Predictive Modeling for Complex Care Management

Complex care management (CCM) or hot spotting programs identify and manage high-need/high-cost patients, improving long-term health quality and medical costs. Typically, physicians refer patients to CCM. Despite strict guidelines to ensure that eligible patients are placed in appropriate programs, such a provider-based approach is limited by provider-capacity and the narrow view of a patient that a provider sees. We propose an ML workflow to augment the provider-based approach, that can flag patients who are suited to CCM. Our predictor uses a global view of a patient’s entire history across multiple providers and time to identify high-risk individuals from among all the individuals in a matter of seconds. On a monthly basis, we evaluate our predictions against physician referrals. In the test dataset, 41% of the top-500 highest risk individuals found by our model were referred to CCM by a physician at some point in the 6-month window following our prediction (top-500 is a parameter that can be set to match the CCM program’s capacity). Of those who were not referred in the 6-month window, 30% were referred at some time in their trajectory. The remaining false positives had a greater than 95% similarity when compared to true positive physician referrals in terms of cost profiles (both prior to referral and after referral) and patient profile. This remarkable similarity suggests that our machine learning predictor can identify new candidates for complex care management and/or predict referrals before a physician has an opportunity to do so.