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Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With th...

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Published in:Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 24
Main Authors: Finkelstein, Joseph, Gabriel, Aileen, Schmer, Susanna, Truong, Tuyet-Trinh, Dunn, Andrew
Format: research tables/charts Journal Article
Published: Springer Nature 9/18/2024
Online Access:View this record in EBSCOhost
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      dt: 9/18/2024
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-024-02104-9
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        atl: Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.
      aug:
        au:
          Finkelstein, Joseph
          Gabriel, Aileen
          Schmer, Susanna
          Truong, Tuyet-Trinh
          Dunn, Andrew
        affil: https://ror.org/03r0ha626 Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, 84108, Salt Lake City, UT, USA
      sug:
        subj:
          Electronic Health Records
          Decision Support Systems, Clinical
          Artificial Intelligence
          Discharge Planning
          Implementation Science
          Health Services Accessibility
          Program Implementation
          Human
          Male
          Female
          Health Personnel
          Semi-Structured Interview
          Purposive Sample
          Conceptual Framework
          Case Managers
          Social Workers
          Nurse Managers
          Hospitalists
          Qualitative Studies
          Content Analysis
          Stakeholder Participation
          Professional Role
          Trust
          Health Care Delivery
          Questionnaires
          Attitude of Health Personnel
          Funding Source
          Male
          Female
      ab: Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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