Comparativo de resultados a partir de diferentes metodologias para o cálculo da provisão para eventos ocorridos e não avisados - peona

Abstract

Since the Federal Constitution of the 1988 and the creation of the Unified Health System, the Brazilian health sector is undergoing major changes. Once identified weaknesses in services provided by the Unified Health System, many Brazilians have come to join the private health plans sold by Carriers of Health Plans. Thus, it became necessary the regulate and control the industry, started from the Law 9656 of 1998 and intensified by the creation of the National Health Agency. The Agency imposes a regulatory and surveillance action causing some carriers of Health Plans have difficulties have to meet certain requirements. Among the obligatorily may be cited as the technical provisions provided, more recently, in normative resolution Nº. 209. The provision to be studied in this work is the Provision for Events Incurred but not Reported (PEONA), established to ensure the commitments assumed by the Carriers of Health Plans, which have occurred and have not been notified to the Operator. To ensure the provision of such provision, the ANS suggests an optional method of calculation, but the operators can seek their own methodology in order to find an estimate that is closer to reality, to obtain the smallest possible outlay to ensure the discharge of commitments. As objective of this study, estimates were calculated using alternative models of computation through deterministic and stochastic methods that use the technique of Run-Off, such as: Chain Ladder, Bornhuetter-Ferguson, Benktander and Monte Carlo simulation. The calculations were based on data processed in a Health Care Provider in the city of Fortaleza, using a demonstration version of the software @ Risk. After comparison of the results for each method, it is concluded that for the data actually used and on the OPS study, the simulation technique provides satisfactory results of the analyzes on the consistency of results.