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Updated: Sep 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A pilot study to identify elderly patients with cognitive impairment for clinical pharmacist polypharmacy review in
1NHS Ayrshire and Arran, United Kingdom.
Background:
Polypharmacy in elderly patients is common with potential for harm. Cognitive impairment is postulated as the biggest contributor to poor medication management with increased risk of hospital admission. There is limited information about approaches to identify high risk patients for polypharmacy review.
Objective:
Pilot study to determine if a new patient prioritisation tool would identify appropriate patients for pharmacist polypharmacy review.
Method:
Prioritisation tool developed to rank community-dwelling elderly patients prescribed 10 or more medications with cognitive impairment for pharmacist polypharmacy review. Tool used General Practice (GP) appointments, Emergency Department attendances, repeat medications and cognitive impairment to create a score to prioritise review invitations. Reviews were completed by GP clinical pharmacists who recorded interventions and measured outcome assessments using the adapted RiO scoring tool.
Results:
Polypharmacy reviews completed for 34 patients from three GP practices. Demographic results were 62% female (n = 21), median 78 years [IQR 72-80], median 3 comorbidities [IQR 2-4] with most reviews conducted face-to-face (n = 29; 85%). Pharmaceutical care interventions were hospital admission possible or likely prevention for the majority of patients (85%, n = 29) which contrasts with the historical level of 33% (n = 228) patients with traditional processes.
Conclusion:
Pilot study demonstrated that the new tool identified appropriate patients for review prioritisation as patients had complex pharmaceutical care needs.
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