Capacity planning simulation for medical staffing
Deliverables
Predicting staffing needs for remote monitoring
Introducing a new technology into the hospital meant thinking about the humans - nurses and surgeons - involved. How would the remote patient monitoring affect staffing? How many nurses and surgeons would need to be available for these patients, now they were being treated at home?
Methodology
Together with my colleague researcher, an MD, I built on the previous static Visio charts to render a simulation model in the simulation software Arena. We wanted to forecast staff capacity based on various simulated scenarios.
Modeling the Simulation using Queuing Theory
The most critical points in correctly estimating these fluctuations in staff was identifying escalation points. While the activities were often easy to map, there were instances where decision-making would automatically be made by the system. Getting these moments right meant having an accurate (and safe) model.
Findings
The model we built was later used by the medical team in implementing the remote monitoring solution. For the first time, what happens when a post-surgery patient leaves the hospital is no longer a blackbox. Medical teams are now able to collect information long before patients are re-admitted. By continuously learning from this new data, the surgeons are now able to more accurately treat recovering patients from home, sparing them the trip to the hospital. They can also intervene earlier and with more accuracy, reducing the re-admission rates.
Link to Scientific Journal
You may find the article, published in the Applied Clinical Informatics Journal, here.
GitHub Project (Public)
Repository (Private)
My Role:
Researcher
Analyst
Project Manager