Abuzour, AS, Wilson, SA 
ORCID: 0000-0002-7959-052X, Woodall, AA ORCID: 0000-0003-2933-0508, Mair, FS, Aslam, A, Clegg, A, Shantsila, E ORCID: 0000-0002-2429-6980, Gabbay, M ORCID: 0000-0002-0126-8485, Abaho, M, Bollegala, D ORCID: 0000-0003-4476-7003 et al (show 15 more authors)
(2026)
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
Journal of Medical Internet Research, 28.
e74304-.
ISSN 1439-4456, 1438-8871
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Text
74304-1213308-5-PB.pdf - Open Access published version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Background: Structured medication reviews (SMRs) are an essential component of medication optimization, especially for patients with multimorbidity and polypharmacy. However, the process remains challenging due to the complexities of patient data, time constraints, and the need for coordination among health care professionals (HCPs). This study explores HCPs’ perspectives on the integration of artificial intelligence (AI)–assisted tools to enhance the SMR process, with a focus on the potential benefits of and barriers to adoption. Objective: This study aims to identify the key user requirements for AI-assisted tools to improve the efficiency and effectiveness of SMRs, specifically for patients with multimorbidity, complex polypharmacy, and frailty. Methods: A qualitative study was conducted involving focus groups and semistructured interviews with HCPs and patients in the United Kingdom. Participants included physicians, pharmacists, clinical pharmacologists, psychiatrists from primary and secondary care, a policy maker, and patients with multimorbidity. Data were analyzed using a hybrid inductive and deductive thematic analysis approach to identify themes related to AI-assisted tool functionality, workflow integration, user-interface visualization, and usability in the SMR process. Results: Four major themes emerged from the analysis: innovative AI potential, optimizing electronic patient record visualization, functionality of the AI tool for SMRs, and facilitators of and barriers to AI tool implementation. HCPs identified the potential of AI to support patient identification and prioritizing those at risk of medication-related harm. AI-assisted tools were viewed as essential in detecting prescribing gaps, drug interactions, and patient risk trajectories over time. Participants emphasized the importance of presenting patient data in an intuitive format, with a patient interface for shared decision-making. Suggestions included color-coding blood results, highlighting critical medication reviews, and providing timelines of patient medical histories. HCPs stressed the need for AI tools to integrate seamlessly with existing electronic patient record systems and provide actionable insights without overwhelming users with excessive notifications or “pop-up” alerts. Factors influencing the uptake of AI-assisted tools included the need for user-friendly design, evidence of tool effectiveness (though some were skeptical about the predictive accuracy of AI models), and addressing concerns around digital exclusion. Conclusions: 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | structured medication reviews, medicine optimization, health technology, risk stratification, artificial intelligence, AI |
| Divisions: | Faculty of Health & Life Sciences Faculty of Science & Engineering Faculty of Science & Engineering > School of Engineering Faculty of Health & Life Sciences > Inst. Population Health Faculty of Health & Life Sciences > Inst. Population Health > Health Data Science Faculty of Health & Life Sciences > Inst. Population Health > Primary Care & Mental Health Faculty of Health & Life Sciences > Inst. Population Health > Public Health, Policy & Systems Faculty of Health & Life Sciences > Inst. Population Health > Inst. Population Health (T&R Staff) Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Inst. Systems, Molec & Integrative Biology (T&R Staff) Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Pharmacology & Therapeutics Faculty of Science & Engineering > School of Engineering > Electrical Engineering and Electronics Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Artificial Intelligence |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Mar 2026 14:22 |
| Last Modified: | 17 Mar 2026 14:22 |
| DOI: | 10.2196/74304 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3197560 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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