JMIR Publications

ABSTRACT

Background:

Background:

Structured Medication Reviews (SMRs) are an essential component of medication optimisation, 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 between healthcare professionals (HCPs). This study explores HCPs perspectives on the integration of AI-assisted tools to enhance the SMR process, with a focus on the potential benefits and barriers to adoption.

Objective:

Objective:

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:

Methods:

A qualitative study was conducted involving focus groups and semi-structured interviews with HCPs and patients in the UK. Participants included doctors, pharmacists, clinical pharmacologists, psychiatrists from primary and secondary care, a policy maker, and patients with multimorbidity. Data were analysed using a hybrid inductive and deductive thematic analysis approach to identify themes related to AI-assisted tool functionality, workflow integration, user-interface visualisation, and usability in the SMR process.

Results:

Results:

Four major themes emerged from the analysis: Innovative AI Potential; Optimising Electronic Patient Record Visualisation; Functionality of the AI tool for SMRs; Facilitators and Barriers to AI Tool Implementation. HCPs identified the potential of AI to support patient identification and prioritising 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 emphasised the importance of presenting patient data in an intuitive format, with a patient interface for shared-decision making. Suggestions included colour-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 sceptical about the predictive accuracy of AI models, and addressing concerns around digital exclusion.

Conclusions:

Conclusion: The findings highlight the potential for AI-assisted tools to streamline and optimise 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-centred design is crucial to ensure that AI-assisted tools support HCPs in delivering high-quality, patient-centred care while minimising cognitive overload and alert fatigue.