ArticleJournal of applied statistics2020
Prediction of appointment no-shows using electronic health records.
Article in Journal of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Patient Non-Attendance for MRI and MSCT Examinations in Croatia: An Observational Study Using Administrative and Patient-Reported Data.Nursing reports (Pavia, Italy) · 2026Article
- Article
- Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study.JMIR formative research · 2025Article
- Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study.JMIR medical informatics · 2024Article
- Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.BMC health services research · 2024Article
- Patients' Nonattendance in Outpatient Specialist Consultations: A National Cohort Analysis of a Health System.Risk management and healthcare policy · 2024Article
- Distance Matters: Investigating No-Shows in a Large Rural Provider Network.Clinical medicine & research · 2023Article
- 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 missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries.BMC health services research · 2023Article
- Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.Frontiers in public health · 2023Article
- Patient No-Show Prediction: A Systematic Literature Review.Entropy (Basel, Switzerland) · 2020Review
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
Appointment no-shows have a negative impact on patient health and have caused substantial loss in resources and revenue for health care systems. Intervention strategies to reduce no-show rates can be more effective if targeted to the subpopulations of patients with higher risk of not showing to their appointments. We use electronic health records (EHR) from a large medical center to predict no-show patients based on demographic and health care features. We apply sparse Bayesian modeling approaches based on Lasso and automatic relevance determination to predict and identify the most relevant risk factors of no-show patients at a provider level.
Indexed as
Identifiers
What OpenQuestion holds
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.