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Open accessJournal ArticleDOI: 10.2196/22219

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

02 Mar 2021-Journal of Medical Internet Research (JMIR Publications Inc., Toronto, Canada)-Vol. 23, Iss: 3
Abstract: Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

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Topics: Data quality (54%)
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Journal ArticleDOI: 10.1001/JAMAPSYCHIATRY.2021.2500
01 Dec 2021-JAMA Psychiatry
Abstract: Importance Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here. Observations Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach. Conclusions and Relevance Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.

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1 Citations


Open accessJournal ArticleDOI: 10.1007/S40615-021-01139-9
Dena Bushman1, Dena Bushman2, Alexander Davidson2, Preeti Pathela2  +5 moreInstitutions (2)
Abstract: COVID-19 mortality studies have primarily focused on persons aged ≥ 65 years; less is known about decedents aged <65 years. We conducted a case-control study among NYC residents aged 21–64 years hospitalized with COVID-19 diagnosed March 13–April 9, 2020, to determine risk factors for death. Case-patients (n=343) were hospitalized decedents with COVID-19 and control-patients (n=686) were discharged from hospitalization with COVID-19 and matched 2:1 to case-patients on age and residential neighborhood. Conditional logistic regression models were adjusted for patient sex, insurance status, and marital status. Matched adjusted odds ratios (aORs) were calculated for selected underlying conditions, combinations of conditions, and race/ethnic group. Median age of both case-patients and control-patients was 56 years (range: 23–64 years). Having ≥ 1 selected underlying condition increased odds of death 4.45-fold (95% CI: 2.33–8.49). Patients with diabetes; morbid obesity; heart, kidney, or lung disease; cancer; neurologic/neurodevelopmental conditions; mental health conditions; or HIV had significantly increased odds of death. Compared with having neither condition, having both diabetes and obesity or diabetes and heart disease was associated with approximately threefold odds of death. Five select underlying conditions were more prevalent among non-Hispanic Black control-patients than among control-patients of other races/ethnicities. Selected underlying conditions were risk factors for death, and most prevalent among racial/ethnic minorities. Social services; health care resources, including vaccination; and tailored public health messaging are important for COVID-19 prevention. Strengthening these strategies for racial/ethnic minority groups could minimize COVID-19 racial/ethnic disparities.

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Topics: Odds ratio (55%), Epidemiology (52%), Public health (50%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/HEALTHCARE9121648
28 Nov 2021-Healthcare
Abstract: Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients’ records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) “confirmed cases”, 10 (3.05%) “probable cases”, 9 (2.74%) “suspected cases”, and 91 (27.74%) “no sufficient evidence”. The identification of “probable cases” and “suspected cases” was more challenging than “confirmed cases” where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients’ records. “No sufficient evidence” records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

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Topics: Patient safety (55%), Data quality (54%)

Open accessJournal ArticleDOI: 10.3390/IJERPH182211923
Javad J Fatollahi1, Sean Bentley1, Neal Doran1, Neal Doran2  +2 moreInstitutions (2)
Abstract: The prevalence of tobacco use increases in times of stress; however, during the initial stage of the COVID-19 pandemic, tobacco use rates stayed the same in most populations. Previous work focused on the initial months of the pandemic, while this study examined the changes in tobacco use during a later peak period of the pandemic. We used data from 61,852 visits to the VA San Diego Healthcare System from November 2019 to February 2021, divided into pre-, early, and peak pandemic periods. Multinomial logistic regression was used to test whether the odds of being a daily or non-daily tobacco user varied over time, by demographic group, or with the presence of specific psychiatric diagnoses. Younger Veterans had a greater reduction in the prevalence of non-daily tobacco use between the early and peak periods, while older Veterans had a rise in daily use from pre- to the early pandemic, which returned to baseline during the peak. Individuals with substance use disorder and serious mental illness diagnoses were more likely to report tobacco use, but psychiatric diagnoses did not predict change over time. These findings demonstrate factors that potentially contribute to changes in tobacco use during a public health crisis and may help guide future targeted cessation efforts.

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26 results found


Open accessJournal ArticleDOI: 10.1371/JOURNAL.PMED.1001885
Eric I Benchimol1, Liam Smeeth2, Astrid Guttmann3, Katie Harron2  +5 moreInstitutions (7)
06 Oct 2015-PLOS Medicine
Abstract: Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.

