Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning

Zidu Xu; Lauren Evans; Jiyoun Song; Sena Chae; Anahita Davoudi; Kathryn H Bowles; Margaret V McDonald; Maxim Topaz

doi:10.1093/jamia/ocae247

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Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning

Journal article

Peer reviewed

Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning

Zidu Xu, Lauren Evans, Jiyoun Song, Sena Chae, Anahita Davoudi, Kathryn H Bowles, Margaret V McDonald and Maxim Topaz

Journal of the American Medical Informatics Association : JAMIA, Vol.31(11), pp.2641-2650

11/01/2024

DOI: 10.1093/jamia/ocae247

PMCID: PMC11491664

PMID: 39302103

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Abstract

To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.OBJECTIVESTo explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery.METHODSGuided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery.Interviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy.RESULTSInterviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy.While recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable.DISCUSSIONWhile recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable.This study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows.CONCLUSIONThis study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows.

decision support systems

home healthcare

user-friendly interface

workflow integration

nursing informatics

Details

Title: Subtitle: Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning
Creators: Zidu Xu - Columbia University
Lauren Evans
Jiyoun Song - University of Pennsylvania
Sena Chae - University of Iowa
Anahita Davoudi
Kathryn H Bowles
Margaret V McDonald
Maxim Topaz - Columbia University
Resource Type: Journal article
Publication Details: Journal of the American Medical Informatics Association : JAMIA, Vol.31(11), pp.2641-2650
DOI: 10.1093/jamia/ocae247
PMID: 39302103
PMCID: PMC11491664
NLM abbreviation: J Am Med Inform Assoc
ISSN: 1527-974X
eISSN: 1527-974X
Publisher: OXFORD UNIV PRESS
Grant note: Agency for Healthcare Research and Quality (AHRQ): R01 HS027742
This study was supported by the Agency for Healthcare Research and Quality (AHRQ) under grant number R01 HS027742. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ.
Language: English
Electronic publication date: 09/20/2024
Date published: 11/01/2024
Academic Unit: Nursing
Record Identifier: 9984708765502771

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