An electronic health record metadata-mining approach to identifying patient-level interprofessional clinician teams in the intensive care unit.

Autor:	Yakusheva O; The Johns Hopkins University School of Nursing, Baltimore, MD 21205, United States., Khadr L; University of Michigan School of Nursing, Ann Arbor, MI 48109, United States., Lee KA; The Johns Hopkins University School of Nursing, Baltimore, MD 21205, United States., Ratliff HC; Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, MI 48105, United States., Marriott DJ; Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, MI 48109, United States., Costa DK; Yale School of Nursing, West Haven, CT 06516, United States.; Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT 06510, United States.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Dec 17. Date of Electronic Publication: 2024 Dec 17.
DOI:	10.1093/jamia/ocae275
Abstrakt:	Objectives: Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs. Materials and Methods: A retrospective analysis of EHR event logs for mechanically-ventilated patients 18 and older from 5 ICUs in an academic medical center during 1/1/2018-12/31/2019. We defined interprofessional teams as all medical providers (physicians, physician assistants, and nurse practitioners), registered nurses, and respiratory therapists assigned to each patient each shift. We created an EHR event logs-mining program that extracts clinicians who interact with each patient's medical record each shift. The algorithm was validated using the Message Understanding Conference (MUC-6) method against manual chart review of a random sample of 200 patient-shifts from each ICU by two independent reviewers. Results: Our sample included 4559 ICU encounters and 72 846 patient-shifts. Our program extracted 3288 medical providers, 2702 registered nurses, and 219 respiratory therapists linked to these encounters. Eighty-three percent of patient-shift teams included medical providers, 99.3% included registered nurses, and 74.1% included respiratory therapists; 63.4% of shift-level teams included clinicians from all three professions. The program demonstrated 95.9% precision, 96.2% recall, and high face validity. Discussion: Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs. Conclusions: Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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