Using Discrete-Event Simulation to Reduce the Incidence of Medical Errors from a Point of Distribution Site
The objective of this research was to develop a computer simulation model that will effectively and efficiently provide the most optimal allocation of resources for a POD site that also minimized medication errors. Using computer-modeling software, a computer model was built with four stations: Triage, Registration, Screening, and Dispensing. The model simulated a 10.1% medical error rate, as seen by a previous POD exercise. The error rate was calculated using observations from the rates of error between two groups of a spring 2015 POD exercise: synchronous groups, and asynchronous groups. Both groups were observed for errors, at screening and at prescribing. Relative risk (RR) was calculated using the number of errors as the outcome. The combination of the two groups produced a RR of 1.26 (CI 0.546, 2.906, p=0.588). On the day of the experiment, the researcher inputted the total number of volunteers into the model, and the model generated the most applicable ratio for distribution of human capital: a one to one ratio of screeners to dispensers. The remainder of the volunteers were divided into two groups, Group B, experimental and Group A, control. Time was recorded using a digital time-stamp at the beginning and at the end of the POD. Observers measured the time required to complete the exercise and determined the number medical errors. Group B had an additional verification station, where a pharmacy students examined the registration forms, as well as the medication dispensed. If there were errors, the verification station made the appropriate corrections prior to allowing the client to depart. Once the data were collected, a two-sample t-test was used to determine the significance of the difference between the average times of the two groups to complete the POD. A Fishers Exact test was used to determine if there was a significance difference in the percent of errors of the between the two groups.
Dietz, Purdue University.
Public health|Information science|Health care management|Computer science
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