Machine Learning Healthcare Scheduling System for the Chilean Primary Public Health Network
DOI:
https://doi.org/10.53591/easi.v1i2.1849Keywords:
Machine Learning, Planificación de citas, Sistema primario de saludAbstract
There are factors that measure the performance of health services, like efficient healthcare access. Managing access to these services to reduce waiting times for patients and users has been a relevant issue at the level of public policies. In Chile, one of the biggest challenges is to provide a system for assigning medical appointments, especially in the public network of Primary Health Care (PHC). Some of the initiatives are administrative procedures, but few of them are in the realm of digital transformation. This research aims to study different machine learning algorithms, including K-nearest neighbors, random forests, decision trees, and support vector machines. The goal is to classify medical appointments according to user preferences and resource constraints, based on data obtained from previous experiences. The potential application of these algorithms to manage an appointment assignment system is evaluated. The results are still conservative and highlight the need to optimize the parameters associated with these algorithms to ensure an efficient allocation of citations to system users.
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