In today's era of "big data" and "data analytics", it is natural to see growing interest among corporate wellness managers to leverage this information to drive their programs, policies, and incentives. Of course, before you can leverage it, you need to have good data to leverage.
And getting good data can be a real challenge these days considering the number of sources, various tracking and management systems, regulatory statutes governing various types of corporate wellness data, as well as reporting irregularities and the like.
Certainly, crafting out a good strategic plan can greatly enhance your request, acquisition, and analysis efforts. Do you have a plan currently in place? If not, invest some time and effort in preparing a plan of action that specifies what you'd like to do, what resources you'd like to tap, steps for proceeding, and when.
For starters, if you're working in a mid-sized or large organization, you may have several in-house data sources to tap such as human resources, benefits, occupational health, safety, risk management, employee assistance, etc.
In addition, it's important to tap external sources such as a third-party medical claims administrator (TPA) and vendors who provide health risk appraisals, biometric screenings, health coaching, online activity courses, incentive programs, and any other service or activity.
In making initial contact with both in-house and external data sources, it's important to explore and ascertain what of datasets are appropriate and inappropriate to share due to privacy and confidentiality issues. Taking the time and energy in making a genuine effort to learning about various data sources can greatly enhance the request process.
Likewise, it's important to have realistic goals and strong justification for each request, with respect to your particular role in an organization-as well as any role and responsibilities you may have in dealing with external sources.
Since data sources, like instruments, and tools, can change unexpectedly, it's important to be able to adapt to these inevitable events in order to build sound relationships with new people and data management protocols. While this acumen applies to anyone in the corporate wellness arena, I think it's particularly essential for consultants and others who may not have regular day-to-day contact with an organization's in-house and external sources.
Whatever role you play in corporate wellness, I believe it's absolutely essential to have a strong basis for making a particular data request. For example, whether you are working as an independent corporate wellness consultant or are part of an in-house wellness team making a request, are you and your team asking the following Socratic-level questions before making a formal data request?
- What types of are we requesting?
- Why are we requesting this type?
- Who is the direct contact person responsible for managing what is being requested?
- How long of time frame are we interested in?
- When would it realistically be available?
And, with whom do we need to partner with in making an effective data request? For example, will the request carry more weight if it's spearheaded by a senior-level manager?
Another key consideration in making an effective data request relates to the scope and specificity of the request. Scope refers to elements such as time-frame, target population, and geographic location. For instance, requesting three to five years of aggregate, organizational level medical claims would naturally give a more representative year-to-year trend of specific back injury claims than simply requesting claims data only for the past year; similarly, request a distribution of employees who completed five consecutive years of a health risk appraisal versus those to who completed only last year's HRA.
Specificity refers to the level in which a data request involves distinguishing data by designated classifications or categories. This would entail asking your health coaching vendor for data distributions by the average number of coaching sessions on a daily vs. weekly vs. monthly vs. seasonal intervals or asking your company's prescription drug benefit manager for seasonal reports that distinguish the top 15 most commonly used brand name vs. generic drugs. In essence, integrating scope-and specificity-level parameters into your data request greatly enhances the degree to which you can eventually leverage in the analysis phase.
Several decades ago, David Aquilina, a nationally-respected data analyst and consultant, poignantly described the relationship between a particular data request and analysis options. Specifically, he initially described how a basic data request limited an individual to doing only a "Basic Assessment" or BA.
Here is an example. A basic data request could be characterized as one involving:
- a single variable
- a short timeframe
- the total population (i.e., all employees)
By and large, this limited information provides only a snapshot of how a single variable relates to a large population over a short span of time. In contrast, expanding the scope of the request-one that includes two or more types of data, over a multi-year time-frame, and breaking a large population down, say, by age groups, for example- would provide an opportunity to conduct a "Problem-Focused Analysis" or PFA.
In fact, a PFA might show a three-dimensional co-relational analysis of age, health plan choice, and health coaching engagement, such as employees in the 40-49 age group who were enrolled in the high deductible health plan engaged in the highest number of weekly health coaching sessions over the past two years. Considering the distinct differences between a BA-focused and PFA-focused data request, which would you prefer to use?
Data vs. Datamation vs. Information
Analyzing corporate wellness data has come a long way over the past generation or two. For example, in the formative years of the corporate wellness industry-the 1960's to the mid-1980's-program managers commonly relied on basic information such as participation and adherence to monitor and evaluate their wellness interventions.
