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Treating Chronic Disease with Data

Amit Bhagat

Does that headline feel hyperbolic? If so, I’m not terribly surprised. For many people—healthcare providers and patients alike—the term “treatment” has a concrete connection to medication, medical equipment and other traditionally understood forms of medical intervention. This is especially true as it relates to the management of chronic diseases like diabetes and obesity that are now widespread enough to qualify as true public health crises.

For diabetes in particular, the challenge is particularly great.  In the U.S. alone, 9.4% of the population has been diagnosed at a rate of 1.4 million people per year. As the seventh leading cause of death in the U.S., the figurative cost of diabetes is as high as the literal cost, which tops out at a staggering $245 billion annually in medical care and lost wages.

But even for a public health problem as large and overwhelming as diabetes, data does indeed meet the definition of a bonafide treatment option, serving as a powerful supplement to support clinical decision-making and solve complex challenges in healthcare. As per usual, it’s a point best-proven by example.

A New Approach in Arizona
The state of Arizona joins the rest of the nation in a growing struggle to manage the care and costs of diabetes. According to the Arizona Department of Health Services, nearly 600,000 Arizonans or 9% of the adult population have been diagnosed. In 2006, there were 1755 diabetes-related hospital stays per 100,000 people in Arizona–170 more than any other Western state—the aggregate costs of which totaled more than $93 million.

As the oldest and largest non-profit health care provider in Southern Arizona, Carondelet Health Network serves a diverse population of clients including high-risk minorities for whom access to quality care and chronic disease management presents a special challenge. With one in three patients admitted to their hospitals presenting as diagnosed diabetics, new and innovative ideas to manage treatment and corresponding costs were in perpetually high-demand.

To test a more pro-active and thereby more cost-effective management approach to the care for this high-risk population, Carondelet Medical Group worked with Amitech to develop an application designed to assess risks of diabetic patients upon admission to the hospital. The Diabetes ScoreCard Program used a proprietary risk scoring algorithm to stratify patients based on clinical data and behavioral survey questions. This score was tied directly to a pre-determined clinical pathway that involved a multidisciplinary, bilingual team of diabetes nurses, dietitians and promotoras (outreach workers).

Data Makes the Difference
At the end of the pilot period, patients enrolled in the Diabetes ScoreCard Program received better care than Carondelet Medical Group’s Routine Care patients (based on standard performance measures) at a lower total cost of care. In fact, net estimated savings in Year 1 to the health plan for the ScoreCard program totaled $21,800 per enrolled member.

In the same way that the Diabetes ScoreCard Program was able to help Carondelet make more informed decisions on resource use, provide better experiences and outcomes for patients and reduce the overall cost of care, so too can data and predictive analytics add value to the fight against chronic diseases around the globe.  Please visit our website to read the full case study and explore other project-based examples that demonstrate the power of data to effect change in the healthcare industry.