Batch model for batched timestamps data analysis with application to the SSA disability program
Qingqi Yue*, NIH; Ao Yuan, NIH; Xuan Che, NIH; Elizabeth Rasch, NIH; Minh Huynh, Impaq; Chunxiao Zhou, NIH
The Oﬃce of Disability Adjudication and Review (ODAR) is responsible for holding hearings, issuing decisions, and reviewing appeals as part of the Social Security Administration’s disability determining process. In order to control and process cases, the ODAR has established a Case Processing and Management System (CPMS) to record management information since December 2003. The CPMS provides a detailed case status history for each case. Due to the large number of appeal requests and limited resources, the number of pending claims at ODAR was over one million cases by March 31, 2015. Our National Institutes of Health (NIH) team collaborated with SSA and developed a Case Status Change Model (CSCM) project to meet the ODAR’s urgent need of reducing backlogs and improve hearings and appeals process. One of the key issues in our CSCM project is to estimate the expected service time and its variation for each case status code. The challenge is that the systems recorded job departure times may not be the true job ﬁnished times. As the CPMS timestamps data of case status codes showed apparent batch patterns, we proposed a batch model and applied the constrained least squares method to estimate the mean service times and the variances. We also proposed a batch search algorithm to determine the optimal batch partition, as no batch partition was given in the real data. Simulation studies were conducted to evaluate the performance of the proposed methods. Finally, we applied the method to analyze a real CPMS data from ODAR/SSA.
Filed under: Clustering