Methods Inf Med - A probabilistic method for computing quantitative risk indexes from medical injuries compensation claims.

Tópicos

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Resumo

CKGROUND: The increasing demand of health care services and the complexity of health care delivery require Health Care Organizations (HCOs) to approach clinical risk management through proper methods and tools. An important aspect of risk management is to exploit the analysis of medical injuries compensation claims in order to reduce adverse events and, at the same time, to optimize the costs of health insurance policies.OBJECTIVES: This work provides a probabilistic method to estimate the risk level of a HCO by computing quantitative risk indexes from medical injury compensation claims.METHODS: Our method is based on the estimate of a loss probability distribution from compensation claims data through parametric and non-parametric modeling and Monte Carlo simulations. The loss distribution can be estimated both on the whole dataset and, thanks to the application of a Bayesian hierarchical model, on stratified data. The approach allows to quantitatively assessing the risk structure of the HCO by analyzing the loss distribution and deriving its expected value and percentiles.RESULTS: We applied the proposed method to 206 cases of injuries with compensation requests collected from 1999 to the first semester of 2007 by the HCO of Lodi, in the Northern part of Italy. We computed the risk indexes taking into account the different clinical departments and the different hospitals involved.CONCLUSIONS: The approach proved to be useful to understand the HCO risk structure in terms of frequency, severity, expected and unexpected loss related to adverse events.

Resumo Limpo

ckground increas demand health care servic complex health care deliveri requir health care organ hcos approach clinic risk manag proper method tool import aspect risk manag exploit analysi medic injuri compens claim order reduc advers event time optim cost health insur policiesobject work provid probabilist method estim risk level hco comput quantit risk index medic injuri compens claimsmethod method base estim loss probabl distribut compens claim data parametr nonparametr model mont carlo simul loss distribut can estim whole dataset thank applic bayesian hierarch model stratifi data approach allow quantit assess risk structur hco analyz loss distribut deriv expect valu percentilesresult appli propos method case injuri compens request collect first semest hco lodi northern part itali comput risk index take account differ clinic depart differ hospit involvedconclus approach prove use understand hco risk structur term frequenc sever expect unexpect loss relat advers event

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