Medical Device/Healthcare Simulation

THE PROBLEM

As the average age of the United State population gets older, certain cancer screening test have

become more common. As a result, the need to increase capacity to service more patients each

day has become a primary concern. The current testing procedure uses a diagnostic device that

is expensive and requires extensive patient set-up and prep time. In addition, the device requires

skilled labor to disassemble, clean, reassemble, and sterilize it. Due to current protocols, each

device can only be used two times per day.

A new disposable device has been developed to facilitate a reduction in the patient prep time and

eliminate costly refurbishment of the current permanent device. The disposable device is less

expensive than the permanent device but is used only once. This raised some key questions for

potential clients.

If the disposable device is used and patient test rates increase by 40 percent, will the additional

revenue be enough to cover the cost of using the device? Second, with the increase in patient

flow, can the current configuration of the same day surgical centers accommodate the increased

demand on patient prep and recovery?

The medical device manufacturer decided that a simulation model would prove to hospitals and

clinics that the new disposable device would provide more revenue than the current method.

BUILDING THE MODEL

The simulation model was designed so that the manufacturing representatives could quickly

configure it using an Excel spreadsheet to figure in the arrival of patients, capacity of patient

waiting/prep areas, surgical bays, recovery stations, and staffing requirements. It produces a

model of the current process and procedure, comparing it against the proposed process and

procedure using the new disposable device. Animation shows both scenarios side by side while

data is exported to comparable spreadsheets.

Members of the medical device manufacturing company and engineers from Flexsim Software

Products Inc. developed the model. Team members were assigned specific responsibilities such

as data preparation and gathering, scenario development, and device configuration. Once the

model was completed, extensive peer review assessed the validity of the data inputs, model

assumptions, and output results. Data sets were evaluated and prepared for quick model

configuration at potential sites.

RESULTS

Results were categorized into two main groups: facility utilization and financial benefits. Facility

utilization reported things like patient wait times, patient check-in and paperwork times, patient

prep time, prep room usage, surgical bay usage and turnaround time, recovery, discharge, and

clinic staffing. Financial benefits compared the cost of resources, staffing, and equipment as a

function of the increase n the patient procedures. These costs were aggregated for each

scenario and then compared.

The results showed that changes in testing protocol and staffing were needed when the

disposable device was used. The costs for some of these changes were not predicted before the

simulation model was used. Bottlenecks in the recovery process due to the increase in patient

flow had to be resolved. The model was able to show the impact of increased numbers of

patients, available cost savings, and procedure changes needed to facilitate the use of the

disposable device. The bottom line was a 20 percent increase in revenue over and above the

added costs of changing from the current testing procedure. This was less than the estimated

increase before the simulation model was developed, but it was well within the acceptable range

to implement the new procedure.

SCREEN SHOT OF MODEL