Most hospitals and clinics don’t have the luxury of experimenting with operational changes that might affect the quality of patient care. So how can these facilities enact continuous improvement solutions without incurring the wrath of healthcare providers and recipients?

Process simulation, or designing a model that can mimic the real-world care delivery process, has stepped forward as an option. Since hospitals typically have a lot of structured and unstructured data available, ranging from volume and time data to detailed patient records, that data can be used to build a process flow model.

Within the model, operational changes can be tested, observed and fully optimized before they are actually instituted within the facility, minimizing impact on patients and medical personnel.

The Alacer team recently created 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.

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 data acquisition was complete, the existing environment was digitally replicated. We then 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, etc. On the seventh try, we found the formula that would deliver the best operational results for the clinic — and it resulted in a 98.8% reduction in cycle time and a whopping 188.1% reduction in patient wait times.

There probably isn’t a single panacea for curing healthcare operational woes. But with this one example, it’s clear to us that simulation modeling can be successfully used to develop new and more profitable ways of managing healthcare delivery processes and optimizing capacity.

 by Richard Paxton