How to use data to customize your health plan and control costs

“In today’s health care market, data can be used as a valuable resource to control costs. By examining customized financial data sets, it’s possible to determine where heath care dollars are being spent and where there is potential waste,” says Brian Fallon, regional vice president of Network Management & Business Development at HealthLink Inc.

Smart Business spoke with Fallon about how employers can use data to build a customized health plan and control costs.

Why is data so valuable?

Health care spending can be analyzed in terms of fixed and variable costs. Fixed costs include administrative costs such as third-party administrator charges, network access fees and the premium for stop loss insurance. Variable costs are just that, variable, and include claim utilization cost incurred by covered members/their dependents, and are impacted by plan design, demographics and the health of the member population served.

Data allows fixed costs to be analyzed in order to find opportunities for saving. But more importantly, data allows you to look at variable cost. You can discover where costs are coming from, and if there are underlying root issues. Then, you have the opportunity to predict variable costs and, using custom plan design strategies and cost containment programs, control health care spending.

Does this only work for self-funded plans?

Historically, yes. For employer groups with less than 100 lives, fully insured employers receive a monthly list bill with premiums owed. Since the carrier assumes the risk and pools it with other employer groups, there is little, if any, reporting. Fully insured groups with greater than 100 employees receive some reporting but the availability varies among carriers. Typically, the greater the enrollment the greater the reporting, because once an employer reaches a certain size, there is less dependency on the risk pool and greater consideration of an employer’s own data.

The customization and flexibility of self-funded arrangements, coupled with the fact that a self-funded plan is the employer’s plan, not the carriers, make them ideal for utilizing data to drive more cost-effective outcomes. The chosen programs and services can be customized for the employer — the plan is theirs, the programs are theirs and the savings is theirs.

Is using data to this extent a recent trend?

Using data to look at costs has always been important and a major benefit of self-funding, but changes, such as Affordable Care Act mandates and the removal of lifetime maximums, have facilitated a more aggressive approach.

How can employers use customized data to examine their health care dollars?

Examining data in this way isn’t a product employers just purchase and apply. It’s a process — and the process starts with availability of data. This depends on whom an employer is working with, how transparent the company is willing to be and the degree of creditability within the data.

Some areas that should be examined are ages within the group, top diagnoses and incidences of high-cost medical conditions. Also, consider non-clinical data — out-of-network and emergency room usage — to see if it is a factor of high spending.

Once there’s concrete understanding of the health plan and member population, your advisers can show you how to proactively manage risks. The best way to affect outcomes is a collaborative relationship between all the required parties needed to design and administer a benefit plan. There are also new opportunities with providers who are willing to collaborate in shared risk agreements.

What else do employers need to know?

How data is presented can be as unique as the network or carrier itself. Discrepancies can distort accuracy, so employers need to understand what the data actually entails. They should know the difference between repriced and actual paid data, how current the data is, whether or not it has duplicates and, when looking at discount data, the facility level discounts. It’s also critical to review the facilities’ case mix indexing and the cost to charge ratios. These components can affect the data, the analysis, and ultimately, the conclusions.

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