Magnifying glass and analyticsOrganizations interested in approaching population health firstneed to understand the data available, the nature of the data, andthe data's context.(Image: Shutterstock)

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Self-insured health systems are adept at looking at individualpatients, diagnosing a health problem and pinpointing a solution.Looking across a population to identify and act on healthimprovement opportunities for their employees is much morechallenging.

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

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So, how can self-insured employers move pastsome of the roadblocks they have faced thus far and start to pickup the pace in the pursuit of successful population healthmanagement? The key is often in the data.

1. Involve the right people from square one

Recognize that population health management is a businessstrategy as well as a clinical one, which will dictate the peopleyou involve in the program. The C-suite needs to be involved whenthe health of the business is at stake. The head of humanresources, the chief financial officer, and the chief medicalofficer should all be part of program creation as it touches oneach of their areas of expertise. Collaboration across these areasensures that goals are aligned and investments in the tools ofpopulation management are sustained.

2. Gather and assemble as much data as possible

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

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

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

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

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Biometric and lab screening data: Indicatorssuch as weight, blood pressure, and blood glucose levels are icingon the cake. Health risk assessment data is also useful.

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Data is meaningless without context and analysis. Organizationsinterested in approaching population health first need tounderstand the data available, the nature of the data, and thedata's context.

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

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

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

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

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

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Be careful about technology that creates attractive graphs andcharts that present only what happened. To inform change, you alsoneed to know why it happened. Make sure you have skilled dataanalysts involved in the technology assessment process that canconfirm that they will be able to use the technology to reveal thestory behind the charts. The best analysts are always looking forcause and effect and often have a financial background. They liveby the axiom, “Correlation doesn't imply causation,” and they aremaestros when it comes to handling data.

4. Turn insights into action

The risk scores created by the stratification engine are a goodplace to begin when figuring out which employee groups should betargeted for population health management. Focus on the highestrisk individuals from a clinical and financial standpoint, butfactor in the organization's strengths as well. If primary care isstrong, zero in on prevention. If the organization has aworld-class cancer institute, create programs that concentrate oncancer prevention and management.

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Individual behavior is one of the biggest wild cards in anypopulation health program. Building a culture of health awarenessand accountability can make all the difference, but organizationsneed to ensure that this culture is pervasive and reinforced.Creating a walking program or offering incentives such as payingfor a gym membership are a start, but they often attract those whoare already motivated. We call this the “affinity effect,” becauseprograms like these tend to attract those who already have anaffinity to doing whatever is offered. The challenge, then, is tothink of ways to build that affinity in your employees by helpingthem understand that self-care is as important as patient care.

5. Measure and revise

The first thing to measure is engagement: how many employees areparticipating in the program? Participation comes in many forms,depending on the interventions. Did the employee visit her primarycare physician after a high blood pressure reading? Did thoseemployees overdue for breast cancer screenings finally getthem?

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An organization that achieves a high level of health awarenessand accountability has programs that engage 90 percent or more ofthe adult population in understanding their health status andactively acting to improve. A highly engaged employee populationsets the stage for the employers to reap the highestrewards from an effective population health managementstrategy.

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

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Costs could go down in the near-term, but be careful inattributing a reduction in cost to the effectiveness of populationhealth management strategies too early in the process. Rememberthat the primary financial goal of population health management isto avoid or mitigate future high-cost events. Due to the lowfrequency and high severity of high-cost events, any qualifiedsuccess needs to be evaluated from a long-term perspective.

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Measure often, and revise as needed. Be sure to allow enoughtime for programs to have an impact, but keep close tabs on theirprogress along the way so you are ready to shift in a new directionif the data points the way.

The key takeaway

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

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Case Escher is managing director ofInteras, the data analysis and consulting division ofThePartners Group, serving more than 600 employee benefit clientsin the Northwest with employee benefits, retirement, and investmentservices; commercial and individual insurance services; data andanalytics; and health and productivity consulting.


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