According to the Harvard Business Review, “half of U.S. spending on healthcare goes to treating the sickest 5% of the population.” [1] Many high-cost, high-acuity individuals are diagnosed with both physical and behavioral health conditions. Therefore, care management initiatives designed to lower cost and improve outcomes need to focus on both physical and behavioral care. Risk stratification models play an important role in assigning the right resources to the highest risk clients in care management initiatives. One of the keys to a successful risk stratification model is accessing the right data, at the right time, and stratifying risk to prevent gaps in care and clients from falling through the proverbial cracks. This includes data from payors, EHRs, HIEs, and CCDs from other entities across a healthcare ecosystem, which is aggregated in a population health solution. The goal of a population health solution is to help organizations understand “who are my patients/clients and what does my patient/client panel look like?” There are several unique considerations for CCBHCs and other behavioral health organizations when it comes to answering these questions. The first is identifying behavioral health client cohorts based on the program or programs in which clients are actively participating. Once the cohort is identified, clinical, financial, and operational analytics can be applied to identify whether everyone in the cohort has been screened for depression, what their emergency department utilization has been, as well as their social determinants of health (SDoH), missed medication refills, or “no-show” appointments. Second, is grouping providers based on care team structure and program, as opposed to specialty and location as is typical in physical medicine. Another important consideration in behavioral health is attribution, which is far more nuanced than the primary care or sub-specialty attribution common in physical medicine. The problem of payors attributing clients only to their primary care provider also creates issues because claims data received by payors can be incomplete and missing critical information.