Abstract
Healthcare systems find it difficult to decide on how long they should wait between submitting insurance claims and following up on the Accounts Receivables (AR) for the submitted claims. The solution to this can be the study of payer-specific historical data to understand payment trends of the submitted claims. This data may include censored data points where the responses were not received from the payers and hence is appropriate to be analyzed with Survival Analysis. To aid the follow-up process on submitted claims, we developed and validated a Time to Response (TTR) survival models using the machine learning method Random Survival Forests (RSF) for Medicare, Medicaid, and Commercial payers. TTR models aim to streamline the insurance claim follow-up process for smoother functioning of healthcare systems. These models capture the previous response time patterns based on the covariates and predict the probability of getting a response for each claim filed, from the day of claim file submission. We studied the effect of demographic, geographic, date-time related field, diagnoses, procedural and clean claim covariates on TTR. The model performance was assessed in terms of Concordance Index (C-index) and Integrated Brier score (IBS). Train and test C-index of Medicare was 0.68 and 0.67, Medicaid 0.74 and 0.74, Commercial 0.76 and 0.75 respectively. Test IBS was reported as 0.02, 0.01 and 0.06 for Medicare, Medicaid and Commercial payers respectively.
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Chaudhuri, R., Parsa, S.P.K., Nagpal, D., R, K. (2022). Time to Response Prediction for Following up on Account Receivables in Healthcare Revenue Cycle Management. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_11
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