A more technical post for those evaluating risk scores for care management, coupled with a real-world example. As healthcare moves toward taking on greater financial risk for keeping people healthy, it is critical for organizations to match people to the interventions they’re most likely to benefit from. This has traditionally meant using various risk scores that are based on claims data.
These days, every care management / value-based care organization has a risk score to help target interventions. Unfortunately, these risk scores often frustrate clinicians by directing them to people who cannot benefit from an intervention – either the person is not actually headed for trouble, or the clinician already knew about that person. Why is that? It turns out most
Healthcare is notorious for its lack of consistent and widely adopted data formats. The one consistent exception is the billing information exchanged between payors and providers. These files are often referred to as “claims.” Because of their ubiquity, many of today’s analytical approaches - from epidemiology to public health, actuarial sciences, business intelligence, and risk scores - rely heavily,
Today, June 12, 2017, Children's Mercy Kansas City, Joslin Diabetes Center, Cyft Inc., and The Leona M. and Harry B. Helmsley Charitable Trust are proud to announce the creation of a new learning health system to improve the care of individuals diagnosed with type 1 diabetes (T1D). Starting in mid-2017, Children's Mercy and Joslin will deploy machine learning-enabled solutions to