Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy.
Carlos González-JuanateyIgnacio H MedranoLaura YebesJose Luis PovedaPublished in: Journal of pharmaceutical policy and practice (2020)
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.
Keyphrases
- adverse drug
- big data
- artificial intelligence
- electronic health record
- machine learning
- deep learning
- healthcare
- emergency department
- end stage renal disease
- drug induced
- newly diagnosed
- clinical decision support
- ejection fraction
- chronic kidney disease
- public health
- health information
- prognostic factors
- type diabetes
- human health
- autism spectrum disorder
- adipose tissue
- primary care
- risk assessment
- metabolic syndrome
- weight loss
- patient reported