Measuring the Immeasurable: Transforming Qualitative Practice Engagement Feedback into Medicines Optimisation Intelligence

NHS Nottingham & Nottinghamshire ICB

Project summary

Medicines optimisation teams generate significant qualitative intelligence during engagement with GP practices; however, this information is often informal, inconsistently recorded, and difficult to analyse systematically. Existing prescribing dashboards primarily measure outcomes and prescribing variation but provide limited insight into the operational realities influencing implementation, including workforce pressure, patient complexity, prescribing interface challenges, and barriers to change.

This project developed a scalable, AI-assisted approach to transform qualitative feedback from medicines optimisation pharmacist (MOP) annual GP practice prescribing review meetings into structured and analysable operational intelligence.

The Nottingham & Nottinghamshire medicines optimisation pharmacists, supported by MO technicians, offer structured annual prescribing review meetings to all practices. Data for the meetings is prepared by the prescribing informatics team using a standard template incorporating PrescQIPP and in-house reports. Themes include savings opportunities, significant safety issues, reducing variation, and “hot-topics”. Meetings are delivered face to face wherever possible and usually last approximately one hour. Uptake is consistently high.

Structured Excel-based feedback forms were completed following meetings and collated into a central dataset. Microsoft Copilot® was used to support initial thematic exploration, helping identify recurring themes and operational drivers. Visual Basic for Applications (VBA)-supported helper columns and keyword-based categorisation methods were then developed to support thematic classification, disagreement analysis, clinical area tagging, and broad year-on-year comparison.

The project enabled analysis of recurring prescribing challenges, workforce and system pressures, patient-centred barriers to optimisation, interface issues between primary and secondary care, and areas of incremental improvement.

Analysis demonstrated that prescribing variation is frequently driven by patient complexity, capacity constraints, and wider system pressures rather than lack of engagement with prescribing guidance.

The work also highlighted the value of medicines optimisation support in enabling safe, credible, and equitable prescribing improvement, particularly within practices managing high levels of deprivation, complex patient populations, and significant workload pressure.

Importantly, the project created a repeatable framework for capturing and analysing frontline qualitative insight at scale using readily available NHS tools.

The project represents an innovative use of AI-assisted analysis within medicines optimisation, helping convert previously unmeasured operational intelligence into actionable system insight while reducing analytical workload and supporting more informed service planning.