The client company is a long-term care (LTC) insurance closed block that is over 20 years old. The block has a significant number of policies with home health care (HHC) benefits, as well as a number of indemnity policies with lifetime benefits coupled with 5% compound inflation riders. The block was in a position where the rate of new claims notices was increasing, claim incidence was higher than expected, claim durations were longer, and policy termination rates were below expectations. All of these factors were consistently beyond the actuarial expectations when the policies were originally issued and added to the complexity of managing the client’s LTC block. Despite the challenges this client faced, Fuzion knew there were steps that could be taken to improve the effectiveness of the management of their block.
Fuzion’s initial focus was to understand the factors impacting benefit payment of new claim requests. To support this initiative and in response to the dynamic challenges of the LTC industry, Fuzion took a deep dive into available claims data to analyze and predict specific outcomes of their clients’ blocks.
To analyze the rate of acceptance, Fuzion undertook a series of actions. We started with benchmarking clinical decision quality in a parallel decision test against a best in class nurse network. Based on statistical techniques, the comparison of the two groups assessing claimants were both interesting and revealed a significant gap. Assessments had different areas of focus and often times, resulted in very different conclusions. We presented the findings to the third-party administrator (TPA) and worked together to close the gap with improved quality of decisions in initial claims.
Next, Fuzion sought to understand the gap between actual and expected termination rates. Research into the reporting of deaths revealed that reliance on the traditional source of death determinations was insufficient because the social security administration death master file was not being universally updated. Fuzion augmented the data source with primary research into suspected deaths which identified additional deaths not previously identified with the current methodology, thus closing the gap between actual and expected termination rates.
Another area of opportunity with the block of business was the client’s administration of the restoration of benefit (ROB) provision within its policies. The misapplication of the ROB provision elongated the duration of the policies, as well as increased claims payments. Fuzion applied clinical benchmarking results with predictive analytics to spot repeating ROB cases as well as those positioning for an upcoming ROB request.
The cumulative results of these actions were impressive. The quality of initial claims decisions increased, claims durations and payments were reduced, and termination rates were positively impacted. Fuzion utilizes predictive analytics, industry leading practices, and deep research to improve clients’ operational and financial results. These steps are performed thoughtfully and with consideration for contractual responsibilities to the clients' policyholders.