by Truven Health Analytics®, IBM Watson Health™
March 30, 2017
Health plans today are facing the complexities of reform, increased competition, and budget constraints — all while dealing with pressures to reduce costs and improve member health. Managing health risk has become a necessity. But to manage risk, plans must first understand their population. To do this well, they need reliable, robust risk and cost of care models.
Risk modeling is a very helpful tool for health plans. It can provide valuable insights into member utilization patterns and risk, which is vital for benefit planning, disease management and wellness program management, and member communications. It can provide deep insights into provider performance, and aid in determining ideal reimbursement and premium rates. Such models are an integral part of a number of Truven Health databases and analytical tools.
Recently, the Society of Actuaries (SOA) released a study, Accuracy of Claims-Based Risk Scoring Models, comparing health risk-scoring models. The study found that Truven Health Analytics’ cost of care model outperformed other risk models in 18 out of 22 measures. The SOA study built on their previous research with similar objectives (the most recent in 2007). In the medical claims category (predictions based only on medical claims data), the current study showed that, in 21 of the 22 measures, the Truven Health model was ranked either first or second. No other model came close to matching this performance. (See the table below for a summary of how Truven Health’s model ranked relative to the competition).
The SOA evaluated Truven Health Analytics’ cost of care model against six others:
The SOA assessed all models on their ability to predict costs using the Truven Health Marketscan® commercial claims dataset of 1 million members, and used three methodologies to evaluate their precision: R-Squared, the mean absolute error statistics, and predictive ratios. All three methodologies measure the statistical difference between the prediction and the actual results. All models produced both a concurrent and prospective cost prediction and were evaluated using both a capped data set (where patient costs were capped at $250,000) and a non-capped data set.
The SOA evaluated the models’ predictive ability using a number of scenarios (total medical costs, simulated random groups, condition-specific predictions, patient cost). In the simulated random group scenario, the SOA created groups of 1,000 and 10,000 patients to simulate the application of the model to subgroups of the population.
The Truven Health model ranked first or second for its ability to predict costs in 21 of the 22 measures studied.
To learn how you can put your data to work, leveraging risk and cost of care models to answer your most pressing business questions, contact Truven Health.