From Pharmacovigilance to Clinical Care Optimization

Autor: Kai-ou Tang, David J. Stone, Christopher Moses, Melek Somai, Edward T. Moseley, Leo Anthony Celi, Padhraig Ryan
Rok vydání: 2014
Předmět:
Zdroj: Big Data. 2:134-141
ISSN: 2167-647X
2167-6461
DOI: 10.1089/big.2014.0008
Popis: In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies. Even well-conducted, randomized controlled trials are likely to have excluded a large proportion of individuals because of any number of issues. The digitization of medical care, which yields rich and accessible drug data amenable to analytic techniques, provides an opportunity to capture the required information via observational studies. We propose the development of an open, accessible database containing properly de-identified data, to provide the substrate for the required improvement in pharmacovigilance. A range of stakeholders could use this to identify delayed and low-frequency adverse events. Moreover, its power as a research tool could extend to the detection of complex interactions, potential novel uses, and subtle subpopulation effects. This far-reaching potential is demonstrated by our experience with the open Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) intensive care unit database. The new database could also inform the development of objective, robust clinical practice guidelines. Careful systematization and deliberate standardization of a fully digitized pharmacovigilance process is likely to save both time and resources for healthcare in general.
Databáze: OpenAIRE