ReviewEntropy (Basel, Switzerland)2020
Patient No-Show Prediction: A Systematic Literature Review.
Review in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Behavioural economic interventions to reduce health care appointment non-attendance: a systematic review and meta-analysis.BMC health services research · 2023Pooled it
- Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- 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
- Factors Associated with Appointment Cancellation on a Commercial Digital Mental Health Platform: A Retrospective Cohort Study in Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.Arthroplasty today · 2026Article
- Absent or not invited? A multilevel qualitative study of patients and healthcare professionals' perspectives on barriers and enablers of outpatient attendance in Southern Denmark.BMC health services research · 2026Article
- What are we missing? Reflections on the 'problem' of missed appointments in the UK.NIHR open research · 2026Article
- Stakeholder perspectives on machine learning models predicting diabetic foot ulcers and amputations in diabetes care: a scenario-based interview study.Frontiers in digital health · 2026Article
- Article
- Identifying Patterns in Dental Visit Attendance Among Pregnant Women: A Retrospective Cohort Study.AJPM focus · 2025Article
- Factors Associated With Patient Failure To Attend Dental Appointments: A Retrospective Analysis.Cureus · 2024Article
- Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.Health science reports · 2024Article
- Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms.Healthcare (Basel, Switzerland) · 2024Article
- Understanding the causes of missingness in primary care: a realist review.BMC medicine · 2024Review
- The impact of the COVID-19 pandemic on rates and predictors of missed hospital appointments in multiple outpatient clinics of The Royal Hospital, Sultanate of Oman: a retrospective study.BMC health services research · 2023Article
- Distance Matters: Investigating No-Shows in a Large Rural Provider Network.Clinical medicine & research · 2023Article
- Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital.The Malaysian journal of medical sciences : MJMS · 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
- To charge or not to charge: reducing patient no-show.Israel journal of health policy research · 2023Article
- Predicting no-show appointments in a pediatric hospital in Chile using machine learning.Health care management science · 2023Article
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
Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients' waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.
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.