🏥 Engineering Problem Solving – ER Simulation Example
1. Define the Problem
2. Identify Key Factors
3. Propose a Model
4. Collect Data
5. Refine the Model
6. Solve the Problem
7. Test the Solution
8. Make Recommendations
1. Define the Problem
Develop a clear and concise statement of the issue. What exactly needs to be solved or improved?
🩺 ER Example: The hospital emergency room is experiencing excessive wait times during peak hours, leading to patient dissatisfaction and reduced quality of care.
2. Identify Key Factors
Determine which variables or components influence the problem.
🩺 ER Example: Factors may include patient arrival rate, triage process speed, number of available doctors, nurse availability, and bed capacity.
3. Propose a Model
Create a conceptual or mathematical model using engineering knowledge. Include assumptions.
🩺 ER Example: Use a queuing model (e.g., M/M/1 or M/M/c) to represent patient arrivals and service rates. Assume patient arrivals follow a Poisson distribution and service times are exponentially distributed.
4. Collect Data / Test Model
Run experiments or gather real-world data to test the model’s validity.
🩺 ER Example: Collect arrival time data over several weeks, measure average service time per patient, and record number of active staff per shift.
5. Refine the Model
Adjust the model based on the observed data to improve its accuracy.
🩺 ER Example: After reviewing data, add variables for patient severity (triage level) and adjust the model to include priority queues or parallel servers.
6. Solve the Problem
Use the model to test solutions and optimize outcomes.
🩺 ER Example: Simulate adding one more nurse during peak hours and observe that the average wait time drops by 40% in the model.
7. Test the Solution
Conduct a real-world pilot or test run to see if the solution works effectively and efficiently.
🩺 ER Example: The hospital adds an extra nurse from 5–9 PM for two weeks and measures wait time improvements in real conditions.
8. Make Recommendations
Conclude based on your findings and recommend next steps.
🩺 ER Example: Recommend scheduling more flexible staff during peak hours and re-evaluating triage software to maintain reduced wait times long-term.