Using data and analytics for care management in medical homes

Less than five years ago, a Minneapolis-based health system went all-in to the medical home model, with great results.

The HealthEast Care System is the largest health care provider in the East Metro area of Minnesota’s Twin Cities. In addition to its four hospitals, HealthEast runs 14 primary care clinics, a variety of specialty care clinics, outpatient services, home and hospice care, medical transportation, and other services.

In 2008, Minnesota passed a health reform law that included a provision for the establishment of “Health Care Homes” (HCHs), a medical home approach to health care in which primary care providers and chronically ill and disabled patients work together to improve the health and quality of care provided them. After piloting the medical home model in 2010, HealthEast determined that medical homes could be a great benefit to the patients they served. By 2012, all of HealthEast’s ambulatory clinics had become state-certified HCHs.

Care coordination and quality measures are key ingredients of successful medical homes, and the Minnesota HCH program included stipulations for both. Minnesota requires their state-certified medical homes to submit quality measures for depression remission, diabetes care, colorectal cancer screenings, vascular care and asthma care. The state also developed a payment methodology that allows HCH programs to be reimbursed for care coordination activities.

HealthEast’s overall strategy to help their chronically ill patients centered on coordinating care for “high utilizers,” patients who used the most inpatient care, and patients with depression. Multiple studies have found that depression as a co-morbidity to other chronic illnesses can worsen outcomes. To put their strategy into practice, HealthEast needed to find the high utilizers and the patients with a clinical diagnosis of depression. Finding these required a tool that could not only identify previous utilization within the data, but also analyze the data predict who would become high utilizers in the future. Plus, the tool needed to help them comply with the various state reporting requirements.

With the aid of Optum One’s comprehensive data sets and highly accurate analytics, HealthEast has reduced the rate of emergency room visits by high-risk populations by an average of 30 percent and reduced hospitalizations by an average of 39 percent.

The next blog in this series will discuss how HealthEast applied data and analytics to get these great results. To download the full HealthEast case study, click here.

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