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

Author:

Oikonomidi Theodora12ORCID,Norman Gill23,McGarrigle Laura24,Stokes Jonathan56,van der Veer Sabine N12ORCID,Dowding Dawn23ORCID

Affiliation:

1. Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester , Manchester, UK

2. National Institute for Health and Care Research Applied Research Collaboration Greater Manchester , Manchester, UK

3. Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester , Manchester, UK

4. Wythenshawe Hospital, Manchester University NHS Foundation Trust , Manchester, UK

5. Centre for Primary Care & Health Services Research, The University of Manchester , Manchester, UK

6. MRC/CSO Social & Public Health Sciences Unit, University of Glasgow , Glasgow, UK

Abstract

AbstractObjectiveOutpatient 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.

Funder

National Institute for Health and Care Research Applied Research Collaboration Greater Manchester

National Institute for Health and Care Research

Department of Health and Social Care

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference44 articles.

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3. Association between outpatient “no-shows” and subsequent acute care utilization;Hwang;J Gen Intern Med,2014

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