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

Oikonomidi, T., Norman, G., McGarrigle, L., Stokes, J. , van der Veer, S. N. and Dowding, D. (2023) Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review. Journal of the American Medical Informatics Association, 30(3), pp. 559-569. (doi: 10.1093/jamia/ocac242) (PMID:36508503) (PMCID:PMC9933067)

Abstract

Objective: Outpatient 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 Methods: Rapid 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. Results: We 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 Conclusions: Predictive 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.

• Item Type: Articles
• Additional Information: This research was funded by the National Institute for Health and Care Research Applied Research Collaboration Greater Manchester (NIHR200174).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stokes, Dr Jonathan
• Authors: Oikonomidi, T., Norman, G., McGarrigle, L., Stokes, J., van der Veer, S. N., and Dowding, D.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU
• Journal Name: Journal of the American Medical Informatics Association
• Publisher: Oxford University Press
• ISSN: 1067-5027
• ISSN (Online): 1527-974X
• Published Online: 12 December 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Journal of the American Medical Informatics Association 30(3): 559-569
• Publisher Policy: Reproduced under a Creative Commons License