Self-insured health systems are adept at looking at individual patients, diagnosing a health problem and pinpointing a solution. Looking across a population to identify and act on health improvement opportunities for their employees is much more challenging.

The reasons for self-insured employers to master population health management are compelling. First, it's the right thing to do by their employees, helping to keep them healthy and head off any problems that might be on the horizon. Second, these organizations are responsible for their employees' health care costs, and effective management can slow cost escalation. Third, research substantiates that healthier employees are more productive, and that minimizing absenteeism—as well as presenteeism—has a positive impact on the organization's bottom line. And finally, they have a wealth of data at their fingertips about their employees, so they can truly be effective at risk identification and stratification, as well as the feedback loop on which interventions work best.

So, how can self-insured employers move past some of the roadblocks they have faced thus far and start to pick up the pace in the pursuit of successful population health management? The key is often in the data.

1. Involve the right people from square one

Recognize that population health management is a business strategy as well as a clinical one, which will dictate the people you involve in the program. The C-suite needs to be involved when the health of the business is at stake. The head of human resources, the chief financial officer, and the chief medical officer should all be part of program creation as it touches on each of their areas of expertise. Collaboration across these areas ensures that goals are aligned and investments in the tools of population management are sustained.

2. Gather and assemble as much data as possible

The more data you have, the more accurate and multi-faceted your insights can be. Ideally, an organization would leverage:

Prescription data: This is probably the single most valuable source of information for risk stratifying a population. First, little lag time occurs between the filling of a prescription and the reporting of that information. Second, individuals with chronic illnesses may not necessarily visit their physician but do tend to take their medications.

Medical claims data: Post-adjudicated claims data is key for visibility into encounters outside the employer's electronic medical record (EMR) system. Other EMR encounter data can augment the claims data.

Demographic information: This includes age, gender, ethnicity, address, allergies, major diagnoses and general medical history.

Biometric and lab screening data: Indicators such as weight, blood pressure, and blood glucose levels are icing on the cake. Health risk assessment data is also useful.

Data is meaningless without context and analysis. Organizations interested in approaching population health first need to understand the data available, the nature of the data, and the data's context.

3. Choose (and use) your data analytics tool wisely

With data assembled, an organization needs some analytical power to begin drawing insights. This power goes beyond the standard Excel spreadsheet and should include the following:

A risk identification and stratification methodology. Risk models take the data at hand and use it to assess how sick an individual is; the individual receives a “risk score” that describes the person's likelihood of using more or fewer services than an average person. Once individuals are scored, they can be grouped into categories from healthy to sick. This stratification process helps organizations know where to focus resources and what type of resources will have the greatest impact. Risk models can also be used to evaluate program success, monitor physician performance, and establish equitable risk-based contracts.

Hierarchical condition category groupers. These tools take the more than 10,000 ICD-10 codes—the alphanumeric codes used to represent myriad diseases, disorders, injuries, symptoms, etc.—and aggregate them into clinically and financially similar groups for easier comparison.

A utilization definition engine. It can be challenging to translate a collection of claims data into a coherent narrative of a medical events. For example, a gall bladder surgery can generate a dozen different bills—one for the anesthesiologist, another for the facility, a third for the surgeon, and so on. An analytics engine must be able to take all those charges, analyze them, and determine: “This was one event. It was gall bladder surgery. Here's what it cost in its entirety.”

Be careful about technology that creates attractive graphs and charts that present only what happened. To inform change, you also need to know why it happened. Make sure you have skilled data analysts involved in the technology assessment process that can confirm that they will be able to use the technology to reveal the story behind the charts. The best analysts are always looking for cause and effect and often have a financial background. They live by the axiom, “Correlation doesn't imply causation,” and they are maestros when it comes to handling data.

4. Turn insights into action

The risk scores created by the stratification engine are a good place to begin when figuring out which employee groups should be targeted for population health management. Focus on the highest risk individuals from a clinical and financial standpoint, but factor in the organization's strengths as well. If primary care is strong, zero in on prevention. If the organization has a world-class cancer institute, create programs that concentrate on cancer prevention and management.

Individual behavior is one of the biggest wild cards in any population health program. Building a culture of health awareness and accountability can make all the difference, but organizations need to ensure that this culture is pervasive and reinforced. Creating a walking program or offering incentives such as paying for a gym membership are a start, but they often attract those who are already motivated. We call this the “affinity effect,” because programs like these tend to attract those who already have an affinity to doing whatever is offered. The challenge, then, is to think of ways to build that affinity in your employees by helping them understand that self-care is as important as patient care.

5. Measure and revise

The first thing to measure is engagement: how many employees are participating in the program? Participation comes in many forms, depending on the interventions. Did the employee visit her primary care physician after a high blood pressure reading? Did those employees overdue for breast cancer screenings finally get them?

An organization that achieves a high level of health awareness and accountability has programs that engage 90 percent or more of the adult population in understanding their health status and actively acting to improve. A highly engaged employee population sets the stage for the employers to reap the highest rewards from an effective population health management strategy.

There is no reason to reinvent the wheel: Where applicable, rely on HEDIS® measures as the standard. For non-HEDIS measures, evidence-based standards of care are key. An informed PCP-attribution methodology is also important to assess and evaluate provider panels and patterns. Look for high engagement, improvement in quality and preventive measures, and fewer gaps in care to gauge the near-to-intermediate success of effective population health management.

Costs could go down in the near-term, but be careful in attributing a reduction in cost to the effectiveness of population health management strategies too early in the process. Remember that the primary financial goal of population health management is to avoid or mitigate future high-cost events. Due to the low frequency and high severity of high-cost events, any qualified success needs to be evaluated from a long-term perspective.

Measure often, and revise as needed. Be sure to allow enough time for programs to have an impact, but keep close tabs on their progress along the way so you are ready to shift in a new direction if the data points the way.

The key takeaway

Population health management can improve the health of groups of individuals, especially the most medically needy. To be successful, these organizations must implement programs that make the most out of their data. Doing so requires both sophisticated analytical tools to interrogate, manipulate, and summarize the data and the skilled problem-solvers to wield them.

Case Escher is managing director of Interas, the data analysis and consulting division of The Partners Group, serving more than 600 employee benefit clients in the Northwest with employee benefits, retirement, and investment services; commercial and individual insurance services; data and analytics; and health and productivity consulting.

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5 tips for self-insurers to up their population health management game

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