Everyone is enjoying the summer, Covid19 restrictions have been lifted, we all get back to regular exercise and outdoor activities. But once in a while, the inevitable happens: An ill-considered step, a brief second of inattention, and injuries all of all types will happen, that require immediate treatment. Luckily our city hosts a modern hospital with an efficient emergency room where the wounded are being taken care of.
To save more lives, the mayor has asked us to review and potentially improve process efficiency in the ER. To do so, we need to realize the following steps
- Understand the current process and model is as simulation
- Formulate key objectives to be optimized
- Assess process statistics and metrics, to unravel potential improvements to help more patients.
- Explore more optimized decision policies to increase
So let's dive right into it without further ado.
Patients are classified two-fold 1. By Severity. The ER is using the well known Emergency Severity Index to triage patients based on the acuity of patients' health care problems, and the number of resources their care is anticipated to require. 2. Type of injury which are defined here
- Surgery rooms that must be equipped by considering the type (i.e., the family) of surgery to be performed. It will take time to prepare a room for a certain type of injury. These setup times are listed in an excel sheet.
- Doctors that are qualified for a subset of all possible injuries
- PD-A Depending on the severity, patients might die if not being treated. Also, if not being treated their severity will increase rather quickly
- PD-B The more busy the waiting room is, the less efficient surgeries tend to be. This is because of stress (over-allocation of supporting personal and material). It is phenomenon that is often observed complex queuing processes such as manufacturing or customer services.
- PD-C Depending on the severity, patients will die during surgery
- PD-D The surgery time correlates with the severity of the injury
- PD-E During nights fewer new patients arrive compared to the day
Clearly, more resources are required in the ER and many supported processes are required to run it. However, we leave these out here, as they are not considered to have a major impact on the overall process efficiency. Choosing a correct level of abstraction with a focus on key actors and resources, is the first key to success when optimizing a complex process.
The tick-unit of the simulation is hours.
Key Objectives & Observations
The head nurse, who is governing the process based on her long-term experience, is scheduling patients based on the following principle
Most urgent injuries first
Clearly if possible it would be great to also * Minimize waiting times * Reduce number of surgery room setups
Because of the great variety rooms, we observe a lot of setup steps to prepare surgery rooms. Often even if patients with the same type of injury all already waiting.
The idea for model above was orginally formulated by Kramer et al. in 2019 :
Other relevant applications arise in the context of health-care, where, for example, patients have to be assigned to surgery rooms that must be equipped by considering the type (i.e., the family) of surgery to be performed. In such cases, the weight usually models a level of urgency for the patient. */
Conclusion & Summary
In this article we have worked out a complex process with partially non-intuitive process dynamics can be modelled with kalasim and optimized using insights from operations research.
Disclaimer: The author is not a medical doctor, so please excuse possible inprecsion in wording and lack of ER process understanding. Feel welcome to suggest corrections or improvements