298. Enhancing Scheduling Efficiency in Interventional Radiology through Optimization and Predictive Process Monitoring
Contributed abstract in session TA-5: Operating Room Scheduling /1, stream Regular talks.
Tuesday, 9:00-10:30Room: Room S6
Authors (first author is the speaker)
| 1. | Massimiliano Ronzani
|
| Fondazione Bruno Kessler | |
| 2. | Matteo Di Cunzolo
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| Dipartimento di Informatica, Università degli Studi di Torino | |
| 3. | Roberto Aringhieri
|
| Dipartimento di Informatica, Università degli Studi di Torino | |
| 4. | Chiara Di Francescomarino
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| Fondazione Bruno Kessler | |
| 5. | Chiara Ghidini
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| Process and Data Intelligence, Fondazione Bruno Kessler | |
| 6. | Alberto Guastalla
|
| Dipartimento di Informatica, Università degli Studi di Torino | |
| 7. | Emilio Sulis
|
| Computer Science Dep., University of Torino |
Abstract
Interventional Radiology (IR) is a growing medical field using imaging technologies for minimally invasive procedures. Despite similarities to operating rooms, IR poses unique management challenges seldom addressed in literature. This study introduces a novel approach, integrating optimization and predictive process monitoring (PPM) models, to address specific IR complexities. Results demonstrate improved scheduling efficiency and patient throughput compared to traditional methods, highlighting the effectiveness of PPM integration.
Keywords
- Operating room planning and scheduling
- Patient scheduling
- Optimization algorithms
Status: accepted
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