Linking Data to Actions and Outcomes in Healthcare Benefits Strategy

This article accompanies Health Partners.

Saturday, June 2, 2012
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As healthcare costs have risen, leading employers are using advanced data analytics to better understand and manage their healthcare benefits programs -- similar to the way they manage their business. But drawing meaningful information from the data is both challenging and more critical than ever.

HR and finance departments often get monthly and quarterly reports from insurance carriers, PBMs, disease-management vendors and even data-warehouse vendors that can provide pages and pages of claims statistics. This volume of data and reports is often ineffective in leading to actionable results. 

Yet, data is critical to understanding root cause, identifying opportunities, evaluating performance and guiding corrective actions. This article outlines three key principles for executing a data-driven healthcare strategy.

Understand and isolate the impact of six key factors driving healthcare costs.

Healthcare program costs are essentially driven by the following six basic factors -- the first five of which can be managed (most employers are still focused on one or two):

* Health Status. An individual's "healthiness" is a primary driver of healthcare demand. Health status is influenced by age, gender, genetics, environment and state of mind.

But behaviors and individual choices, both positive and negative, play a significant role as well. 

By leveraging diagnosis-based risk scoring, biometric measures, lab results and health-risk questionnaires, health status across the population can be classified, trended and tracked.

* Healthcare Consumption Patterns. The ways in which individuals engage providers and "buy" healthcare services can have a significant impact on costs. 

With advances in technology and changing attitudes toward transparency, true consumerism in healthcare is starting to emerge. Opportunities for improved consumer choices can be found by analyzing utilization patterns and employee choices within the claims-experience data. 

* Provider Treatment Patterns. Health providers, primarily doctors, can impact claim levels through their treatment tendencies. The number and types of tests, the drugs that are prescribed, whether or not surgery is recommended, and the hospitals that are used can all cause significant variations in costs from provider to provider. 

Provider-treatment patterns can also be analyzed by examining variations in healthcare services across providers while controlling for differences in diagnoses.

* Negotiated Cost Structures. In the U.S. health system, there are significant variations in price. Even the same provider will have many different price schedules for the same set of services, depending on the health-plan networks they belong to and the contracts in place. 

Examining provider experience, actual prices and how they change over time is important to understanding net employer costs.

* Program Design Features. Plan designs determine cost sharing, the out-of-pocket amounts that must be paid by participants through deductibles, coinsurance and copayments. 

But plan designs also influence all other aspects of spending, by directing and shaping participant actions, such as which provider to choose, how many tests to get, healthy choices, etc. The impact of plan design and financial incentives on cost levels is often under-appreciated by plan sponsors and it is important to understand -- whether or not the design and incentives are driving the appropriate behaviors. 

Taking a detailed look at the incentives and determining whether the amounts that are being paid for the intended results are creating value is an important part of the analysis.

* Random Events. Many healthcare claims, especially some of the larger dollar amounts, are simply the result of random occurrences. Even if not purely random, the unpredictability of many of these medical events makes them random for practical purposes. 

Examples include accidents, aneurisms, premature babies and certain diseases such as pancreatic cancer. Understanding the random nature of healthcare data is necessary to properly analyze claims data and avoid erroneous conclusions.

By isolating these six factors and focusing the data analysis on each of them, we can better understand what is driving overall cost levels.

Connect the dots to understand interplay between strategies and outcomes.

The next key is to "connect the dots" and understand the interplay between the six cost factors, along with corporate initiatives, and the controllable factors influencing these.

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By examining the interconnections, better insights can be gained about the downstream cost impact and time frame associated with health behaviors, the ROI and impact of program interventions, the intended and unintended consequences of incentives and cost sharing, and the impact on non-medical factors such as productivity and employee engagement.

Additional data sources to "connect" include clinical systems, biometrics, lab results, health-risk questionnaires, health-risk scoring algorithms, disability and absence data, workers' compensation data, wellness and disease-management programs, business data and even personal spending.

Advanced data analytics can help to determine the correlation and potential cause-and-effect relationship between healthcare programs and improved quality, morale and even profits.

Integrate strategic planning, forecasting and monitoring into healthcare benefits.

An advanced understanding of the interplay between cost drivers, productivity, engagement and program interventions should then help drive a holistic and proactive approach to healthcare strategic planning. 

For example, financial models can be developed to facilitate dynamic forecasting and monitoring to establish performance metrics that reflect both leading indicators and lagging outcomes, and provide a roadmap for mid-course evaluation and timely correction.

Too often, strategies we are deploying are created in a vacuum and isolated, without fully understanding what the impact and time frame is on the desired outcomes.

Is your population getting healthier, using services wiser? Have you established the intended impact of strategies being implemented and then measured against those objectives? Are your strategies having intended consequences, unintended consequences or any consequences at all?

By taking this approach, leading employers have been able to synthesize data, program results and outcomes into actionable information and produce executive-health dashboards that can be shared between benefits professionals, finance and top leadership as appropriate. 

With continued technological and systems advances, more and more specialized healthcare data is at employers' fingertips. While it's easy to get lost in a sea of numbers and statistics, smart analytics will help sift through the data to make meaningful connections, ignore spurious data and provide actionable results that can be the foundation for future benefits strategy.

Ron Barlow is a managing director in the healthcare practice of PricewaterhouseCoopers' Human Resource Services. Kim Starmann is a director in that practice.  Both are based in Chicago.

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