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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 -
JAMA Network Open Aug 2023Despite the large health burden, reliable data on sepsis epidemiology are lacking; studies using International Statistical Classification of Diseases and Related Health... (Observational Study)
Observational Study
IMPORTANCE
Despite the large health burden, reliable data on sepsis epidemiology are lacking; studies using International Statistical Classification of Diseases and Related Health Problems (ICD)-coded hospital discharge diagnosis for sepsis identification suffer from limited sensitivity. Also, ICD data do not allow investigation of underlying pathogens and antimicrobial resistance.
OBJECTIVES
To generate reliable epidemiological estimates by linking data from a population-based database to a reference standard of clinical medical record review.
DESIGN, SETTING, AND PARTICIPANTS
This was a retrospective, observational cohort study using a population-based administrative database including all acute care hospitals of the Scania region in Sweden in 2019 and 2020 to identify hospital-treated sepsis cases by ICD codes. From this database, clinical medical records were also selected for review within 6 strata defined by ICD discharge diagnosis (both with and without sepsis diagnosis). Data were analyzed from April to October 2022.
MAIN OUTCOMES AND MEASURES
Hospital and population incidences of sepsis, case fatality, antimicrobial resistance, and temporal dynamics due to COVID-19 were assessed, as well as validity of ICD-10 case identification methods compared with the reference standard of clinical medical record review.
RESULTS
Out of 295 531 hospitalizations in 2019 in the Scania region of Sweden, 997 patient medical records were reviewed, among which 457 had sepsis according to clinical criteria. Of the patients with clinical sepsis, 232 (51%) were female, and 357 (78%) had at least 1 comorbidity. The median (IQR) age of the cohort was 76 (67-85) years. The incidence of sepsis in hospitalized patients according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria in 2019 was 4.1% (95% CI, 3.6-4.5) by medical record review. This corresponds to an annual incidence rate of 747 (95% CI, 663-832) patients with sepsis per 100 000 population. No significant increase in sepsis during the COVID-19 pandemic nor a decrease in sepsis incidence when excluding COVID-19 sepsis was observed. Few sepsis cases caused by pathogens with antimicrobial resistance were found. The validity of ICD-10-based case identification in administrative data was low.
CONCLUSIONS AND RELEVANCE
In this cohort study of sepsis epidemiology, sepsis was a considerable burden to public health in Sweden. Supplying administrative data with information from clinical medical records can help to generate reliable data on sepsis epidemiology.
Topics: Humans; Female; Aged; Aged, 80 and over; Male; Incidence; Cohort Studies; Pandemics; COVID-19; Sepsis; Medical Records; Anti-Infective Agents
PubMed: 37642964
DOI: 10.1001/jamanetworkopen.2023.31168 -
Orphanet Journal of Rare Diseases Sep 2023To obtain updated estimates of the incidence and prevalence of neurofibromatosis type 1 (NF1) and type 2 (NF2). (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To obtain updated estimates of the incidence and prevalence of neurofibromatosis type 1 (NF1) and type 2 (NF2).
STUDY DESIGN
We conducted a systematic search of NF1 and NF2 incidence or prevalence studies, in OVID Medline, OVID Embase, Web of Science, and Cinahl. Studies were appraised with the Joanna Briggs Institute Prevalence Critical Appraisal tool. Pooled incidence and prevalence rates were estimated through random-effects meta-analysis.
RESULTS
From 1,939 abstracts, 20 studies were fully appraised and 12 were included in the final review. Pooled NF1 prevalence was 1 in 3,164 (95%CI: 1 in 2,132-1 in 4,712). This was higher in studies that screened for NF1, compared to identification of NF1 through medical records (1 in 2,020 and 1 in 4,329, respectively). NF1 pooled birth incidence was 1 in 2,662 (95%CI: 1 in 1,968-1 in 3,601). There were only 2 studies on NF2 prevalence, so data were not pooled. Pooled NF2 birth incidence was 1.08 per 50,000 births (95%CI: 1 in 32,829-1 in 65,019).
CONCLUSION
We present updated estimates of the incidence and prevalence of NF1 and NF2, to help plan for healthcare access and allocation. The prevalence of NF1 from screening studies is higher than from medical record studies, suggesting that the disease may be under recognized. More studies are needed regarding the prevalence of NF2.
Topics: Humans; Incidence; Neurofibromatosis 1; Prevalence; Health Services Accessibility; Medical Records
PubMed: 37710322
DOI: 10.1186/s13023-023-02911-2 -
Arthritis Care & Research Aug 2023Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation...
OBJECTIVE
Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation for gout research in the ED is the timely ability to identify acute gout patients. Our goal was to refine a multicriteria, electronic medical record alert for gout flares and to determine its diagnostic characteristics in the ED.
METHODS
The gout flare alert used electronic medical record data from ED nursing notes and was triggered by the term 'gout' preceding past medical history in the chief complaint, the term 'gout' and a musculoskeletal problem in the chief complaint, or the term 'gout' in the problem list and a musculoskeletal chief complaint. We validated its diagnostic properties to assess presence/absence of gout through manual medical record review using adjudicated expert consensus as the gold standard.
