Abstract
This issue of eGEMS focuses on application of data science as a driver of health care transformation. Importantly, quantitative or qualitative analysis with a particular method is only one downstream step in the process of leveraging data. Effective analytics occurs on a continuum with multiple complementary phases, categorized here as data acquisition, ensuring or enhancing data access and usability, data analysis, and dissemination. Each of these activities is encompassed in the series of papers presented.
Highlights
Converging demographic, epidemiologic, and economic trends have created an imperative to accelerate health care transformation efforts in the United States
Acquired data must be usable as substrate in a formative event-conversion of data into knowledge and actionable insights through analytics
The learning health system model is an example of a transformational framework underpinned by data science
Summary
Converging demographic, epidemiologic, and economic trends have created an imperative to accelerate health care transformation efforts in the United States. There is still equipoise regarding selection and deployment of specific, scalable models to transform health care, the field of data science (methods, processes, and systems for leveraging data), is a common thread in achieving that goal. The data leading to actionable insights can be derived both from day-to-day processes (e.g., clinic visits, billing) and specialized activities (e.g., quality improvement, research).
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