Projects per year
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 randomised controlled trials (RCTs) and non-RCTs. We searched MEDLINE, Cochrane CENTRAL, EMBASE, IEEE Xplore, and clinical trial registries on 30 March 2022 (updated on 8 July 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 [RR] 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 MRI 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 Conclusion: Predictive modelling 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 non-targeted interventions addressed to all patients.
Materials and Methods: Rapid systematic review of randomised controlled trials (RCTs) and non-RCTs. We searched MEDLINE, Cochrane CENTRAL, EMBASE, IEEE Xplore, and clinical trial registries on 30 March 2022 (updated on 8 July 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 [RR] 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 MRI 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 Conclusion: Predictive modelling 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 non-targeted interventions addressed to all patients.
| Original language | English |
|---|---|
| Journal | Journal of the American Medical Informatics Association |
| Publication status | Accepted/In press - 25 Nov 2022 |
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NIHR Applied Research Collaboration Greater Manchester.
Cullum, N., Abel, K., Ainsworth, J., Arden Armitage, C., Bee, P., Bower, P., Bucci, S., Burden, S., Burns, A., Checkland, K., Dixon, W., Dowding, D., Dumville, J., French, D., Grande, G., Green, J., Griffiths, J., Hodgson, D., Keady, J., Kislov, R., Kontopantelis, E., Lovell, K., Meacock, R., Morciano, M., Munford, L., O'Neill, T., Peek, N., Pendleton, N., Sanders, C., Spooner, S., Stanmore, E., Sutton, M., Todd, C., Turner, S., Van Der Veer, S., Webb, R., Whittaker, W. & Wilson, P.
1/10/19 → 30/09/24
Project: Research
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DHSCRG: Digital Health and Social Care Research Group
Dowding, D., Hawley-Hague, H., O'Connor, S., Stanmore, E., Kirk, S., Hall, A., Burden, S., Deane, J., Eost-Telling, C., Gasteiger, N., Jeyasingham, D., Christie, J., Rogers, K., Dumville, J., Atkinson, R., Vercell, A. & Ford, C.
Project: Research