Clinical and Operational Risk: A Bayesian Approach

Chiara Cornalba ()
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Chiara Cornalba: University of Pavia

Methodology and Computing in Applied Probability, 2009, vol. 11, issue 1, 47-63

Abstract: Abstract In health care organizations (HCOs) adverse events may provoke dangerous consequences on patients, such as death, a longer hospital stay, and morbidity. As a consequence, HCO’s department needs to manage legal issues and economic reimbursements. Governances and physicians are interested in operational (OR) and clinical risk (CR) assessment, mainly for forecasting and managing losses and for a correct decision making. Currently, scientific researches, which are objected to a quantification of CR and OR in HCO, are scarce; absence of regulatory constraints and limited awareness of benefits due to risk management do not provide incentives to elaborate on how risks can be quantified. This paper is aimed at proposing Bayesian methods to manage operational and clinical adverse events in health care. Bayesian Networks (BNs) are useful for assessing risks given end stage renal disease (ESRD) as a context of application; some prior probability distributions are advised for representing knowledge before experimental results and Bayesian utility functions for making the optimal decision. The method is described as from the theoretical as from the empirical point of view, thanks to the health care and haemodialysis department, for this application. The ultimate goal is to introduce a methodology useful for managing operational and clinical risk for haemodialysis patients and departments.

Keywords: Bayesian Network; Bayesian utility function; Clinical risk; Operational risk; Prior probability distribution; 62C10; 91B30; 35B45; 91B16 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s11009-007-9068-9

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