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Using Discrete-Event Simulation to Model Web-Based Crisis Counseling Service Operation: Evaluation Study.
Byron Chiang1, Yik Wa Law1,2, Paul Siu Fai Yip1,2
1Centre of Suicide Research and Prevention, University of Hong Kong, Hong Kong, China (Hong Kong).
JMIR Formative Research
|August 7, 2024
Summary
This study used discrete-event simulation to optimize crisis counseling staffing, increasing service conversion rates by 18% and improving response times for users seeking mental health support.
Area of Science:
- Operations Research
- Public Health
- Computer Science
Background:
- The COVID-19 pandemic exacerbated mental health issues globally, increasing demand for digital counseling services.
- Crisis intervention platforms faced challenges managing increased user volumes and diverse user needs.
- Optimizing resource allocation is crucial for non-profit counseling services with limited capacity.
Purpose of the Study:
- To assess the queuing performance of a 24-hour text-based crisis counseling platform.
- To develop a discrete-event simulation (DES) model to evaluate optimal staffing combinations.
- To inform service priority decisions and manage demand-supply equilibrium.
Main Methods:
- Utilized historical database records for user and queue statistics.
- Developed a Python-based discrete-event simulation (DES) model.
- Incorporated time-dependent user arrivals, varied worker capacities, and user types (repeat/non-repeat) into the model.
- Employed time-series forecasting for arrival rate prediction.
Main Results:
- Strategic human resource deployment, guided by DES simulations, increased the overall conversion rate from 36.96% to 54.81%.
- Achieved an 85% probability of users receiving counseling within 10 minutes.
- Freed up 39.57% additional capacity for high-risk, non-repeat users.
Conclusions:
- Discrete-event simulation (DES) models enable data-driven optimization of service capacity for web-based counseling platforms.
- Non-profit organizations can strategically manage bottlenecks and enhance service uptake, even with resource constraints.
- Scientific data modeling is key to improving efficiency and accessibility in mental health crisis support.
