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Optimizing Resource Allocation for Outpatients : Machine Learning-Based Length-of-Stay Predictions and Patient Scheduling

Boersen, N. (2023) Optimizing Resource Allocation for Outpatients : Machine Learning-Based Length-of-Stay Predictions and Patient Scheduling.

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Abstract:Efficient allocation of hospital resources and patient scheduling are critical to optimising healthcare delivery. This article presents a comprehensive methodology to improve bed occupancy and streamline patient scheduling processes. The approaches take advantage of length-of-stay predictions and integer linear programming techniques to optimise resource allocation and enhance the workflow of hospital planners. The study includes a combination of regression and classification machine learning models and ILP for patient scheduling. Validation and correction methods are developed and implemented to minimise the risk of underestimation resulting in logistical problems. The results demonstrate the effectiveness of the models, with regression achieving an R2 score of 0.776 and classification achieving an accuracy of 77.20%. Additionally, a graphical user interface was developed to be used by planning personnel. The discussion highlights the strengths and limitations of the methodology and proposes future research directions. This paper offers valuable information on increasing the use of scarce resources and implementation in the real world.
Item Type:Essay (Master)
Clients:
Red Cross Hospital, Beverwijk, The Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:58 process technology
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:https://purl.utwente.nl/essays/95151
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