Introduction: The delay in the processing of sick leaves (SLs) is a public health problema in Chile, considering that this affects the payment of the subsidy to the individuals destined to perform the prescribed medical rest while unable to work. The aim of this study was to explore the differences in the processing time of electronic SLs (ESLs) evaluated by medical audit (MA) and the SLs evaluated by a predictive medical audit system (PMAS) based on artificial neural networks. Materials and methods: The processing time of the ESLs that were processed by PMAS was compared with the processing time of those that were examined only by MA, using Kaplan Meier
curves, log-rank test, and multivariate Cox models. Results: The processing rate for PMAS was 1.7-fold to 5.5-fold faster than MA, after adjusting for potential confounding variables. Discussion: The implementation of the PMAS reduced the processing time of ESLs, which benefits the workers affiliated to the public insurance system in Chile.
Keywords:
Sick leave, Artificial intelligence, Medical audit, Government financing, Chile
Bernales, B., Bravo, S., Causa, L., Gómez, N., & Valdés, M. (2021). Contributions of a predictive medical audit system in the electronic management of sick leaves. Revista Chilena De Salud Pública, 24(2), p. 115–126. https://doi.org/10.5354/0719-5281.2020.61265
