It’s Hard to Operationalize Population Health Well, but It’s Not Impossible

Posted in Feature Articles

Population health has been a healthcare industry buzzword for over a decade and many organizations have tried to transform themselves to achieve its promise. Although some have succeeded, many others have not.

Becker’s Hospital Review recently spoke to Jonas Varnum, vice president of population health strategic services at Health Catalyst, about the complexities associated with operationalizing population health and keys to success.

Market Dynamics and Organizational Issues Make Population Health Challenging

There is a continued inability for providers to gain enough market volume to be successful at scale with population health. However, it’s not necessarily the fault of provider organizations. According to Mr. Varnum, “We continue to see payers struggle to offer providers a consistent glide path for taking on risk. CMS, for example, has tried several different models, but it recently stated that only six have generated enough savings to merit expansion. In addition, many payer models are overly complex.”

Another challenge is that providers often need to change their care delivery structure before they can accept downside risk. In many cases, providers don’t have the tools required to make those changes. The good news is that CMS has acknowledged the need for waivers and operational tools for providers. The bad news is that CMS is only one payer among many.

From an organizational perspective, health systems and care delivery networks must make a concerted effort to succeed with at-risk contracts. This means aligning systemwide to work with different, disconnected payer models and making various operational adjustments.

“A consistent operational model is essential for success with population health,” Mr. Varnum said. “It starts by producing analytics that can be leveraged across clinical, financial and operational domains. By using analytics across use cases, organizations see consistent results, regardless of the type of payer arrangement or where the organization is in its population health journey.”

To Support Population Health, Every Organization Must Focus on Self-Service, Financial and Intervention Analytics

Based on his experience at Health Catalyst, Mr. Varnum recommends every healthcare organization prioritize three areas to enable population health.

First, make self-service analytic tools available for every single department that supports population health goals. “These analytics must be built on data that comes from a single source of truth,” Mr. Varnum said. “In addition, every group needs to work from the same definition of quality measures. If organizations create standardized definitions for quality measures and front-line providers can see those directly in the EHR, it becomes easy to make adjustments at the point of care for different patient populations.”

Second, use financial analytics to understand benchmarks and utilization for patient populations. “I’ve seen many accountable care organizations, clinically integrated networks, population health service organizations and payers sit back and ask about the per member per month for their patient populations,” Mr. Varnum said. “Instead, what they really need to know are the use cases in those populations, the benchmarks provided in contracts and utilization within populations. And they need to access that information quickly.”

Third, apply intervention analytics to isolate the impact and effectiveness of primary interventions like care management, remote patient monitoring, patient engagement tools and more. The key is to understand the root cause of problems and then to identify whether the interventions enacted drove success for the patient population. As Mr. Varnum said, “Knowing the effectiveness of primary interventions is the only way to scale successes.”

With The Right Tools and Systems in Place, Organizations Can Rapidly Expand Successful Population Health Initiatives

Health Catalyst recently worked with a large integrated delivery network in California with around 150,000 members and many at-risk contracts. “Within four days, our team developed and deployed an algorithm to identify COVID risk for mortality in their ACO population,” Mr. Varnum said. “The organization was able to risk stratify their entire patient population with the highest risk of mortality within one week. Care managers visualized the specific factors contributing to increased risk and developed prioritized member lists for outreach.”

The Health Catalyst team also identified approximately $10 million in opportunity across 50,000 members for a large IDN on the hospital side with multiple risk arrangements. The organization decided to address appropriate ED utilization. “We reviewed 20 different interventions and isolated three for the IDN to focus on. It took about three days to complete that analysis when historically it took multiple months, saving approximately 90% in internal staff efficiency. While the organization continues to review the impact on ED utilization, it has been able to rollout a substantial amount of appropriate utilization campaigns with their efficient operations. ” Mr. Varnum said.


Data, analytics and cross-departmental collaboration are common features among organizations that successfully operationalize population health. It all comes back to having the tools, structure and processes in place to rapidly identify exactly where the organization needs to focus and how to intervene.

“I’ve been hearing that population health risk feels more daunting today than it used to,” Mr. Varnum said. “However, we’ve seen a lot of good progress. It’s critical to leverage data appropriately, partner with internal teammates to understand what the analytics show and identify the changes that are needed.”

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