Evidence map›Paper›PMID 36508503›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2023

Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.

Theodora Oikonomidi, Gill Norman, Laura McGarrigle, Jonathan Stokes, Sabine N van der Veer, Dawn Dowding

Open access · hybridAbstract readSystematic Review
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
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  5. 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 · 2026
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  6. Article
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  13. Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024
    Article
  14. Article
  15. Article
  16. Innovative informatics interventions to improve health and health care.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  17. Article
  18. 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 Quality
    Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Theodora OikonomidiCentre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.ORCID 0000-0003-2094-6015
Gill NormanNational Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK.
Laura McGarrigleNational Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK.
Jonathan StokesCentre for Primary Care & Health Services Research, The University of Manchester, Manchester, UK.
Sabine N van der VeerCentre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.ORCID 0000-0003-0929-436X
Dawn DowdingNational Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK.ORCID 0000-0001-5672-8605
Manchester Academic Health Science Centre · GBUniversity of Manchester · GBManchester University NHS Foundation Trust · GB

Funding

Department of Health NIHR200174
6 · The paper itself

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.

Indexed as

OutpatientsText MessagingHumansappointment attendancedecision support systemmissed appointmentno-showpredictive model

Identifiers

PMID36508503
PMCPMC9933067
OpenAlexW4311285501

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.