36. Optimizing Inpatient Postoperative Therapy Scheduling: Integrating AI-assisted Physical Therapy
Invited abstract in session FA-12: Scheduling in Healthcare II, stream Health Care Management.
Friday, 8:45-10:15Room: H10
Authors (first author is the speaker)
| 1. | Lorenz Wagner
|
| University of Augsburg | |
| 2. | Christian Jost
|
| Operations Management, Technical University of Munich | |
| 3. | Sebastian Schiffels
|
| Wirtschaftswissenschaftliche Fakultät, Universität Augsburg | |
| 4. | Jens Brunner
|
| Department of Technology, Management, and Economics, Technical University of Denmark |
Abstract
At hospitals, receiving postoperative therapy is essential after lung cancer, cardiac, and orthopedic surgeries. However, the limited availability of specialized therapists often delays treatments and prolongs hospital stays. To address this challenge, companies offer visual AI assisted therapy to supplement in-person therapy. This creates new opportunities for the efficient scheduling of therapy sessions. Our research offers a comprehensive optimization model to derive therapy schedules that integrate traditional in-person and AI-assisted therapy. The proposed mathematical model minimizes the Length of Stay, contingent upon the treatment effectiveness of combined therapy and AI sessions, intending to optimize therapist resource utilization. To efficiently solve large-scale scheduling instances, we introduce a Column Generation heuristic. Computational experiments using real-world data from our practice partner, Breathment, demonstrate that our approach can effectively complement traditional physiotherapy, providing a scalable solution to address therapist shortages while maintaining high-quality patient care. This research contributes to healthcare operations research by offering practical solutions for hospitals to optimize their physiotherapy services through technology integration.
Keywords
- Health Care
Status: accepted
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