Big Data, Little Data, and Care Coordination for Medicare Beneficiaries with Medigap Coverage

Abstract

Most healthcare data warehouses include big data such as health plan, medical, and pharmacy claims information for many thousands and sometimes millions of insured individuals. This makes it possible to identify those with multiple chronic conditions who may benefit from participation in care coordination programs meant to improve their health. The objective of this article is to describe how large databases, including individual and claims data, and other, smaller types of data from surveys and personal interviews, are used to support a care coordination program. The program described in this study was implemented for adults who are generally 65 years of age or older and have an AARP(®) Medicare Supplement Insurance Plan (i.e., a Medigap plan) insured by UnitedHealthcare Insurance Company (or, for New York residents, UnitedHealthcare Insurance Company of New York). Individual and claims data were used first to calculate risk scores that were then utilized to identify the majority of individuals who were qualified for program participation. For efficient use of time and resources, propensity to succeed modeling was used to prioritize referrals based upon their predicted probabilities of (1) engaging in the care coordination program, (2) saving money once engaged, and (3) receiving higher quality of care. To date, program evaluations have reported positive returns on investment and improved quality of healthcare among program participants. In conclusion, the use of data sources big and small can help guide program operations and determine if care coordination programs are working to help older adults live healthier lives.

Keywords: big data analytics; case management; data acquisition and cleaning; medicare; predictive analytics.