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2908. Optimizing General Practitioner Appointments with Machine Learning: A Data-Driven Approach
Invited abstract in session MB-15: Machine learning and analytics in healthcare, stream OR in Health Services (ORAHS).
Monday, 10:30-12:00Room: 18 (building: 116)
Authors (first author is the speaker)
| 1. | Eric Leung
|
Abstract
Global healthcare faces a significant challenge with missed appointments, leading to financial losses and decreased service efficiency. According to the UK’s National Health Service (NHS) reports over 300 million missed appointments annually, costing over £216 million. This paper introduces an innovative machine learning-based system for optimizing General Practitioner (GP) appointments, enhancing scheduling by analyzing patterns of cancellations and no-shows. Utilizing both real and hypothetical data, we propose a highly applicable system which employs fuzzy logic algorithm to dynamically adjust GP schedules, prioritizing urgent cases and allowing walk-ins. This approach significantly benefits healthcare providers by maximizing resource utilization and reducing idle time. From a patient’s perspective, it ensures better access to care and shorter wait times for urgent needs. Our findings demonstrate the system’s potential in offering practical recommendations for managing walk-ins and unforeseen consultations more efficiently, paving the way for improved patient satisfaction and operational effectiveness in healthcare settings worldwide.
Keywords
- Health Care
- Machine Learning
- OR/MS and the Public Sector
Status: accepted
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