ArticleJournal of medical systems2019
Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- How to adjust the expected waiting time to improve patient's satisfaction?BMC health services research · 2023Trial
- AI-powered models for overcrowding prediction at TUMS hospitals.Scientific reports · 2025Article
- Data in ambulatory care logistics: What modelers need and what practice can offer.Health care management science · 2025Article
- Predicting hospital outpatient volume using XGBoost: a machine learning approach.Scientific reports · 2025Article
- Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.Scientific reports · 2024Article
- Demand prediction of medical services in home and community-based services for older adults in China using machine learning.Frontiers in public health · 2023Article
- Forecasting of Daily Outpatient Visits Based on Genetic Programming.Iranian journal of public health · 2022Article
- Predicting demand for long-term care using Japanese healthcare insurance claims data.Environmental health and preventive medicine · 2022Article
- Prioritization criteria of patients on scheduled waiting lists for abdominal wall hernia surgery: a cross-sectional study.Hernia : the journal of hernias and abdominal wall surgery · 2021Article
- Artificial intelligence-assisted reduction in patients' waiting time for outpatient process: a retrospective cohort study.BMC health services research · 2021Article
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
- Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model.Computational and mathematical methods in medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best method to predict demand for outpatient appointments comparing machine learning and traditional methods, this retrospective study analyzed "appointment requests" at a major outpatient department in a destination medical center. Two separate locations (A and B) were assessed with 20 traditional, hybrid (traditional + machine learning) and machine learning methods to determine the best forecasting outcome (lowest Forecast Standard Error, FSE). Data characteristics from both datasets were examined. 20 forecasting models were then assessed and compared for the best result. Location A's data displayed a cyclical and non-trending pattern while Location B's displayed a cyclical and trending pattern. Both Location A and B yielded the feature engineered XGBoost model (machine learning) with the lowest out-of-sample FSE. It is important to carefully analyze and understand the underlying data set pattern and then test a variety of traditional, machine learning, and hybrid prediction methods to achieve optimal predictive results. Additionally, the use of feature engineering or hybrid methods can augment the usefulness of machine learning methods.
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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.