Developing an AI-Assisted Tool That Identifies Patients With Multimorbidity and Complex Polypharmacy to Improve the Process of Medication Reviews: Qualitative Interview and Focus Group Study

The findings highlight the potential for AI-assisted tools to streamline and optimize the SMR process, particularly for patients with multimorbidity and complex polypharmacy. However, successful implementation depends on addressing concerns related to workflow integration, user acceptance, and evidence of effectiveness. User-centered design is crucial to ensure that AI-assisted tools support HCPs in delivering high-quality, patient-centered care while minimizing cognitive overload and alert fatigue.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-06-11
DOI
https://doi.org/10.2196/74304
Primary Topic
Pharmaceutical Practices and Patient Outcomes
Type
article
Field-Weighted Citation Impact
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article

Developing an AI-Assisted Tool That Identifies Patients With Multimorbidity and Complex Polypharmacy to Improve the Process of Medication Reviews: Qualitative Interview and Focus Group Study

Maurice O’Connell, Layik Hama, Matthew Sperrin, Iain Buchan et al.
1 links
Journal of Medical Internet Research
Pharmaceutical Practices and Patient Outcomes
article

Developing an AI-Assisted Tool That Identifies Patients With Multimorbidity and Complex Polypharmacy to Improve the Process of Medication Reviews: Qualitative Interview and Focus Group Study

Maurice O’Connell, Layik Hama, Matthew Sperrin, Iain Buchan, Alan Woodall, Tjeerd van Staa, Gary Leeming, Roy A. Ruddle, Frances S Mair, Pietá Schofield, Asra Aslam, Harriet Cant, Eduard Shantsila, Samuel D. Relton, Mark Gabbay, Danushka Bollegala, Michael Abaho, L. Walker, Simon Maskell, Aseel S. Abuzour, Emma Lo, Samantha A. Wilson, Olusegun Popoola, Alan Griffiths
article en

Abstract

The findings highlight the potential for AI-assisted tools to streamline and optimize the SMR process, particularly for patients with multimorbidity and complex polypharmacy. However, successful implementation depends on addressing concerns related to workflow integration, user acceptance, and evidence of effectiveness. User-centered design is crucial to ensure that AI-assisted tools support HCPs in delivering high-quality, patient-centered care while minimizing cognitive overload and alert fatigue.

Journal of Medical Internet ResearchVol. 28
University of Leeds (GB), University of Liverpool (GB), Aintree University Hospitals NHS Foundation Trust (GB), University of Manchester (GB), Bronllys Hospital (GB), Mersey Care NHS Trust (GB), NIHR Research Delivery Network (GB), University of Glasgow (GB)
Openalex Percentile: Top 85%
Pharmaceutical Practices and Patient Outcomes
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