Engineering Healthcare: Six Sigma and Computer Simulation in an Emergency Department
|dc.contributor.author||Roberts, Lance ( )|
|dc.identifier.citation||Roberts, L. (2004). Engineering healthcare: Six sigma and computer simulation in an emergency department (Unpublished thesis). Texas State University-San Marcos, San Marcos, Texas.|
This project used a combination of a computer based simulation tool, specifically the ProModel MedModel healthcare computer simulation tool, and a statistical-based continuous improvement methodology, Six Sigma, to model/analyze the daily operations of the Emergency Department (ED) at Central Texas Medical Center (CTMC) in San Marcos, Texas. The goal was to use these tools in an effort to decrease the variation in the length of stay (LOS) for its patients. By decreasing the variation in LOS the resultant impact should be a reduction in the number of highly dissatisfied customers and a reduction in the mean LOS. In addition, there were two business goals - to increase the quality of care (especially in regards to reducing the number and sources of errors) and to decrease the amount of time to disposition a patient, especially in cases of admissions to the hospital.
Over the last seven months the Six Sigma project team used the Six Sigma DMAIC methodology (Define, Measure, Analyze, Improve, and Control) to identify several improvement projects that were targeted to address inefficient or ineffective ED processes. The team used this tool to systematically identify six major process inputs that needed improvement: the chart system for tracking patients within the ED process, the personnel that supported these processes, physician communication with patients and internal support personnel, diagnostic results (both laboratory and radiological processes), materials, and the equipment/supplies used to support the ED processes. The hospital is using the results of the Define, Measure, and Analyze phases to begin making improvements in their business processes. Some of the Improvement phase projects include the redesign of their materials supply rooms, redesigning the chart system used to track the progress of patients through the ED, redesigning the process for forwarding patients in the waiting room into the emergency room area, analyzing the root cause for variability in laboratory turn-around-times (TAT), writing new operating procedures for the transportation of patient samples to the laboratory, and using communication technology to improve the communication between personnel in the department.
Some of the early results show that the new Triage to ED bed process is dramatically reducing the variation in patient wait time for this portion of the entire process. This improvement is reducing the variation in patient lengths of stay. And, this decrease in wait time variability causes a concurrent, downward shift in the mean wait time. Thus, patients are spending much less time in waiting which has resulted in a decrease in the number of patients leaving the ED without being seen (LWBS). The project team found that the new, improved Triage to ED bed process decreased the number of LWBS patients from 32 to 16. Therefore, based on the average revenue generated from an ED patient of $505.67, the new process could generate an additional $97,089 per year! In addition, a root cause analysis of the variability in lab TATs pinpointed one, specific type of lab order as a culprit for the variability in TAT. Eliminating this cause of “special variation” in the process will reduce patient length of stay. Also, the redesign of the supply room and the use of communication technology are expected to make patient care more efficient. Once the implemented improvements are stable, the hard-won improvements will be controlled in the Control phase. Simple control charts, such as the x-bar and p-charts are to be used to keep the new processes under control.
Additionally, a computer simulation tool was used to explore several alternative process improvement scenarios. This tool is a great choice for engineers when they need to explore potential solutions that would be difficult to pilot in any “real” sense. The level of difficulty associated with a proposed change render solutions that may be too time intensive, cost prohibitive, or risky to pursue. The level of risk level may prevent feasible solutions from being tried at all. Conversely, experimentally changing a particular process only to find that the return was less than the investment would be worse. It would waste valuable time and resources. In this study an “as-is” model of the current ED system was built and analyzed; then it was compared to several “what-if ’ models to explore the effects of the following business scenarios: the addition of business hours for the ED’s Minor Emergency Clinic (MEC), the installation of a PACS system to eliminate the transportation of x-ray film, the addition of a single Hematology Technician to draw and transport laboratory samples from the ED area to the lab, and new bedside registration and discharge processes. The computer simulations showed that the addition of a single Hematology Technician and the addition of the new bedside registration/discharge process did not significantly reduce patient LOS. However, computer simulations show that there were statistically significant reductions in the overall, mean patient LOS in the other two scenarios - the addition of MEC hours and PACS system installation scenarios. These two simulation models demonstrated a 3.3% and 3.8% reduction in the overall, mean patient LOS for each scenario respectively. The time, cost, difficulty, and risk levels are high for these types of proposed process improvements. Thus, computer simulation is an essential and valuable tool in assessing the effect of these business changes without actually changing the existing system.
|dc.format.medium||1 file (.pdf)|
|dc.title||Engineering Healthcare: Six Sigma and Computer Simulation in an Emergency Department|
|thesis.degree.department||Health Services and Research|
|thesis.degree.grantor||Texas State University--San Marcos|
|thesis.degree.name||Master of Science|
|dc.description.department||Health Information Management|
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