50. Panel Size Management for Enhanced Patient Care: A Data-Driven Simulation Approach
Invited abstract in session HD-4: Poster session 2, stream Sessions.
Thursday, 13:30-14:00Room: St Olavs, Kunnskapssenteret KA12
Authors (first author is the speaker)
| 1. | Mina Moeini
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| Mathematics, Simon Fraser University | |
| 2. | Jessica Stockdale
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| Department of Mathematics, Simon Fraser University | |
| 3. | Alexander Rutherford
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| Department of Mathematics, Simon Fraser University | |
| 4. | Krisztina Vasarhelyi
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| Vancouver Coastal Health Research Institute | |
| 5. | Le Tuan Minh Nguyen
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| Simon Fraser University | |
| 6. | Jacob Umbach
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| Simon Fraser University | |
| 7. | Tarnjit McCauley
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| Vancouver Coastal Health | |
| 8. | Cassandra Djurfors
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| Vancouver Coastal Health | |
| 9. | Jenny Hamilton Harding
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| Vancouver Coastal Health | |
| 10. | Kenneth Hawkins
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| Vancouver Coastal Health | |
| 11. | Harleen Arora
|
| Vancouver Community Primary Care Program, Vancouver Coastal Health |
Abstract
Determining optimal panel size—patients assigned to a healthcare provider—is crucial for ensuring timely access to care and provider well-being. While conventional methods rely on population averages, they fail to address the unique needs of primary care in Community Health Centres (CHCs) serving biopsychosocial complex populations, including individuals experiencing homelessness, mental health conditions, and barriers to care. CHCs often operate with providers working less than full-time while managing complex cases.
In partnership with Vancouver Coastal Health, we developed a discrete event simulation (DES) model that simulates CHC operations as discrete events occurring over time. This approach captures the unpredictability of client arrivals and service demands, providing a predictive, adaptive representation of patient flow and provider workload.
Using three years of administrative data to model walk-in and booked appointments and no-shows, we determined optimal panel size ranges meeting key performance indicators, including time to next available appointment and frequency of multiple-patient overbooking. Our recommended panel sizes were consistently smaller than those produced by standard calculation methods, aligning with the complex care needs of CHC clients and the unique operational models of each center.
Our simulation model is a practical decision-support tool enabling CHCs to address capacity management challenges through data-driven decision-making.
Keywords
- Decision support
- Modelling and simulation
- Healthcare management
Status: accepted
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