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As healthcare organizations gather more clinical data, extracting value from that information has become a top priority. However, to make this data actionable for research and development, health systems must collect, process and mine it to identify trends and patterns that could advance strategic goals.The data mining process is key to generating insights that drive healthcare analytics projects, but it is only the beginning. After datasets are created and mined, stakeholders may also want to pursue electronic phenotyping — a process that can help bolster clinical and translational research efforts by allowing users to query healthcare data.This primer will explore electronic phenotyping, its potential and pitfalls in healthcare and potential use cases.Penn Medicine indicates that “electronic phenotyping involves querying electronic health record systems (EHRs) and other clinical information systems and databases to identify patients who meet a specific set of clinical characteristics.”
The National Institutes of Health (NIH) Pragmatic Trials Collaboratory notes that “in the context of [EHRs], a ‘computable phenotype,’ or simply ‘phenotype,’ is a clinical condition or characteristic that can be ascertained by means of a computerized query to an EHR system or clinical data repository using a defined set of data elements and logical expressions. These queries can identify patients with particular conditions and can be used to support a variety of purposes, including population management, quality measurement, and observational and interventional research.”
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