147. Dynamic Predictive Modeling for Decision Support and Patient Management in Intensive Care Units
Invited abstract in session MD-3: ED and ICU, stream Sessions.
Monday, 13:30-15:00Room: NTNU, Realfagbygget R9
Authors (first author is the speaker)
| 1. | Daniel García-Vicuña
|
| Department of Statistic, IT and Mathematics, Public University of Navarre | |
| 2. | Ana María Anaya-Arenas
|
| Université du Québec à Montréal | |
| 3. | Janosch Ortmann
|
| GERAD, CRM and UQAM | |
| 4. | Angel Ruiz
|
| Department of Operations and Decision Systems, Universite Laval | |
| 5. | Fermin Mallor
|
| Institute of Smart Cities, Public University of Navarre |
Abstract
The limited availability of Intensive Care Unit (ICU) beds highlights the critical challenges hospitals face when demand exceeds capacity. In such contexts, healthcare providers must make complex decisions regarding admissions, discharges, and surgery scheduling—often under significant uncertainty and pressure. These decisions, while necessary, can lead to logistical bottlenecks and adverse patient outcomes. This work presents a dynamic predictive model that supports decision-making by anticipating ICU patient trajectories and enabling more efficient bed management. Built on real-world patient data, the model combines clinical variables and historical information to estimate ICU length of stay and evolving occupancy levels. The approach integrates predictive analytics with simulation techniques to evaluate the operational impact of different strategies under variable demand scenarios. Our results show that informed forecasting can help reduce premature discharges and surgical delays by guiding clinicians toward more balanced resource allocation. The model is designed as a practical decision-support tool to assist ICU managers in aligning patient care needs with available capacity, particularly in periods of high stress or crisis.
Keywords
- Decision support
- Modelling and simulation
- Forecasting
Status: accepted
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