Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy

Journal of Pharmaceutical Policy and Practice

Jose Luis Poveda¹  Carlos Del Rio-Bermudez²  Ignacio H. Medrano²  Laura Yebes² 

[1] Pharmacy Department, Drug Clinical Area, University and Polytechnic Hospital La Fe, Avda. Fernando Abril Martorell 106, 46026, Valencia, Spain;Savana Medica, Madrid, Spain;

关键词: Natural language processing;    Electronic health records;    Machine learning;    Pharmacovigilance;   

DOI  :  10.1186/s40545-020-00276-6

来源: Springer

【 摘 要 】

The digitalization of health and medicine and the growing availability of electronic health records (EHRs) has encouraged healthcare professionals and clinical researchers to adopt cutting-edge methodologies in the realms of artificial intelligence (AI) and big data analytics to exploit existing large medical databases. In Hospital and Health System pharmacies, the application of natural language processing (NLP) and machine learning to access and analyze the unstructured, free-text information captured in millions of EHRs (e.g., medication safety, patients’ medication history, adverse drug reactions, interactions, medication errors, therapeutic outcomes, and pharmacokinetic consultations) may become an essential tool to improve patient care and perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs. This approach has an enormous potential to support share-risk agreements and guide decision-making in pharmacy and therapeutics (P&T) Committees.

【 授权许可】

CC BY