Simulating a cure for clinical operations
By Richard Paxton, June 18, 2013
How process simulation can analyze and help cure real-world challenges such as capacity and throughput.
Process simulation, or designing a model that can represent a real-world process or system over time, has been used for decades in many industries to test everything from safety engineering to technology performance. As hospitals and clinics push more strongly into continuous improvement modes and look for ways to cut costs without impacting patient care, process simulation has stepped forward as an option. Since experimenting with operational changes in patient care areas can have unforeseen and unpredictable results, process simulation can virtually mimic the care delivery process, enabling “What if?” scenarios. This can include analyses of real-world challenges such as addressing capacity issues and throughput without disruption to staff and patients.
Process improvement in healthcare is not new. For years, organizations have been making targeted improvements in wait time, quality and standardization using proven tools and techniques such as Lean Six Sigma. The challenge comes in implementing change (process improvement solutions) in an environment with no room for error. And that’s where the beauty of simulation modeling can come into play: Operational changes can be tested, observed and fully optimized before they are instituted in real time, minimizing impact on patients and medical personnel.
Recently, the Alacer Group had the opportunity to create a simulation model of a clogged waiting room within a major Midwestern hospital’s outpatient pediatrics clinic. Over half of the “well baby” appointments took longer than their allotted time, resulting in needed follow-up visits to complete tests and vaccinations – amounting to over $400,000 in lost revenue annually. It was quickly apparent that the right resources were not in the right places at the right times to support patient surges and deliver maximum throughput. Adding to the complexity was the fact that it was a teaching hospital, where residents needed the advice of the attending physician and to have scheduled hands-on experience with patients.
The first step in examining any problem, whether it is within the hospital setting or not, is to acquire historical data. Hospitals have a lot of structured and unstructured data available, ranging from the volume and time data to detailed patient records. If that data is digitized and readily accessible, building a process flow model is straightforward.
The existing system then needs to be questioned and cataloged. At the outpatient pediatrics clinic, significant daily wait times for “well baby” checkups increased throughout the morning, causing a ripple effect; as a result, hospital administrators were not scheduling appointments at mid-day to accommodate these delays before entering the afternoon period. This practice eventually became accepted as the norm, despite the fact that patient flow is a key driver of revenue, patient satisfaction and the quality of care.
To identify pain points and areas of opportunity, the Alacer team conducted a detailed assessment of the site, gathered data and documented potential improvement opportunities. Some of these deliverables included an examination of staffing schedules, time/motion studies, an inventory review (process/aging/ordering), flow maps (patient/physician/staff) and department-specific performance data, such as wait time.
Once the data is acquired, the existing environment can be replicated. Sometimes, the model can even be designed before the data acquisition is complete and just tweaked as new data is available. And when the event simulation is in place, existing assumptions can then be verified and the process of identifying problems and bottlenecks can begin.
For this outpatient pediatrics clinic, patient flow was described in up to seven steps:
- Patient placement in a room and vitals obtained
- A visit with a resident physician
- Resident physician meets with attending physician (if required)
- Attending physician meets with patient (if required)
- Completion of orders
- Schedule follow-up visit
The cataloged available resources included:
- One attending physician and four resident physicians
- Three service reps, two patient care technicians and two nurses
- Reception desk, patient waiting area, vitals and testing area, and six patient rooms (1.5 rooms per resident)
- Hours of operation: Monday through Friday, eight hours per day
By inputting actual costs, facility features and square footage; number of nurses and physicians; as well as available time, the simulation model can be extremely accurate and predictive of what the end results will be. A few of the questions hospital administrators and the Alacer team wanted to explore were:
- Are 1.5 rooms enough for each resident?
- If visits are 20 minutes long, can patients be scheduled at 20-minute intervals?
- What is the patient cycle time, and how long do patients wait? What should the staffing mix and levels be to support the clinic’s maximum capacity?
The Alacer team created multiple simulation modeling “What if?” scenarios that changed the number of patients arriving at any given time, the number of nurses available, the length of time a resident worked with a physician and more. The following table provides an example of the first scenario that established a baseline of five patients every 20 minutes.
The Alacer team and hospital administrators quickly learned that scheduling patients at 20-minute intervals did not take into consideration the time required for other processes, including resident instruction. In this scenario, the recommendation was to reduce the patient arrival rate to four every 30 minutes to more closely match the clinic’s capacity – but patient wait times still averaged 1.69 hours.
It took seven analyses to determine what would deliver the best results for the outpatient pediatrics clinic, as demonstrated in the following table.
This model showed that the clinic could dramatically improve patient value by adding a fifth third-year resident to the clinic’s staffing model and by balancing the productivity of the first-year resident with a six-room setup. The improvement to the clinic’s existing performance could be dramatic, even though the number of patients seen remained constant (36 versus a baseline of 37):
- Reduction in cycle time = 98.8 percent
- Reduction in patient wait times 188.1 percent
Through simulation modeling, hospital administrators could also quickly understand the problems that were impacting the clinic. For example, first-year residents proved to be unproductive; in contrast, third-year residents took the least amount of supervision, needing a consult with the attending physician only 25 percent of the time. By adding one third-year resident, there was a dramatic improvement in productivity. This would require an additional nurse and the restructuring of the patient rooms so that the third-year residents had dedicated facilities, but revenue generation would increase.
Once in place, the changes at this hospital were impressive. After completing the modeling exercise and instituting operational changes, 87 percent of patient visits were completed within their allotted time. This resulted in the hospital’s ability to book more patients, generating $560,000 in new revenue. Additionally, as efficiency increased and the need for follow-up visits decreased, $400,000 per year in lost revenue was eliminated.
There probably isn’t a single panacea for curing healthcare operational woes. But simulation modeling is playing a bigger role in developing new and more profitable ways of managing care delivery processes and optimizing capacity.
About the author
Richard Paxton is the CEO of Seattle-based The Alacer Group and is the practice principal for financial services, insurance and healthcare in all geographic markets. He can be reached at Richard@alacergroup.com.
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