The data was then extracted into standardized tables. Two researchers assessed all search results independently to identify eligible studies. All studies evaluating big data analytics about patients consuming multiple drugs were considered. The search strategy was defined by the principles of a systematic search, using the PICO scheme. MethodsĪ computerized search was conducted in February 2019 and updated in May 2020, using the PubMed, Web of Science and Cochrane Library databases. The aim of this review was to evaluate whether there are existing big data analysis techniques that can help to identify patients consuming multiple drugs and to assist in the reduction of polypharmacy in patients. It is hypothesized that big data analysis is able to reveal patterns in patient data that would not be identifiable using conventional methods of data analysis. In the following we use the term analysis of big data as referring to the computational analysis of large data sets to find patterns, trends, and associations in large data sets collected from a wide range of sources in contrast to using classical statistics programs. eHealth solutions are increasingly recommended in healthcare, with big data analysis techniques as a major component. The use of multiple medications increases the potential for drug interactions and for prescription of potentially inappropriate medications. Polypharmacy is a key challenge in healthcare especially in older and multimorbid patients.
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