Authors:
Inês Ferreira
1
and
André Vasconcelos
2
Affiliations:
1
Instituto Superior Técnico, Lisbon and Portugal
;
2
Instituto Superior Técnico, Lisbon, Portugal, Instituto de Engenharia de Sistemas e Computadores, Investigaç ão e Desenvolvimento (INESC-ID), Lisbon and Portugal
Keyword(s):
No-show, MedClick, Health Care, Supervised Learning, Logistic Regression, Prediction.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Health Information Systems
;
Healthcare Management Systems
;
Pattern Recognition and Machine Learning
Abstract:
A no-show is one of the phenomena that leads to an efficiency decrease in various sectors, including in the health care sector. When a scheduled patient misses an appointment without cancelling, it will not only waste the clinic’s resources, but it will also deny medical service to another patient who could have benefited from the respective time slot. This paper describes the research that is being developed in the context of MedClick, an online platform that aims to help medical service providers increase the efficiency of their practices. The solution supports the reduction of no-shows by predicting their occurrence and finding replacements to fulfill “last-minute” vacancy slots. A supervised learning algorithm (logistic regression) is being implemented and it will be used to predict the probability of no-show for each patient. The system will run this algorithm 48 hours before each appointment so that there is still enough time to find a replacement, if necessary. The prediction
is based on features related to the respective clinic and patient, which requires access to the database.
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