Using Predictive Analytics for Early Identification of Short-Term Disability Claimants Who Exhaust Their Benefits
Early interventions can help short-term disability insurance (STDI) claimants return to work following onset of an off-the-job medical condition. Accurately targeting such interventions involves identifying claimants who would, without intervention, exhaust STDI benefits and transition to longer-term support. We identify factors that predict STDI exhaustion and transfer to long-term disability insurance (LTDI). We also explore whether waiting for some claims to resolve without intervention improves targeting efficiency.
We use a large database of STDI claims from private employer-sponsored disability insurance programs in the United States to predict which claims will exhaust STDI or transition to LTDI. We use a split sample approach, conducting logistic regressions on half of our data and generating predictions for the other half. We assess predictive accuracy using ROC curve analysis, repeating on successive subsamples, omitting claims that resolve within 2, 4, and 6 weeks.
Age, primary diagnosis, and employer industry were associated with the two outcomes. Rapid attrition of short-duration claims from the sample means that waiting can substantially increase the efficiency of targeting efforts. Overall accuracy of classification increases from 63.2% at week 0 to 82.9% at week 6 for exhausting STDI benefits, and from 63.7 to 83.0% for LTDI transfer.
Waiting even a few weeks can substantially increase the accuracy of early intervention targeting by allowing claims that will resolve without further intervention to do so. Predictive modeling further narrows the target population based on claim characteristics, reducing intervention costs. Before adopting a waiting strategy, however, it is important to consider potential trade-offs involved in delaying the start of any intervention.