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Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.
Yin, Andrew L; Guo, Winston L; Sholle, Evan T; Rajan, Mangala; Alshak, Mark N; Choi, Justin J; Goyal, Parag; Jabri, Assem; Li, Han A; Pinheiro, Laura C; Wehmeyer, Graham T; Weiner, Mark; Safford, Monika M; Campion, Thomas R; Cole, Curtis L.
  • Yin AL; Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States. Electronic address: aly27@cornell.edu.
  • Guo WL; Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, United States.
  • Sholle ET; Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States.
  • Rajan M; Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Alshak MN; Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Choi JJ; Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Goyal P; Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Jabri A; Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Li HA; Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Pinheiro LC; Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Wehmeyer GT; Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Weiner M; Department of Medicine, Weill Cornell Medicine, New York, NY, United States; Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States.
  • Safford MM; Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, United States.
  • Campion TR; Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY, United States.
  • Cole CL; Department of Medicine, Weill Cornell Medicine, New York, NY, United States; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
Int J Med Inform ; 157: 104622, 2022 01.
Article in English | MEDLINE | ID: covidwho-1507080
ABSTRACT

INTRODUCTION:

Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR). MATERIALS AND

METHODS:

For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.

RESULTS:

For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).

CONCLUSION:

Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pharmaceutical Preparations / COVID-19 Type of study: Observational study / Prognostic study / Qualitative research / Reviews Limits: Humans Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pharmaceutical Preparations / COVID-19 Type of study: Observational study / Prognostic study / Qualitative research / Reviews Limits: Humans Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article