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1,579 Citations


Open accessJournal ArticleDOI: 10.1056/NEJMOA2007621
Mandeep R. Mehra1, Sapan S. Desai, SreyRam Kuy2, Timothy D. Henry3  +1 moreInstitutions (4)
Abstract: Background Coronavirus disease 2019 (Covid-19) may disproportionately affect people with cardiovascular disease. Concern has been aroused regarding a potential harmful effect of angiotensi...

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858 Citations


Open accessJournal ArticleDOI: 10.1016/S0140-6736(20)31180-6
13 Jun 2020-The Lancet
Abstract: Summary Background Hydroxychloroquine or chloroquine, often in combination with a second-generation macrolide, are being widely used for treatment of COVID-19, despite no conclusive evidence of their benefit. Although generally safe when used for approved indications such as autoimmune disease or malaria, the safety and benefit of these treatment regimens are poorly evaluated in COVID-19. Methods We did a multinational registry analysis of the use of hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19. The registry comprised data from 671 hospitals in six continents. We included patients hospitalised between Dec 20, 2019, and April 14, 2020, with a positive laboratory finding for SARS-CoV-2. Patients who received one of the treatments of interest within 48 h of diagnosis were included in one of four treatment groups (chloroquine alone, chloroquine with a macrolide, hydroxychloroquine alone, or hydroxychloroquine with a macrolide), and patients who received none of these treatments formed the control group. Patients for whom one of the treatments of interest was initiated more than 48 h after diagnosis or while they were on mechanical ventilation, as well as patients who received remdesivir, were excluded. The main outcomes of interest were in-hospital mortality and the occurrence of de-novo ventricular arrhythmias (non-sustained or sustained ventricular tachycardia or ventricular fibrillation). Findings 96 032 patients (mean age 53·8 years, 46·3% women) with COVID-19 were hospitalised during the study period and met the inclusion criteria. Of these, 14 888 patients were in the treatment groups (1868 received chloroquine, 3783 received chloroquine with a macrolide, 3016 received hydroxychloroquine, and 6221 received hydroxychloroquine with a macrolide) and 81 144 patients were in the control group. 10 698 (11·1%) patients died in hospital. After controlling for multiple confounding factors (age, sex, race or ethnicity, body-mass index, underlying cardiovascular disease and its risk factors, diabetes, underlying lung disease, smoking, immunosuppressed condition, and baseline disease severity), when compared with mortality in the control group (9·3%), hydroxychloroquine (18·0%; hazard ratio 1·335, 95% CI 1·223–1·457), hydroxychloroquine with a macrolide (23·8%; 1·447, 1·368–1·531), chloroquine (16·4%; 1·365, 1·218–1·531), and chloroquine with a macrolide (22·2%; 1·368, 1·273–1·469) were each independently associated with an increased risk of in-hospital mortality. Compared with the control group (0·3%), hydroxychloroquine (6·1%; 2·369, 1·935–2·900), hydroxychloroquine with a macrolide (8·1%; 5·106, 4·106–5·983), chloroquine (4·3%; 3·561, 2·760–4·596), and chloroquine with a macrolide (6·5%; 4·011, 3·344–4·812) were independently associated with an increased risk of de-novo ventricular arrhythmia during hospitalisation. Interpretation We were unable to confirm a benefit of hydroxychloroquine or chloroquine, when used alone or with a macrolide, on in-hospital outcomes for COVID-19. Each of these drug regimens was associated with decreased in-hospital survival and an increased frequency of ventricular arrhythmias when used for treatment of COVID-19. Funding William Harvey Distinguished Chair in Advanced Cardiovascular Medicine at Brigham and Women's Hospital.

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Topics: Hydroxychloroquine (64%)

803 Citations


Open accessJournal ArticleDOI: 10.1136/AMIAJNL-2011-000681
Nicole G. Weiskopf1, Chunhua Weng1Institutions (1)
Abstract: Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

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Topics: Data quality (68%), Data element (57%), Data collection (53%)

709 Citations


Open accessJournal ArticleDOI: 10.1097/MLR.0B013E31829B1DBD
William R. Hersh1, Mark G. Weiner2, Peter J. Embi3, Judith R. Logan1  +7 moreInstitutions (9)
01 Aug 2013-Medical Care
Abstract: The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.

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367 Citations