Communities: Brokers, Advisors & Consultants

Using Data Analytics to Cut Medical Costs

By Nadine Hays, Executive Vice President, Verisk Health

Prior to World War II, employers had very little to do with their employees’ health insurance.  A federally-imposed wage freeze after World War II drove employers to offer fringe benefits, including health insurance, to differentiate their companies and attract workers. 
 
Today, employers provide health benefits for nearly 157 million Americans, making them the principal source of health insurance in the United States. As medical costs reach all-time highs, employers are struggling to continue to provide health insurance to their employees and families. To combat these costs, employers must look for new ways to control spending.
 
Data analytics is one strategy that employers are increasingly turning to in order to identify opportunities to save themselves -and their employees - money without cutting back on the benefits their employees have come to expect.
 
Data analytics is instrumental to any medical cost-controlling strategy for a number of reasons. Data analytics:
  1. Provides clarity and transparency into the overall health of a population
  2. Allows an employer to identify disease or wellness trends
  3. Improves the focus and implementation and execution of health and wellness initiatives
  4. Facilitates more accurate budgeting and forecasting
Employers are starting to use data analysis strategies and tools that health plans have been deploying for years. Among these tools are data analysis and reporting platforms that enable rapid data integration, quality assurance, benchmarking, analysis, and customizable drill down capabilities. These robust platforms allow employers to turn their data into actionable information and measurable outcomes.
 
Data Clarity and Transparency
As employers face changing health care legislation (e.g., Patient Protection and Affordable Care Act) and sky-rocketing health care costs, it’s imperative they understand the overall health of their populations. To do this, employers need clear and transparent data. Data empowers employers to make informed decisions about everything from health care premiums that accurately reflect the risk of their populations to potential health and wellness programs that will have an impact on the cost and quality of health care delivery.
 
Data clarity and transparency are particularly critical as the risk pool for employers will change through health care reform. For example, the requirement that individuals under age 26 must be granted coverage under their parents’ insurance plan, will, in many cases, come from the employer. In order to ensure accurate premium costs, employers need to have an accurate understanding of the risk each member brings to the insurance pool. Insurance claim data, coupled with health risk assessment data, provide a picture of the total population and its disease burden.
 
Trend Identification
Data analysis allows employers to identify disease trends that impact their population, and their bottom line. By identifying trends—such as spikes in utilization, gaps in evidence-based care standards, disease prevalence and admissions—employers can more efficiently and effectively invest in resources and programs that will benefit their employees, and bend the cost curve.
For example, if data analysis revealed a growing trend in claims associated with hypertension (a precursor to some of the costliest chronic conditions), the employer could target and engage the at-risk employees and dependents in a heart healthy wellness program designed to educate employees on the importance of heart health and avoid conditions such as coronary artery disease.            
 
Health and Wellness Program Effectiveness
As employers better understand the overall health of their population, they can better allocate resources to programs and services focusing on the unique needs of their population. Through ongoing data analysis, employers can assess the effectiveness of these programs. Assessing the utilization, participation, and results associated with health management programs will help employers identify initiatives that work, as well as those that  drain resources with no quantifiable return on investment.
 
Employers are particularly interested in absenteeism and productivity data. Analyzing this data helps identify further business impacts caused by health care related absence. This analysis also enables employers to design appropriate return-to-work programs. 
 
Notably, employers should think about working with a third party to identify employees for appropriate programs.  A third party keeps employee-employer trust relationships intact and prevents the employee from feeling that participation in the program will affect his/her job. 
 
In addition, third parties can assist with tracking and reporting health and wellness-related costs separated by employer- and employee-related costs, as well as develop a mechanism to measure return on investment for any health and/or wellness initiatives.
 
The growing interest in value-based benefit design, which rewards the use of high value services and healthy lifestyles, is another opportunity for employers to leverage their data to reduce health care costs. Analyzing medical and prescription claims, health risk assessment, and biometric data will provide a baseline from which value based plans can be designed. For fully insured populations, lower premiums may be available.  For self-funded employers, knowing the member population is critical to implementing a value-based benefit design plan. 
 
Budgeting and Forecasting
Data analysis platforms can help employers get a handle on present and future health care costs, as they provide access to timely and accurate cost and budget information. Predictive models and risk adjustment methodologies embedded within data analysis platforms allow employers to adjust for risk, measure plan and physician performance, set rates within plans, predict future cost for budgets, and refine potential wellness and care management strategies.
 
For budgeting and forecasting purposes, employers can use data analytics to:
  • Track trends in cost, utilization, and risk measures to understand cost drivers
  • Analyze group risk by business segment, location, group size, etc.
  • Establish pricing assumptions, rating processes, and underwriting guidelines
  • Enhance rating processes through risk and predictive models
  • Assess risk across business units within the  organization
Conclusion
From simply understanding the dynamics of your insured population to designing value-based benefits programs, using health data is critical to managing the increasing cost of health care and changes required by the recently passed health care reform legislation.
 
To reduce the overall impact of health care costs, employers should focus on the aspects of care that are within their control. They should evaluate the population, identify trends, implement targeted and appropriate health management programs, and identify opportunities for benefit redesign.
 
 
About the Author:
     Nadine Hays is responsible for strategic partnerships and marketing across the several healthcare market segments served by Verisk Health. She also oversees the company's international operations.She has nearly 20 years of experience in clinical informatics and healthcare IT, working with several leaders in the industry to introduce innovative solutions for managing the quality and cost of care. Prior to joining Verisk Health, Nadine was the Chief Marketing Officer of D2Hawkeye, which provided data analytics and decision-support tools to the healthcare industry. Before leading the marketing and sales teams at D2, Nadine led the strategy and business development team for McKesson Health Solutions, the integrated care management and claims performance arm of McKesson Inc. She also served as an executive at various healthcare and software companies, including Ingenix, Iameter and National Healthtech. Nadine is a licensed attorney, having practiced as a securities specialist for several years.Nadine has a BA from the University of California, Santa Barbara and a JD from Loyola Law School in Los Angeles, Calif.

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