ArticleInternational journal of environmental research and public health2020
A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Novel Prediction-Optimization Machine Learning Framework for Nanofluid-Based Photovoltaic/Thermal Systems.Nanomaterials (Basel, Switzerland) · 2026Article
- The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department.Cureus · 2023Article
- Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents.Scientific reports · 2022Article
- Article
- Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images.Diagnostics (Basel, Switzerland) · 2022Article
- A pull-strategy for the appointment scheduling of surgical patients in a hospital-integrated facility.Health systems (Basingstoke, England) · 2022Article
- Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation.BMC veterinary research · 2021Article
- An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.Journal of healthcare engineering · 2021Review
- Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data.Journal of healthcare engineering · 2021Article
- Effective Analysis of Inpatient Satisfaction: The Random Forest Algorithm.Patient preference and adherence · 2021Article
- Article
Corrections and comments
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.