Modelling of risk management in health care facilities: Fid-Bau Portal

Modelling of risk management in health care facilities

Okoroh, M.I. / Ilozor, B.D. / Gombera, P.P.

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To evaluate the use of neural networks in healthcare facilities risk management.

Design/methodology/approach –

The data used to develop the input to the national health service facilities risk exposure system (NHSFRES) was solicited from 60 healthcare managers. Risk exposure system has been developed using the risk knowledge that was articulated from experienced healthcare operators through postal questionnaires and repertory grid interviews. This knowledge was then transformed and represented in Trajan 4.0, an expert system shell that uses artificial neural networks as its modelling technique.

Findings –

It provides healthcare facilities operators an avenue to evaluate their own risk management method (point score system) based on their own healthcare business knowledge/judgment and corporate objectives for various FM service operations.

Research limitations/implications –

The key issue that should always be noted by NHSFRES users is that, the concept of measuring or evaluating business risks will always be uncertain. Professional judgment, based on sound information, is an essential element in interpreting and using the system.

Practical implications –

The model provides healthcare facilities managers a vehicle for predicting pre- and post-facilities risk-factors in healthcare operations before they occur. A clear understanding of the risk signals would mean that appropriate management course of action should to be considered that will improve FM operators' business performance.

Originality/value –

The NHSFRES is developed using the risk knowledge that was articulated from experienced healthcare operators through postal questionnaires and repertory grid interviews. It provides a reasonable early warning signal to the healthcare managers, and can be used by decision makers to evalute the severity of risks on healthcare facilities business operations.