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Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.
Finkelstein, Joseph; Gabriel, Aileen; Schmer, Susanna; Truong, Tuyet-Trinh; Dunn, Andrew.
Afiliación
  • Finkelstein J; Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA. Joseph.Finkelstein@utah.edu.
  • Gabriel A; Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA.
  • Schmer S; Department of Case Management, Mount Sinai Health System, New York, NY, USA.
  • Truong TT; Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Dunn A; Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
J Med Syst ; 48(1): 89, 2024 Sep 18.
Article en En | MEDLINE | ID: mdl-39292314
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sistemas de Apoyo a Decisiones Clínicas / Registros Electrónicos de Salud Límite: Humans Idioma: En Revista: J Med Syst Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sistemas de Apoyo a Decisiones Clínicas / Registros Electrónicos de Salud Límite: Humans Idioma: En Revista: J Med Syst Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos