Billy Beane, the man made famous by Moneyball, didn’t turn the Oakland Athletics into contenders by following standard measurables — home runs, RBI, etc. — for professional baseball players. Instead, he focused on little-used metrics that made a big difference in wins and losses. He created a plan and stuck to it. And he changed the Major League Baseball (MLB) club’s culture to trust the “moneyball” strategy of using data and analytics to draft players who could help them win.
Health care organizations also need to look beyond the obvious to better focus attention on the levers that can most meaningfully improve performance. For example, simply tracking historical hospitalizations — or even using predictive methods to identify those at high risk for future ones — will identify a mix of patients with both modifiable and relatively difficult to modify, end-stage disease. By looking at complementary metrics, such as overall co-morbidity burden, functional status, and biometric or lab values related to the patient’s disease (ejection fraction, measures of kidney function, etc.), we can better identify patients who are likely to benefit from our interventions.
Other less-used metrics with particular value include rate of conversion to stage IV or V renal failure in diabetics, ratio of co-morbid depression in chronic disease, and lability of blood pressure and HgA1c control in people with hypertension and diabetes, respectively. Psychosocial risk factors are also highly predictive of outcomes, yet organizations are only starting to invest in systematically capturing and organizing such information for risk stratification purposes.
Once these metrics are identified, providers must ensure all staff understand what the metrics represent and train clinical staff to focus on what is preventable and modifiable. For example, clinicians are often surprised to learn that rates of co-morbid depression in high-risk, chronically ill populations regularly exceed 50 percent. When new information catalyzes a discussion between physicians, nurses and other supporting staff about how to respond, it has the power to prompt new ideas — like universal depression screening in the waiting room — that can improve patient care.
For more information on building a blueprint based on data and analytics, download Optum’s Moneyball Analytics eBook by clicking here.
In our next post, we’ll talk about how improving patient care through data and analytics is a journey that never really ends.
About the Author:
Alejandro Reti, MD, MBA
Chief Medical Officer, Optum Analytics
With responsibility for the Office of the CMO, Alejandro is accountable for the clinical integrity and relevance of Optum Analytics’ provider solutions and contributes to thought leadership and clinical product innovation for the organization. Alejandro came to Optum from Premier, where he served as Vice President, Population Health Products with general management responsibility for Premier’s organically developed population health suite. Prior to Premier, Alejandro served as Senior Vice President, Clinical Informatics at Verisk Health, where he led development of a provider analytics solution that achieved top 4 in market share nationally. Previously, Alejandro held positions of increasing responsibility at Avalere Health and The Advisory Board Company. Alejandro received a bachelor’s degree in Psychology from Amherst College, magna cum laude and his MD and MBA degrees from Yale University.