Abstract
Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. Many different types of data may be used to guide population health management programs and to estimate program value. In addition to the variety of data required for these programs, big population health program data are characterized by large volume, high velocity, and inconsistent data flows. This manuscript describes how big data analytics have been used to craft a population health program to help improve the lives of about four million older adults who have an AARP® Medicare Supplement Insurance plan (i.e., a Medigap plan). Plan enrollees have access to a wellness program, holistic care coordination programs, two telephone-based advice lines, concierge support for insurance and medical care needs, and a program designed to help reduce unnecessary emergency room (ER) visits. During 2009–2011, these program components led to several improvements in health care. For example, increased duration in care coordination was associated with fewer hospital readmissions, and participants were significantly more likely to have recurring physician office visits and recommended laboratory tests. Participants in ER decision support reduced their ER visits by 1299 visits per 1000 insureds, compared with a reduction of 1121 visits per 1000 insureds for individuals who did not participate in the program. Better depression management helped reduce depression symptoms in 59 % of participants engaged in that program. Big data analytics of member data suggested the need for a wellness program feature, which began in 2014. Analytics of disease management services offered in 2009–2011 helped to combine and refocus these program features to enhance effectiveness in later years. Using big data to help manage and evaluate a population health program has led to several improvements in health care. Program management, reporting, and evaluation processes generated additional data which, when analyzed, continues to refine program implementation and quality. Future improvements to this program may include enhanced integration of social service programs that will generate their own data streams for analyses designed to further improve health and wellbeing.
Highlights
Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior
A good population health management approach takes into account the multiple determinants of health, including the adequacy of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior [2]
This paper demonstrates how big data collection and analytic processes allow researchers and program managers to focus on a broad set of population health management program outcomes with such data
Summary
Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. In addition to the variety of data required for these programs, big population health program data are characterized by large volume, high velocity, and inconsistent data flows This manuscript describes how big data analytics have been used to craft a population health program to help improve the lives of about four million older adults who have an AARP® Medicare Supplement Insurance plan (i.e., a Medigap plan). A good population health management approach takes into account the multiple determinants of health, including the adequacy of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior [2]. We describe how this can work for those who have traditional fee-for-service Medicare coverage along with a Medicare Supplement Insurance (i.e., Medigap) plan
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