OBJECTIVES: To construct a simulation model of the hospital drug distribution system, to evaluate the effects of different changes to the system on unavailability-related medication administration errors (U-MAEs), to test the most promising system in a controlled trial and to explore the model's validity. METHODS: A discrete-event simulation model of the drug distribution system was constructed, using data collected on a vascular surgery ward and a renal medicine ward as model inputs. The model's output was the U-MAE rate. The model was used to explore the effects on U-MAEs of different changes to the system. One of the changes predicted to reduce U-MAEs, a patients' own drugs scheme, was introduced on each study ward; U-MAE rates were measured using observations before and after its introduction and compared with those predicted by the model. RESULTS: The model predicted that the introduction of a patients' own drugs system would reduce unavailability-related errors on each ward; in practice, there was a slight decrease on the medical ward but an increase on the surgical ward. Reasons for these findings were explored and four contributing factors identified. Three of these related to failure to follow hospital procedures, the fourth to an issue for which no policy existed. If these factors had been taken into account, the error rates predicted by the model would have been similar to those observed. CONCLUSIONS: Simulation modelling is a potentially useful approach to the study of U-MAEs, although care must be taken to ensure that such models reflect actual practice rather than stated policy.
OBJECTIVES: To construct a simulation model of the hospital drug distribution system, to evaluate the effects of different changes to the system on unavailability-related medication administration errors (U-MAEs), to test the most promising system in a controlled trial and to explore the model's validity. METHODS: A discrete-event simulation model of the drug distribution system was constructed, using data collected on a vascular surgery ward and a renal medicine ward as model inputs. The model's output was the U-MAE rate. The model was used to explore the effects on U-MAEs of different changes to the system. One of the changes predicted to reduce U-MAEs, a patients' own drugs scheme, was introduced on each study ward; U-MAE rates were measured using observations before and after its introduction and compared with those predicted by the model. RESULTS: The model predicted that the introduction of a patients' own drugs system would reduce unavailability-related errors on each ward; in practice, there was a slight decrease on the medical ward but an increase on the surgical ward. Reasons for these findings were explored and four contributing factors identified. Three of these related to failure to follow hospital procedures, the fourth to an issue for which no policy existed. If these factors had been taken into account, the error rates predicted by the model would have been similar to those observed. CONCLUSIONS: Simulation modelling is a potentially useful approach to the study of U-MAEs, although care must be taken to ensure that such models reflect actual practice rather than stated policy.
Authors: Matthew Reynolds; Christos Vasilakis; Monsey McLeod; Nicholas Barber; Ann Mounsey; Sue Newton; Ann Jacklin; Bryony Dean Franklin Journal: Health Care Manag Sci Date: 2011-02-23