Predicting the Future: Using Simulation Modeling to Forecast Patient Flow on General Medicine Units
BACKGROUND: Hospitals are complex adaptive systems within which multiple components such as patients, practitioners, facilities, and technology interact. A careful approach to optimization of this complex system is needed because any change can result in unexpected deleterious effects. One such approach is discrete event simulation, in which what-if scenarios allow researchers to predict the impact of a proposed change on the system. However, studies illustrating the application of simulation in optimization of general internal medicine (GIM) team inpatient operations are lacking.
METHODS: Administrative data about admissions and discharges, data from a time-motion study, and expert opinion on workflow were used to construct the simulation model. Then, the impact of four changes – aligning medical teams with nursing units, adding a hospitalist team, adding a nursing unit, and adding both a nursing unit and hospitalist team with higher admission volume – were modeled on key hospital operational metrics.
RESULTS: Aligning medical teams with nursing units improved team metrics for aligned teams but shifted patients to unaligned teams. Adding a hospitalist team had little benefit, but adding a nursing unit improved system metrics. Both adding a hospitalist team and a nursing unit would be required to maintain operational metrics with increased patient volume.
CONCLUSION: Using simulation modeling, we provided data on the implications of four possible strategic changes on GIM inpatient units, providers, and patient throughput. Such analyses may be a worthwhile investment to study strategic decisions and make better choices with fewer unintended consequences.
© 2019 Society of Hospital Medicine
A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,23 began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.
Hospital Data
We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.
Model Building and Internal Validation
On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.
What-If Scenario Testing
We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:
- constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
- increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
- increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
- modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).