With the advent of Microsoft Excel and evolving statistical applications in the mid-1980s, an era of datamation unfolded in which worksite wellness personnel expanded their data analyses into trending and forecasting valuations.
At that point worksite wellness personnel could begin to seriously look at how some of their performance metrics such as participation, engagement, health cost trending, and employee productivity were tracking on a longitudinal, year-to-year time-frame.
Of course, various technology-driven tools and techniques have evolved over the past decade to provide worksite wellness personnel with an ever-expanding array of real information platforms that explore potential relationships between two or more variables-like risk factor cost calculators, dashboards, ACOEM's Blueprint, HERO Scorecard, IBI's H&P Snapshot, WellSteps' ROI calculator, etc.).
Naturally, the ability to generate real strategic information is dependent, in large part, upon the overall quality, accuracy, and objectivity of data used in these valuations. In essence, the time and effort required for acquiring good data provides worksite wellness decision-makers with the opportunity to leverage numerical values into dynamic sets of information. Several examples come to mind on how worksite health managers are leveraging various types of data to achieve specific health management goals.
Data Management Case Studies
Jared Pankowski, M.S.Ed., client services strategist at TargetCare, Charlotte, North Carolina, analyzes claims data over a two to three-year period. He assesses changes in health risk factor levels and medical costs associated with these risks.
Claims analysis also allows him to look at the cost-avoidance that a wellness program has been able to produce over this time period. Analyzing claims enables him to assess gaps in a workforce's health care to share with vendors so they can target their programs and services to drive better risk reduction and health management outcomes.
A second example of the value of data-driven leveraging is evident at Hamilton Medical Center in Dalton, Georgia, where vice president Danny Wright used a break-even analysis approach to determine the financial viability of a proposed wellness center expansion.
By acquiring cost data from various in-house and external sources, he was able to integrate and compare expected and actual results once a new service is operational. In making the decision to initiate a substantial expansion and renovation of HMC's wellness center which involved new, cutting-edge services, the research that went into the analysis and the results were paramount in his decision to invest in this initiative.
A third example of data-driven leveraging was instrumental to determine if an in-house health clinic was the most cost-effective approach to providing employee physicals, primary care, vaccinations, and allergy shots. In doing so, Judy Garrett, RN, COHN, health services manager at Syngenta Crop Protection in Greensboro, North Carolina, compared the cost of the clinic to costs if those visits had been submitted under the employees' health care plan.
As part of this analysis, she continued to measure everything in the wellness program, from participation to biometric results, in addition to an annual review of health care claims. The data provided information to guide the direction and highlight needs in the employee wellness program. Moreover, she contends that it also gave her health management team a way to objectively show senior management the overall progress their efforts have made toward improving employees' health and organizational health cost management.
Overall, she reports the data and cost-effectiveness analysis kept Health Services and the wellness program, Reaping Rewards, an integral part of the company.
Cindy Guillaume, LCSW, manager, employee assistance services) and her health management colleagues at Poudre School District in Fort Collins, Colorado, worked with an independent consultant for almost 18 months to gather retrospective and current data on various key metrics- from participation and engagement to medical care costs and productivity indices.
The acquired data was integrated into a comprehensive benefit-cost analysis and return on investment (ROI) study that provided the school board with the confidence to approve funding for a broad-based integrated health management system.
A final example of data-driven leveraging relates to forecasting for driving better employee health and productivity outcomes. Robin Rager, Ph.D., principal, Optimum Health Management, LLC, Torrance, California, uses data on biometrics, health behaviors, health-related work impairment, employee salaries/wages, and medical and workers' compensation claims to conduct health management forecasting for his respective clients.
Current trends in the prevalence of health risk factors and chronic conditions are assessed to determine the potential impact of targeted population health management initiatives on avoiding future medical and lost productivity costs. By combining a bit of art along with scientific methodology, Robin is able to use such forecasting to provide valuable information for strategic planning and projecting the ROI of the program.
Certainly, today's array of wellness data analysis tools provides decision-makers with various platforms and techniques to track, monitor, and evaluate their respective programs, policies, and incentives. Are you taking advantage of today's information technology to leverage your corporate wellness data or are you still relying on basic data and a basic assessment? As the New Year unfolds, perhaps 2016 is the year to ramp up your data and basic assessment efforts into a strategy to generate real strategic information.
About the Author
Since 1979, David has conducted econometric-based worksite wellness program and clinic analyses in the U.S. and Europe. He has written five books on corporate wellness and health management. He is professor emeritus at East Carolina University and directed the worksite health promotion academic program for nearly three decades.
