care.
MATERIALS AND METHODS
Starting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD.
RESULTS
For our cumulative focused update, we reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. We identified 8 articles from the original systematic review, 8 from our new search, and 4 recommended by an expert. We identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening.
CONCLUSION
A variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.
Topics: Humans; Electronic Health Records; Sensitivity and Specificity; Alzheimer Disease; Phenotype
PubMed: 37252836
DOI: 10.1093/jamia/ocad086 -
International Journal of Population... 2023Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable... (Review)
Review
INTRODUCTION
Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.
METHODS
We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.
RESULTS
The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.
CONCLUSION
Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.
Topics: Confidentiality; Data Anonymization; Electronic Health Records; Health Insurance Portability and Accountability Act; Health Records, Personal; Review Literature as Topic; United States
PubMed: 38414537
DOI: 10.23889/ijpds.v8i1.2153 -
American Journal of Human Genetics Jul 2023Two major goals of the Electronic Medical Record and Genomics (eMERGE) Network are to learn how best to return research results to patient/participants and the... (Review)
Review
Two major goals of the Electronic Medical Record and Genomics (eMERGE) Network are to learn how best to return research results to patient/participants and the clinicians who care for them and also to assess the impact of placing these results in clinical care. Yet since its inception, the Network has confronted a host of challenges in achieving these goals, many of which had ethical, legal, or social implications (ELSIs) that required consideration. Here, we share impediments we encountered in recruiting participants, returning results, and assessing their impact, all of which affected our ability to achieve the goals of eMERGE, as well as the steps we took to attempt to address these obstacles. We divide the domains in which we experienced challenges into four broad categories: (1) study design, including recruitment of more diverse groups; (2) consent; (3) returning results to participants and their health care providers (HCPs); and (4) assessment of follow-up care of participants and measuring the impact of research on participants and their families. Since most phases of eMERGE have included children as well as adults, we also address the particular ELSI posed by including pediatric populations in this research. We make specific suggestions for improving translational genomic research to ensure that future projects can effectively return results and assess their impact on patient/participants and providers if the goals of genomic-informed medicine are to be achieved.
Topics: Child; Adult; Humans; Electronic Health Records; Genomics; Genome; Translational Research, Biomedical; Population Groups
PubMed: 37343562
DOI: 10.1016/j.ajhg.2023.05.011 -
Yearbook of Medical Informatics Aug 2023To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022. (Randomized Controlled Trial)
Randomized Controlled Trial
OBJECTIVES
To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.
METHOD
A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.
RESULTS
Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).
CONCLUSIONS
The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.
Topics: Humans; Artificial Intelligence; Medical Informatics; Electronic Health Records; Big Data; Peer Review
PubMed: 38147857
DOI: 10.1055/s-0043-1768748 -
Frontiers in Public Health 2023In the age of digitalization and big data, personal health information is a key resource for health care and clinical research. This study aimed to analyze the... (Review)
Review
BACKGROUND
In the age of digitalization and big data, personal health information is a key resource for health care and clinical research. This study aimed to analyze the determinants and describe the measurement of the willingness to disclose personal health information.
METHODS
The study conducted a systematic review of articles assessing willingness to share personal health information as a primary or secondary outcome. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. English and Italian peer-reviewed research articles were included with no restrictions for publication years. Findings were narratively synthesized.
RESULTS
The search strategy found 1,087 papers, 89 of which passed the screening for title and abstract and the full-text assessment.
CONCLUSION
No validated measurement tool has been developed for willingness to share personal health information. The reviewed papers measured it through surveys, interviews, and questionnaires, which were mutually incomparable. The secondary use of data was the most important determinant of willingness to share, whereas clinical and socioeconomic variables had a slight effect. The main concern discouraging data sharing was privacy, although good data anonymization and the high perceived benefits of sharing may overcome this issue.
Topics: Health Records, Personal; Privacy; Information Dissemination; Surveys and Questionnaires
PubMed: 37546309
DOI: 10.3389/fpubh.2023.1213615 -
Tidsskrift For Den Norske Laegeforening... Sep 2023
Topics: Humans; Periodicals as Topic; Medical Records
PubMed: 37753759
DOI: 10.4045/tidsskr.23.0584 -
Journal of the American Medical... Sep 2023We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR...
OBJECTIVE
We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies.
MATERIALS AND METHODS
We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process.
RESULTS
We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology.
DISCUSSION
There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality.
CONCLUSION
Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.
Topics: Electronic Health Records; Data Accuracy
PubMed: 37390812
DOI: 10.1093/jamia/ocad120 -
BMC Medical Informatics and Decision... Jul 2023Personal Health Records (PHRs) are designed to fulfill the goals of electronic health (eHealth) and empower the individual in the process of self-care. Integrated PHR... (Review)
Review
BACKGROUND
Personal Health Records (PHRs) are designed to fulfill the goals of electronic health (eHealth) and empower the individual in the process of self-care. Integrated PHR can improve the quality of care, strengthen the patient-healthcare provider relationship, and reduce healthcare costs. Still, the process of PHR acceptance and use has been slow and mainly hindered by people's concerns about the security of their personal health information. Thus, the present study aimed to identify the Integrated PHR security requirements and mechanisms.
METHODS
In this applied study, PHR security requirements were identified with a literature review of (library sources, research articles, scientific documents, and reliable websites). The identified requirements were classified, and a questionnaire was developed accordingly. Thirty experts completed the questionnaire in a two-round Delphi technique, and the data were analyzed by descriptive statistics.
RESULTS
The PHR security requirements were identified and classified into seven dimensions confidentiality, availability, integrity, authentication, authorization, non-repudiation, and right of access, each dimension having certain mechanisms. On average, the experts reached an agreement about the mechanisms of confidentiality (94.67%), availability (96.67%), integrity (93.33%), authentication (100%), authorization (97.78%), non-repudiation (100%), and right of access (90%).
CONCLUSION
Integrated PHR security is a requirement for its acceptance and use. To design a useful and reliable integrated PHR, system designers, health policymakers, and healthcare organizations must identify and apply security requirements to guarantee the privacy and confidentiality of data.
Topics: Humans; Electronics; Health Care Costs; Health Facilities; Health Records, Personal; Privacy
PubMed: 37430242
DOI: 10.1186/s12911-023-02225-0