Human Resources and Benefits

A Practical Approach to Controlling the Cost of Health Care

healthcare costs

What can employers do to control the rising cost of health care within their own companies? What levers or tools exist to help employers keep expenses at a minimum while offering a quality benefits program in compliance with the Affordable Care Act (a.k.a. “Obamacare”)?

Cost Management Approaches 

In the past, employers have tried to shift the cost of benefits programs to employees by increasing deductibles, co-payments, and co-insurance rates. Employers reasoned that when employees faced the cost of these expenses themselves they would think more carefully about incurring medical expenses.

Cost-based approaches may have potential value and should be considered in the overall design of a specific benefit plan, but they also have inherent practical limitations because they are focused primarily upon plan features and not upon people and their health.

High deductible plans are one way to lower premiums and encourage consumerism; however, they may discourage the very employees  who need treatments or check-ups the most to bypass medical care to save money. Also, ACA now places constraints that limit the curtailing of benefits as employers strive to keep their plans in compliance with the law.

Risk Management Approaches

In efforts to get at the root of the problem, employers are increasingly turning to population risk management and wellness programs to encourage employees to be healthy. They reason that if illness can be reduced, the total cost of procedures and supplies will follow. In recent years, such risk-management measures have proliferated in the form of wellness programs, on-site clinics, staff nurses and case managers, financial and other incentives for healthy behavior, fitness centers or gym subsidies, bicycle pathways, and even vending machines loaded with nutritious snacks.

Such well-intended risk management initiatives may contribute to a cost reduction in the long run. The drawback, however, is that their effects are difficult to measure, therefore making such programs difficult to adopt. For example, if you buy gym memberships for everyone, you may not know that the employees who really need them are actually using them, and it may be difficult to show the program pays for itself.The effect of these initiatives may take years, even decades, to become discernible.

Find the Answers in the Data

Research shows that about 50% of an employer’s annual health care budget stems from as few as 3 to 5% of the employees. The rest of the employees, the vast majority, incur expenses that are comparable to one another. Only a few very serious health problems are needed to account for massive, budget-breaking expenses that most of us will never see. Such bills can grow to several hundred thousand dollars or more. Plan actuaries routinely bury these costs and smooth them over in cost averaging exercises across all covered members. But make no mistake, these cases drive the budget and, far more importantly, they are painful and heartbreaking to the individuals and families who endure them.

What would happen if these high-cost claimants could be accurately identified well in advance and assigned case managers who could prevent them from sliding into a tragic and costly condition? Health would improve and costs would decline. The science of predictive analytics in health care (i.e. the use of data to detect emerging health risk) is currently helping employers to identify and address emerging high-cost claimants in time to help them safeguard their health. Predictive analytics are surprisingly affordable. They focus on employee health, and when the findings are acted upon, the results are immediate, both in terms of health and plan cost.  The proof is in the growth of plan reserves and the decline of overall expenses.

How to Get Started

To take advantage of this technology, self-funded employers seek out a proven analytics service (these are generally recommended or provided by the employee benefit advisor). The insurance agent, the analytics vendor, and the employer will work together to compile data. The vendor will analyze the data at routine intervals and provide population risk scores and actionable information to the employer. Case management resources can then be organized to focus upon the highest priority cases (generally 3 to 5% of the covered lives). To get started:

  • Identify a predictive-analytics vendor with substantial experience in integrating claims history and non-claims information with a track record of producing measurable savings.
  • Once the plan design is set, communicate to employees how the plan will work, what additional data will be collected, and what incentives will accompany participation. Employees need a clear explanation and accurate expectations about what will happen both at implementation (e.g. blood testing, HRAs, vision tests, etc.) and later on, when reports and results are delivered.
  • Follow through is just as important as the analytics. A follow-through plan includes skilled case/disease managers and a corresponding budget. Design that plan.
  • Discuss the plan and use of predictive analytics with stop-loss carriers in advance and utilize supportive reinsurers who are willing to monitor the savings and reflect them in refunds as well as future premium adjustments.

The careful integration of a good predictive vendor service can greatly assist employers in reducing their health care expenses. Unlike other methods, a properly implemented predictive analytics program will make its value felt immediately. Most importantly, a good program may save not only money, but also employee and member lives.

Jon Prince

Jonathan M. Prince is chief executive officer of DataSmart Solutions, a subsidiary of Leavitt Group Enterprises. DataSmart Solutions provides predictive analytical services that help lower health plan costs for employers.