Speaker
Description
Background: Medication errors are a major cause of preventable patient harm, yet the true incidence is underestimated because traditional reporting systems rely on retrospective, paper-based documentation prone to underreporting and delayed detection. Pharmacists frequently detect clinically significant prescribing errors, yet these interventions go largely undocumented. Digitally enabled real-time surveillance offers a proactive alternative by standardizing reporting and enabling immediate analysis of prescribing trends. This study evaluates a pharmacist-led digital real-time surveillance model to strengthen medication safety and support data-driven quality improvement.
Objective: To determine the prevalence, types, and severity of prescription errors intercepted by pharmacists and to assess the feasibility of a pharmacist-led, real-time digital surveillance tool in standardizing error documentation and supporting timely analysis of prescribing trends.
Methods: This prospective observational study will be conducted over one month at Mathari National Teaching and Referral Hospital’s main pharmacy. Pharmacists will identify and document prescription errors in real time as prescriptions are processed. Data will be captured using a structured digital tool, standardizing reporting and enabling real-time data entry at the dispensing stage. Variables will include error type (WHO-adapted classification), severity, pharmacist interventions, and potential clinical impact of intercepted errors. The system incorporates a real-time analytics dashboard visualizing prescribing trends across departments, drug categories, and severity levels. From an estimated 7,500 monthly prescriptions and a conservative error rate of 7.5%, the expected error yield is 563. Applying Yamane’s (1967) formula at 95% confidence and 5% margin of error, a minimum of 250 prescription errors will be analyzed for meaningful subgroup analysis across error types, severity levels, and departments.
Expected Results: A higher prevalence of prescription errors is anticipated compared to conventional reporting systems. Most errors are expected to be intercepted by pharmacists before reaching patients, demonstrating the clinical impact of pharmacist-led surveillance. The digital approach should improve reporting completeness and early identification of high-risk prescription errors.
Conclusion: Prescription errors are anticipated to occur at rates much higher than those captured by traditional reporting systems. Combining pharmacist-led real-time observation with digitally enabled surveillance and analytics is a feasible approach to strengthening medication safety.