SynthesisJournal of the American Medical Informatics Association : JAMIA2023
Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 27 citations in OpenAlex.
- Patient and carer perceptions and experiences of technology-facilitated appointment reminders in healthcare: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Article
- Article
- Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.Arthroplasty today · 2026Article
- Prevalence and Drivers of Missed Healthcare Appointments in Poland: Insights From a 2025 Survey.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Preoperative waiting time and no-show risk in day surgery: a large-scale cohort study.Frontiers in medicine · 2026Article
- Prediction without intervention: why fifteen years of no-show models have not closed the loop on patient adherence.Frontiers in digital health · 2026Article
- Article
- Understanding No-Show Patterns in Healthcare: A Retrospective Study from Northern Italy.Healthcare (Basel, Switzerland) · 2025Article
- External Validation of an EHR-Based Model for Risk of Patient No-Show in Primary Care.JAMA network open · 2025Article
- Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.Journal of medical systems · 2025Article
- Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study.JAMIA open · 2024Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- To confirm your appointment, please press one: Examining demographic and health system interface factors that predict missed appointments in a pediatric outpatient neuropsychology clinic.The Clinical neuropsychologist · 2024Article
- Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors.ATS scholar · 2023Article
- Innovative informatics interventions to improve health and health care.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Factors associated with nonattendance at annual diabetes check-up in Ningbo, China: a case-control study.Frontiers in public health · 2023Article
- Reframing "No-Show": Patient-Reported Barriers for Missed Post-Hospital Discharge Primary Care Follow-Up Visits.American journal of medical quality : the official journal of the American College of Medical QualityArticle
- Vision 2030 healthcare efficiency: Radiology no-shows at King Fahd Hospital of the University - Financial and operational burden.Journal of family & community medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveOutpatient no-shows have important implications for costs and the quality of care. Predictive models of no-shows could be used to target intervention delivery to reduce no-shows. We reviewed the effectiveness of predictive model-based interventions on outpatient no-shows, intervention costs, acceptability, and equity. MATERIALS AND
methodsRapid systematic review of randomized controlled trials (RCTs) and non-RCTs. We searched Medline, Cochrane CENTRAL, Embase, IEEE Xplore, and Clinical Trial Registries on March 30, 2022 (updated on July 8, 2022). Two reviewers extracted outcome data and assessed the risk of bias using ROB 2, ROBINS-I, and confidence in the evidence using GRADE. We calculated risk ratios (RRs) for the relationship between the intervention and no-show rates (primary outcome), compared with usual appointment scheduling. Meta-analysis was not possible due to heterogeneity.
resultsWe included 7 RCTs and 1 non-RCT, in dermatology (n = 2), outpatient primary care (n = 2), endoscopy, oncology, mental health, pneumology, and an magnetic resonance imaging clinic. There was high certainty evidence that predictive model-based text message reminders reduced no-shows (1 RCT, median RR 0.91, interquartile range [IQR] 0.90, 0.92). There was moderate certainty evidence that predictive model-based phone call reminders (3 RCTs, median RR 0.61, IQR 0.49, 0.68) and patient navigators reduced no-shows (1 RCT, RR 0.55, 95% confidence interval 0.46, 0.67). The effect of predictive model-based overbooking was uncertain. Limited information was reported on cost-effectiveness, acceptability, and equity. DISCUSSION AND
conclusionsPredictive modeling plus text message reminders, phone call reminders, and patient navigator calls are probably effective at reducing no-shows. Further research is needed on the comparative effectiveness of predictive model-based interventions addressed to patients at high risk of no-shows versus nontargeted interventions addressed to all patients.
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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.