How CCBHCs Can Identify and Manage High-Risk, High-Cost Clients for Better Outcomes

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.

Care management teams need a solution that stratifies risk with industry leading algorithms that utilize risk markers.

What CCBHCs need in a population health solution

No two organizations are the same, so there is a fine balance needed between standardization and flexibility. Also, every state has very specific care management and/or population health requirements and standards. CCBHCs need population health solutions that group clients and providers in a meaningful way, specific to the needs of their providers as well as the needs of their program administrators, clinical leaders, and quality directors. CCBHCs and their provider teams need insights from a population health solution that lead to action—whether at the point of care, in their EHR workflow, or with a specific client cohort. The value is in the outreach efforts to reduce relapse and negative outcomes by making data-informed outreach decisions based upon risk markers, gaps in care, and other data across an ecosystem.

On the other hand, program administrators and quality teams need population health solutions to provide a broad view of the organization’s client population and their demographics, identifying and understanding their high-risk client cohorts, and clinical and financial analytics to measure outcomes. One particular challenge for program administrators is how to define high-risk client cohorts. Some group clients are based on having multiple conditions or on a combination of physical and behavioral health diagnoses and social determinants of health. Others focus on clients recently discharged from an in-patient setting or clients with a history of missing follow-up appointments.

There are many ways that SDoH data can get into a population health tool and benefit both CCBHCs and their clients. It can come from an EHR (e.g., from demographics, problem lists, or assessments such as PRAPARE), it can come from claims, and it can even come from a datamart. This data can be invaluable in helping to accurately assess risk and identify high-risk client cohorts.

Risk stratification for CCBHCs

The importance of risk stratification is to understand where to focus healthcare resources to get people access to the right resources when they need them. There are all kinds of ways to define high-risk populations. Risk stratification algorithms are designed to do this; however, there is a lack of validated risk stratification algorithms designed specifically for behavioral health. The good news is that the Johns Hopkins ACG® System has the potential to generate retrospective and prospective risk scores relevant for CCBHCs and other behavioral health organizations using markers, such as psychiatric conditions or combinations of conditions, severe mental illness, pharmacy data, and utilization of behavioral health services. There are other risk stratification models that can be leveraged for behavioral health populations, but the industry needs a better, more robust gold standard model that has very specific risk markers and algorithms.

Best Practices for selecting the right population health solution for your CCBHC

One of the most important considerations when selecting a population health solution for your CCBHC is the right balance of standardization and flexibility. You need the flexibility to identify client cohorts and group provider teams in a way that makes the most sense for your organization. You also want a solution that allows you to look at clients by behavioral health condition(s) and a risk stratification model that works for you.

Most importantly, population health data is most valuable when it’s actionable—meaning it’s integrated with your EHR and available in your providers’ workflows so they can take meaningful action based on the information.

For more information on how NextGen Healthcare can help support your CCBHC, please visit our website.

[1] Robert Pearl and Philip Madvig. “Managing the Most Expensive Patients,” https://hbr.org/2020/01/managing-the-most- expensive-patients, January – February 2022 issue.