RESULTS
In January 2020, we analyzed 202 patient records from 2 university-based EDs; from these records, 57 patients were identified by our gout flare alert, and 145 were identified by other means as potentially having an acute gout flare. The gout flare alert's positive predictive value was 47% (95% confidence interval [95% CI] 34-60%), negative predictive value was 94% (95% CI 90-98%), sensitivity was 75% (95% CI 61-89%), and specificity was 82% (95% CI 76-88%). The diagnostic properties were similar at both institutions.
CONCLUSION
Our multicomponent gout flare alert had reasonable sensitivity and specificity, albeit a modest positive predictive value. An electronic gout flare alert may help enable the conduct of gout research in the ED setting.
Topics: Humans; Gout; Electronic Health Records; Symptom Flare Up; Sensitivity and Specificity; Emergency Service, Hospital
PubMed: 36408730
DOI: 10.1002/acr.25061 -
Einstein (Sao Paulo, Brazil) 2023To evaluate the time interval and possible delay in transportation to referral units for the treatment of testicular torsion.
OBJECTIVE
To evaluate the time interval and possible delay in transportation to referral units for the treatment of testicular torsion.
METHODS
We retrospectively analyzed all cases of spermatic cord torsion surgically treated at a university hospital between January 2018 to December 2021. We evaluated the time intervals, including pain onset until the first presentation (D1), interhospital transference time (D2), pain onset until urological evaluation in a tertiary service (D3), urological evaluation until surgery (D4), and time from pain onset to surgical treatment (D5). We analyzed demographic and surgical data, orchiectomy rates, and time intervals (D1-D5). Torsions presented to the first medical presentation within 6h were considered early for testicular preservation.
RESULTS
Of the 116 medical records evaluated, 87 had complete data for the time interval analysis (D1 to D5) and were considered the total sample. Thirty-three had D1 ≤6h, 53 had D1 ≤24h (includes patients in the D1 ≤6h subgroup), and 34 had D1 >24h. The median time intervals of the total samples and subgroups D1 ≤6h, D1 ≤24h, and D1 >24h were D1 = 16h 42min, 2h 43min, 4h 14min and 72h, D2 = 4h 41min, 3h 39min, 3h 44min and 9h 59min; D3 = 24h, 6h 40min, 7h and 96h; D4 = 2h 20min, 1h 43min, 1h 52min and 3h 44min; D5 = 24h 42min, 8h 03min, 9h 26min and 99h 10min, respectively. Orchiectomy rates of the total sample, subgroups D1 ≤6h, D1 ≤24h, and D1 >24h were 56.32%, 24.24% (p<0.01), 32.08% (p<0.01), and 91.18% (p<0.01), respectively.
CONCLUSION
Late arrival at the emergency department or a long interhospital transference time determined a large number of patients who underwent orchiectomy. Thus, public health measures and preventive strategies can be developed based on the data from this study aiming to reduce this avoidable outcome.
Topics: Male; Humans; Spermatic Cord Torsion; Retrospective Studies; Emergency Service, Hospital; Hospitals, University; Medical Records
PubMed: 37341219
DOI: 10.31744/einstein_journal/2023AO0238 -
BMJ Open Respiratory Research Sep 2023Animal experiments and clinical trials have revealed a potential relationship between sleep disorders and asthma. However, the associations between these factors remain... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Animal experiments and clinical trials have revealed a potential relationship between sleep disorders and asthma. However, the associations between these factors remain unclear.
MATERIAL AND METHODS
We searched PubMed, Embase, Web of Science and Cochrane Library databases for eligible studies published before 30 December 2022. Studies investigating the association between sleep disorders (insomnia, poor sleep quality and insufficient sleep time) and asthma were selected. Sleep disorders were assessed using questionnaires, interviews, or medical records. Asthma was diagnosed based on medical history and drug use. The Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality checklist were employed for quality assessment. We used OR with 95% CI as the effect measures and forest plots to display the results. Heterogeneity was evaluated using statistics and subgroup analyses were performed for bias analysis. Publication bias was evaluated using the funnel plots and Egger's test.
RESULTS
Twenty-three studies were included in the primary analysis, which suggested a positive association between sleep disorders and asthma (OR: 1.38, 95% CI 1.10 to 1.74). Subgroup analyses were conducted according to the study design, age, family history of asthma and type of sleep disorders. We did not find any association between sleep disorders and asthma in children aged ˂12 years (OR: 1.13, 95% CI 0.97 to 1.32). The association was insignificant in studies where the family history of asthma was adjusted for (OR: 1.16, 95% CI 0.94 to 1.42). Funnel plot and Egger's test indicated a significant publication bias.
CONCLUSION
Sleep disorders are associated with an increased prevalence and incidence of asthma. However, the quality of the evidence was low because of potential biases.
PROSPERO REGISTRATION NUMBER
CRD42023391989.
Topics: United States; Animals; Child; Humans; Sleep Wake Disorders; Asthma; Checklist; Databases, Factual; Medical Records
PubMed: 37735102
DOI: 10.1136/bmjresp-2023-001661 -
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 -
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 -
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 -
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