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Effective population health management requires collecting, processing, and analyzing large amounts of patient data, making data analytics tools key for any population health initiative.
Population health is often conceptualized based on a definition in a review published in the March 2003 issue of the American Journal of Public Health, which states that the term refers to “the health outcomes of a group of individuals, including the distribution of such outcomes within the group.”
The authors further posited that population health encompasses “health outcomes, patterns of health determinants, and policies and interventions that link these two.”
The American Hospital Association (AHA), as part of its Population Health Framework, indicates that leveraging robust data sources and advanced quantitative analytics solutions are crucial to understanding these patterns at the population level.
As of 2018, the AHA Population Health Survey shows that a majority of health systems are incorporating strategies to advance population health, but many still have work to do.
Below, HealthITAnalytics details the various data analytics tools that can be used alongside other solutions, like patient engagement technologies, for population health management. In addition, artificial intelligence (AI) and machine learning (ML) can, and often are, incorporated into all of the tools listed below. These technologies can support a vast number of population health-related use cases, including medication adherence, chronic disease management, mental healthcare, precision medicine, person-centered care for seniors, and flagging patient SDOH needs.