Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Oikonomidi, Theodora, Norman, Gill, McGarrigle, Laura, Stokes, Jonathan, van der Veer, Sabine N, Dowding, Dawn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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
_version_ 1784889595833352192
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
work_keys_str_mv AT oikonomiditheodora predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview
AT normangill predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview
AT mcgarriglelaura predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview
AT stokesjonathan predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview
AT vanderveersabinen predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview
AT dowdingdawn predictivemodelbasedinterventionstoreduceoutpatientnoshowsarapidsystematicreview