132. Aiding decision making within Infection Prevention and Control (IPC): An Analytical and Simulation Modelling Approach
Contributed abstract in session TD-3: Patient flow, stream Regular talks.
Tuesday, 16:00-17:30Room: Room S2
Authors (first author is the speaker)
| 1. | Nicholas Jelicic
|
| CORU, UCL | |
| 2. | Sonya Crowe
|
| UCL | |
| 3. | Martin Utley
|
| Clinical Operational Research Unit, University College London | |
| 4. | Tom Monks
|
| University of Exeter Medical School, University of Exeter |
Abstract
Infection prevention and control (IPC) decisions play a critical role in mitigating the spread of infectious diseases in healthcare settings. These include cancelling visitor appointments, isolating infected patients and quarantining bays. While these measures are introduced to reduce the spread of infections, some put restrictions on patient movement and thus worsen patient flow, creating organisational and medical issues. IPC nurses and hospital bed managers are therefore faced with hard decisions about how to reduce the risk of infections spreading without unduly impacting patient flow.
We developed a Discrete Event Simulation model to determine the consequences of an IPC policy in terms of a set of metrics which captures the impact on the spread of infection and patient flow. We also developed a Continuous Time Markov Chain Model that was used to validate the simulation model and to give exact results for simple scenarios.
In this presentation, I will focus on one use case of our models and describe methods we have established to support decision making; for example providing general rules of thumb about when policies are preferable. I will describe how they might be used in practice, including the benefits and drawbacks of each.
This presentation is an exploration of how models can be used to aid healthcare workers with complex decisions, in this case relating to trade-offs between the potential impact of IPC decisions on infection spread and patient flow.
Keywords
- Patient flow
- Epidemiology and disease modelling
- Modelling and simulation
Status: accepted
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