Does Real Time Insurance Claims-Based Analysis Impact Population Health?

The fast changing healthcare landscape is often characterized by a shift from fee-for-service with an emphasis on volume to a value-based reimbursement structure with an emphasis on population health management. To thrive in this new environment, providers must be proactive (anticipate problems before they arise) as opposed to reactive (be there to escalate care when an individual patient’s condition deteriorates). To support proactive clinical decision-making, providers must have and rely on effective, real time, risk stratification of their patient population. However, even with a primary care-led clinical workforce, the provider may lack access to healthcare utilization information and thus the ability to stratify their patients based on risk. This lack of information may impede processes for patient engagement and designs for community integration, critical to success in a value-based environment.

This paper evaluates the success of an insurer-driven intervention to reduce hospitalizations among their fully-insured Medicare Advantage member population who were diagnosed with Congestive Heart Failure. The insurer implemented a proprietary algorithm using claims data to predict patient-level likelihood of hospitalization (LOH). Patients with high LOH scores were contacted and offered enrollment in a care management program. The care management program involved the assignment of health coaches to help patients manage their chronic condition, close gaps in care, and provide a single point of contact leveraging health plan social workers, medical directors, and pharmacists. The algorithm was applied in 10 waves between July, 2013 and May, 2015. Each patient was assigned an LOH score at the time the algorithm was ran and ranked from high to low. In each wave, the care management team dictated to the insurer how many patients they can approach, based on their resource availability and capacity. This created a set of arbitrary cutoff points for each wave, separating treated and untreated members with very similar predicted LOH scores.

We observe both the intent-to-treat group (patients approached by the care management team), as well as, those who were enrolled in the program. We utilize a fuzzy regression discontinuity design with multiple waves to evaluate the effectiveness of the insurer-driven care management strategy. Our outcome measures include: all hospitalizations, cardiac-related hospitalizations, length-of-stay, emergency department visits, primary care visits, medication adherence, and cost. This analysis will evaluate both the success of these LOH algorithms in predicting downstream health care utilization and the success of the care management intervention.