Leveraging Claude AI for Advanced ADR Detection and Pharmacovigilance: Innovations in Pharmacy Practice
Abstract
Background: Adverse drug reactions (ADRs) continue to be a leading source of morbidity, hospitalization, hospital stay, and avoidable health expenditure. The conventional pharmacovigilance process has relied heavily upon spontaneous, manual reporting, prone to the following limitations: under-reporting, delayed reporting, fragmentation in clinical documentation, and a lack of feedback to the prescribing physician. Recent breakthroughs in the application of artificial intelligence (AI) in the form of large language models, such as the Claude AI model, have enabled the instant analysis of EHR, clinical free text, and patient self-reported outcomes. Aim and Objectives: The aim of the present study is to assess (1) the application of Claude AI for the identification of ADRs through the use of Predictive Analytics and NLP, (2) the usefulness of Claude AI for real-time documentation of ADR reporting, and (3) the effect of pharmacovigilance using AI on patient safety. Methodology: A prospective observational study was conducted over 6 months in a tertiary-care teaching hospital with 220 in patients receiving polypharmacy. Claude AI was integrated with hospital EHRs and a custom ADR chatbot interface. The AI system continuously analysed medication orders, laboratory values, clinical progress notes, and patient-reported symptoms. Potential ADRs flagged by Claude AI were reviewed and validated by clinical pharmacists using standard causality, severity, and preventability scales. Pharmacist interventions and outcomes were documented. Descriptive statistics and inferential analyses were performed to compare ADR detection, reporting time, and intervention outcomes before and after AI integration. Results: Claude AI performed at 85.0% accuracy for ADR detection, with a 88.5% sensitivity rate and 82.3% specificity rate for validated cases by a licensed pharmacist. The median time to ADR detection occurred 60% faster than the traditional time for documented ADRs. AI-assisted counselling and notifications increased the rate of adherence to safety procedures by 40% while also increasing the incorporation of ADR data completeness by 50%. Of the 220 patients, 82 (or 37.3%) experienced at least one ADR event. A total of 134 ADRs were recorded. AI-assisted pharmacovigilance enabled more timely reaction to ADRs while preventing the progression to serious ADRs. Conclusion: Claude AI has shown substantial promise for revolutionizing the performance of electronic surveillance for ADRs in pharmacovigilance. Incorporation of Claude AI into current pharmacy practices can help in promoting early detection of harmful outcomes of medications while optimizing clinical decisions for safe practices in pharmacovigilance. Further studies in this domain are essential for ascertaining the positive implications of using Claude AI in pharmacovigilance practices for the future.