Abstract
As part of studies that employ health electronic records databases, this paper advocates the employment of graph theory for investigating drug-switching behaviors. Unlike the shared approach in this field (comparing groups that have switched with control groups), network theory can provide information about actual switching behavior patterns. After a brief and simple introduction to fundamental concepts of network theory, here we present (i) a Python script to obtain an adjacency matrix from a records database and (ii) an illustrative example of the application of network theory basic concepts to investigate drug-switching behaviors. Further potentialities of network theory (weighted matrices and the use of clustering algorithms), along with the generalization of these methods to other kinds of switching behaviors beyond drug switching, are discussed.
Highlights
Information and Communication Technologies (ICTs) are major players in society nowadays
This paper aimed to promote the use of network theory as a method to investigate switching behaviors
Network theory may represent a useful tool for analyzing switching behavior for healthcare researchers
Summary
Information and Communication Technologies (ICTs) are major players in society nowadays. Health and medical services have been radically transformed by the ICT progress over recent decades. In this regard, a pivotal role has been played by health care databases, which contain information about individuals’. ICTs allow continuously and automatically updating administrative databases as soon as an event takes place (the prescription of a drug made by a physician, medical procedures, diagnoses information, records of health services, etc.). We will focus on analyzing electronic health records database data related to drug prescription and, drug switching. We propose the use of network theory to improve the analyses of these kinds of data. We provide a step-bystep user guide, along with a ready-to-use Python script for obtaining an adjacency matrix from raw data
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