Leveraging Claude AI for Advanced ADR Detection and Pharmacovigilance: Innovations in Pharmacy Practice
Article Sidebar
Main Article Content
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.
Article statistics
- Abstract views
- 200
- PDF Downloads
- 46
- Total views
- 246
Downloads
Article Details
Data Availability Statement
none
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Transdisciplinary Medical, Clinical and Health Research (TMCHR) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows others to share, copy, distribute, and adapt the work, provided that proper credit is given to the original author(s) and the source.
Authors retain copyright and grant Transdisciplinary Medical, Clinical and Health Research (TMCHR) the right of first publication.
How to Cite
References
World Health Organization. (2002). The importance of pharmacovigilance: Safety monitoring of medicinal products. WHO.
Giacomini, M., Giannasi, G., Arzenton, E., & Barberan, J. (2019). The burden of adverse drug reactions in hospitalized patients. Expert Opinion on Drug Safety, 18(6), 531–541.
Pirmohamed, M., James, S., Meakin, S., Green, C., Scott, A. K., Walley, T. J., Farrar, K., Park, B. K., & Breckenridge, A. M. (2004). Adverse drug reactions as cause of admission to hospital: Prospective analysis of 18 820 patients. BMJ, 329(7456), 15–19. DOI: https://doi.org/10.1136/bmj.329.7456.15
Bates, D. W., Cullen, D. J., Laird, N., Petersen, L. A., Small, S. D., Servi, D., Laffel, G., Sweitzer, B. J., Shea, B. F., Hallisey, R., et al. (1995). Incidence of adverse drug events and potential adverse drug events. JAMA, 274(1), 29–34. DOI: https://doi.org/10.1001/jama.1995.03530010043033
Alomar, M. J. (2014). Factors affecting the development of adverse drug reactions. Saudi Pharmaceutical Journal, 22(2), 83–94. DOI: https://doi.org/10.1016/j.jsps.2013.02.003
Edwards, I. R., & Aronson, J. K. (2000). Adverse drug reactions: Definitions, diagnosis, and management. The Lancet, 356(9237), 1255–1259. DOI: https://doi.org/10.1016/S0140-6736(00)02799-9
Hazell, L., & Shakir, S. A. (2006). Under-reporting of adverse drug reactions: A systematic review. Drug Safety, 29(5), 385–396. DOI: https://doi.org/10.2165/00002018-200629050-00003
Lopez-Gonzalez, E., Herdeiro, M. T., & Figueiras, A. (2009). Determinants of under-reporting of adverse drug reactions: A systematic review. Drug Safety, 32(1), 19–31. DOI: https://doi.org/10.2165/00002018-200932010-00002
Coloma, P. M., Schuemie, M. J., Trifirò, G., Gini, R., Herings, R., Hippisley-Cox, J., Mazzaglia, G., Picelli, G., Corrao, G., Pedersen, L., et al. (2013). Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring. European Journal of Clinical Pharmacology, 69(3), 321–332.
Harper, L., Morrill, K., & Hennessy, S. (2021). Machine learning approaches to adverse drug event detection using electronic health records. Clinical Pharmacology & Therapeutics, 109(4), 876–886.
Wang, Y., Coiera, E., & Runciman, W. (2019). Adverse drug event detection using natural language processing from electronic health records: A systematic review. Drug Safety, 42(5), 721–732.
Anthropic. (2023). Claude: An AI assistant for tasks, conversations, and reasoning. (Technical overview).
Topol, E. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. DOI: https://doi.org/10.1038/s41591-018-0300-7
Mahendran, P., Harish, K., & Narayan, S. (2022). Artificial intelligence in clinical pharmacy services: Opportunities and challenges. Journal of Clinical Pharmacy and Therapeutics, 47(5), 706–716.
Ramesh, M., Pandit, J., & Parthasarathi, G. (2003). Adverse drug reactions in a South Indian hospital-Their severity and cost involved. Pharmacoepidemiology and Drug Safety, 12(8), 687–692. DOI: https://doi.org/10.1002/pds.871
Agarwal, R., Daher, A. M., & Ismail, N. M. (2016). Adverse drug reactions and their risk factors among hospitalized patients. International Journal of Clinical Pharmacy, 38(3), 616–621.
Kanjanarat, P., Winterstein, A. G., Johns, T. E., Hatton, R. C., Gonzalez-Rothi, R., & Segal, R. (2003). Nature of preventable adverse drug events in hospitals: A literature review. American Journal of Health-System Pharmacy, 60(17), 1750–1759. DOI: https://doi.org/10.1093/ajhp/60.17.1750
Bate, A., & Evans, S. J. (2009). Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiology and Drug Safety, 18(6), 427–436. DOI: https://doi.org/10.1002/pds.1742
Harpaz, R., DuMouchel, W., Schuemie, M., Onakpoya, I., Boyd, I., Ryan, P., Madigan, D., & Friedman, C. (2012). Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clinical Pharmacology & Therapeutics, 93(6), 539–546. DOI: https://doi.org/10.1038/clpt.2013.24
Harpaz, R., Wang, Y., & Chase, H. S. (2014). Extracting adverse drug reactions from electronic health records using NLP and structured data. Journal of the American Medical Informatics Association, 21(1), 43–52.
Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). AI for radiology and clinical decision-making: Opportunities and challenges. npj Digital Medicine, 6, 45.
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023). Large language models encode clinical knowledge. npj Digital Medicine, 6, 119. DOI: https://doi.org/10.1038/s41586-023-06291-2
Kaboli, P. J., Hoth, A. B., McClimon, B. J., & Schnipper, J. L. (2006). Clinical pharmacists and inpatient medical care: A systematic review. Archives of Internal Medicine, 166(9), 955–964. DOI: https://doi.org/10.1001/archinte.166.9.955
Rawson, T. M., Moore, L. S. P., Hernandez, B., Charani, E., Castro-Sanchez, E., & Holmes, A. H. (2021). A systematic review of the role of pharmacists in antimicrobial stewardship programmes. Journal of Antimicrobial Chemotherapy, 76(7), 1741–1750.
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310. DOI: https://doi.org/10.1186/s12911-020-01332-6
Naranjo, C. A., Busto, U., Sellers, E. M., Sandor, P., Ruiz, I., Roberts, E. A., Janecek, E., Domecq, C., & Greenblatt, D. J. (1981). A method for estimating the probability of adverse drug reactions. Clinical Pharmacology & Therapeutics, 30(2), 239–245. DOI: https://doi.org/10.1038/clpt.1981.154
Hartwig, S. C., Siegel, J., & Schneider, P. J. (1992). Preventability and severity assessment in reporting adverse drug reactions. American Journal of Hospital Pharmacy, 49(9), 2229–2232. DOI: https://doi.org/10.1093/ajhp/49.9.2229
Schumock, G. T., & Thornton, J. P. (1992). Focusing on the preventability of adverse drug reactions. Hospital Pharmacy, 27(6), 538.
Payne, T. H., Hines, L., Chan, R. C., Hartman, S., Kapusnik-Uner, J., Russ, A. L., & Tamis, J. E. (2015). Recommendations to improve the usability of drug–drug interaction clinical decision support alerts. Journal of the American Medical Informatics Association, 22(6), 1243–1250. DOI: https://doi.org/10.1093/jamia/ocv011
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In I. G. Cohen et al. (Eds.), Big Data, Health Law, and Bioethics (pp. 295–336). Cambridge University Press. DOI: https://doi.org/10.1016/B978-0-12-818438-7.00012-5