Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design

Background Drug–drug interactions (DDIs) are common and can result in patient harm. Electronic health records warn clinicians about DDIs via alerts, but the clinical decision support they provide is inadequate. Little is known about clinicians’ real-world DDI decision-making process to inform more e...

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Main Authors: Nervana Elkhadragy, Amanda P Ifeachor, Julie B Diiulio, Karen J Arthur, Michael Weiner, Laura G Militello, Peter A Glassman, Alan J Zillich, Alissa L Russ-Jara
Format: Article
Language:English
Published: BMJ Publishing Group 2023-12-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/13/12/e075512.full
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author Nervana Elkhadragy
Amanda P Ifeachor
Julie B Diiulio
Karen J Arthur
Michael Weiner
Laura G Militello
Peter A Glassman
Alan J Zillich
Alissa L Russ-Jara
author_facet Nervana Elkhadragy
Amanda P Ifeachor
Julie B Diiulio
Karen J Arthur
Michael Weiner
Laura G Militello
Peter A Glassman
Alan J Zillich
Alissa L Russ-Jara
author_sort Nervana Elkhadragy
collection DOAJ
description Background Drug–drug interactions (DDIs) are common and can result in patient harm. Electronic health records warn clinicians about DDIs via alerts, but the clinical decision support they provide is inadequate. Little is known about clinicians’ real-world DDI decision-making process to inform more effective alerts.Objective Apply cognitive task analysis techniques to determine informational cues used by clinicians to manage DDIs and identify opportunities to improve alerts.Design Clinicians submitted incident forms involving DDIs, which were eligible for inclusion if there was potential for serious patient harm. For selected incidents, we met with the clinician for a 60 min interview. Each interview transcript was analysed to identify decision requirements and delineate clinicians’ decision-making process. We then performed an inductive, qualitative analysis across incidents.Setting Inpatient and outpatient care at a major, tertiary Veterans Affairs medical centre.Participants Physicians, pharmacists and nurse practitioners.Outcomes Themes to identify informational cues that clinicians used to manage DDIs.Results We conducted qualitative analyses of 20 incidents. Data informed a descriptive model of clinicians’ decision-making process, consisting of four main steps: (1) detect a potential DDI; (2) DDI problem-solving, sensemaking and planning; (3) prescribing decision and (4) resolving actions. Within steps (1) and (2), we identified 19 information cues that clinicians used to manage DDIs for patients. These cues informed their subsequent decisions in steps (3) and (4). Our findings inform DDI alert recommendations to improve clinicians’ decision-making efficiency, confidence and effectiveness.Conclusions Our study provides three key contributions. Our study is the first to present an illustrative model of clinicians’ real-world decision making for managing DDIs. Second, our findings add to scientific knowledge by identifying 19 cognitive cues that clinicians rely on for DDI management in clinical practice. Third, our results provide essential, foundational knowledge to inform more robust DDI clinical decision support in the future.
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spelling doaj-art-aaecfbab85ff48218f0d0dfe80b4dbc72025-08-20T03:52:16ZengBMJ Publishing GroupBMJ Open2044-60552023-12-01131210.1136/bmjopen-2023-075512Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert designNervana Elkhadragy0Amanda P Ifeachor1Julie B Diiulio2Karen J Arthur3Michael Weiner4Laura G Militello5Peter A Glassman6Alan J Zillich7Alissa L Russ-Jara8School of Pharmacy, University of Wyoming, Laramie, Wyoming, USARichard L. Roudebush VA Medical Center, U.S. Department of Veterans Affairs, Veterans Health Administration, Indianapolis, Indiana, USAApplied Decision Science, LLC, Dayton, Ohio, USARichard L. Roudebush VA Medical Center, U.S. Department of Veterans Affairs, Veterans Health Administration, Indianapolis, Indiana, USAHealth Services Research and Development Service CIN 13-416, Center for Health Information and Communication, U.S. Department of Veterans Affairs (VA), Veterans Health Administration, Indianapolis, Indiana, USAApplied Decision Science, LLC, Dayton, Ohio, USADepartment of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USADepartment of Pharmacy Practice, College of Pharmacy, Purdue University, West Lafayette, Indiana, USAHealth Services Research and Development Service CIN 13-416, Center for Health Information and Communication, U.S. Department of Veterans Affairs (VA), Veterans Health Administration, Indianapolis, Indiana, USABackground Drug–drug interactions (DDIs) are common and can result in patient harm. Electronic health records warn clinicians about DDIs via alerts, but the clinical decision support they provide is inadequate. Little is known about clinicians’ real-world DDI decision-making process to inform more effective alerts.Objective Apply cognitive task analysis techniques to determine informational cues used by clinicians to manage DDIs and identify opportunities to improve alerts.Design Clinicians submitted incident forms involving DDIs, which were eligible for inclusion if there was potential for serious patient harm. For selected incidents, we met with the clinician for a 60 min interview. Each interview transcript was analysed to identify decision requirements and delineate clinicians’ decision-making process. We then performed an inductive, qualitative analysis across incidents.Setting Inpatient and outpatient care at a major, tertiary Veterans Affairs medical centre.Participants Physicians, pharmacists and nurse practitioners.Outcomes Themes to identify informational cues that clinicians used to manage DDIs.Results We conducted qualitative analyses of 20 incidents. Data informed a descriptive model of clinicians’ decision-making process, consisting of four main steps: (1) detect a potential DDI; (2) DDI problem-solving, sensemaking and planning; (3) prescribing decision and (4) resolving actions. Within steps (1) and (2), we identified 19 information cues that clinicians used to manage DDIs for patients. These cues informed their subsequent decisions in steps (3) and (4). Our findings inform DDI alert recommendations to improve clinicians’ decision-making efficiency, confidence and effectiveness.Conclusions Our study provides three key contributions. Our study is the first to present an illustrative model of clinicians’ real-world decision making for managing DDIs. Second, our findings add to scientific knowledge by identifying 19 cognitive cues that clinicians rely on for DDI management in clinical practice. Third, our results provide essential, foundational knowledge to inform more robust DDI clinical decision support in the future.https://bmjopen.bmj.com/content/13/12/e075512.full
spellingShingle Nervana Elkhadragy
Amanda P Ifeachor
Julie B Diiulio
Karen J Arthur
Michael Weiner
Laura G Militello
Peter A Glassman
Alan J Zillich
Alissa L Russ-Jara
Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
BMJ Open
title Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
title_full Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
title_fullStr Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
title_full_unstemmed Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
title_short Cognitive task analysis of clinicians’ drug–drug interaction management during patient care and implications for alert design
title_sort cognitive task analysis of clinicians drug drug interaction management during patient care and implications for alert design
url https://bmjopen.bmj.com/content/13/12/e075512.full
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