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Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review
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 cos...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933067/ https://www.ncbi.nlm.nih.gov/pubmed/36508503 http://dx.doi.org/10.1093/jamia/ocac242 |
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author | Oikonomidi, Theodora Norman, Gill McGarrigle, Laura Stokes, Jonathan van der Veer, Sabine N Dowding, Dawn |
author_facet | Oikonomidi, Theodora Norman, Gill McGarrigle, Laura Stokes, Jonathan van der Veer, Sabine N Dowding, Dawn |
author_sort | Oikonomidi, Theodora |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9933067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-99330672023-02-17 Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review Oikonomidi, Theodora Norman, Gill McGarrigle, Laura Stokes, Jonathan van der Veer, Sabine N Dowding, Dawn J Am Med Inform Assoc Review 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. Oxford University Press 2022-12-12 /pmc/articles/PMC9933067/ /pubmed/36508503 http://dx.doi.org/10.1093/jamia/ocac242 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Review Oikonomidi, Theodora Norman, Gill McGarrigle, Laura Stokes, Jonathan van der Veer, Sabine N Dowding, Dawn Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title | Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title_full | Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title_fullStr | Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title_full_unstemmed | Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title_short | Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
title_sort | predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933067/ https://www.ncbi.nlm.nih.gov/pubmed/36508503 http://dx.doi.org/10.1093/jamia/ocac242 |
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