An ideal first step brokers can take when evaluating new changes to their self-funded clients’ benefits structure is a health insurance claim audit. This will lead to lower healthcare costs through identification of trends and high-cost claimants.
There are two generally accepted approaches for auditing claims: 100-percent claims audit and stratified random sample audit.
The 100-percent claims audit tests all claims for modeled attributes, such as eligibility, plan design features, compliance with an administrator’s policies and procedures, and industry practices. This 100-percent claims audit approach identifies hard-to-discover, systemic processing errors and potential overpayment recoveries.
The stratified random-sample approach is an end-to-end claims processing audit used to measure an administrator’s claims processing accuracy and timeliness against performance guarantees, industry practices, and marketplace standards. Results from the sample can be extrapolated to the full claims population and can be expressed in statistical terms.
Both options are generally accepted, but Truven Health Analytics has found that the results of 100-percent auditing were significantly different and better than the results of auditing based on random-sampling. They conducted a study a few years ago and looked at multiple sets of claims errors from two large Fortune 100 corporations and compared them to 100 simulated audits each of 300- and 400-claim random samples taken from the same claim errors. Random sampling, according to the study, failed to identify a significant amount of claim errors, which translated into appreciable financial losses.
The study concluded that all corporations and government entities relying on random-sampling methodologies for health plan auditing should consider 100-percent-of-claims auditing of some type.
And with a meaningful and accurate claims audit, companies and public entities have the benchmarks in place to analyze trends. With accurate claims data companies can stratify their population according to three risk levels: high, medium, and low. The thresholds for these risk levels are based on algorithms outlined in ATP IV, JNC8, ADA, and Framingham Heart Study.
The real savings then comes from implementing onsite population health risk management services and working with the medium- and high-risk patients to reduce those risks. Verification of savings is confirmed by comparing the claims trend prior to opening an onsite healthcare program to claims trend post opening, adjusting for medical trend and high cost claimants.
These results are easy to share with clients, as they include hard and soft dollar savings. But the first step is devising an accurate claims auditing system.