ArticleJournal of the American Medical Informatics Association : JAMIA2018
Designing risk prediction models for ambulatory no-shows across different specialties and clinics.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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
26 citing papers in PubMed, 59 citations in OpenAlex.
- Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative.Journal of general internal medicine · 2023Trial
- Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study.Ophthalmology science · 2026Article
- Public perspectives on the use of different data types for prediction in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Evaluating the Relationship between Neighborhood-Level Social Vulnerability and Patient Adherence to Ophthalmology Appointments.Ophthalmic epidemiology · 2024Article
- Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.BMC health services research · 2024Article
- How patients distinguish between clinical and administrative predictive models in health care.The American journal of managed care · 2024Article
- Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system.BMC health services research · 2023Article
- Predicting and explaining absenteeism risk in hospital patients before and during COVID-19.Socio-economic planning sciences · 2023Article
- Social determinants of health and the prediction of missed breast imaging appointments.BMC health services research · 2022Article
- No-show after extracorporeal shock wave lithotripsy treatment in endourology clinic: Can we build a typical patient profile?International journal of urology : official journal of the Japanese Urological Association · 2022Article
- Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.BMC medical informatics and decision making · 2022Article
- Offering transportation services to economically disadvantaged patients at a family health center: a case study.Health systems (Basingstoke, England) · 2022Article
- Predicting no-shows for dental appointments.PeerJ. Computer science · 2022Article
- A novel model to label delirium in an intensive care unit from clinician actions.BMC medical informatics and decision making · 2021Article
- Efficient Prediction of Missed Clinical Appointment Using Machine Learning.Computational and mathematical methods in medicine · 2021Article
- Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions.PloS one · 2021Article
- Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations.JAMA network open · 2020Article
- Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.BMC medical informatics and decision making · 2020Article
- Patient No-Show Prediction: A Systematic Literature Review.Entropy (Basel, Switzerland) · 2020Review
- Prediction of appointment no-shows using electronic health records.Journal of applied statistics · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Objective: As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods: Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results: Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion: Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.
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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.