The power of digital communications: improving outpatient attendances in south London

Bartlett, Mark; Blazer, Simon; Hobson, Gina; Abbs, I C

doi:10.7861/futurehosp.5-1-43

Cited by 5 publications

(2 citation statements)

References 7 publications

(5 reference statements)

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“…The most common strategies used were short message service (SMS) text message reminders, phone call reminders, overbooking of the Open access appointments, charging a fine to patients who miss their appointment and the use of an online scheduling system to ease the process of appointment cancellation and rebooking. [18][19][20][21][22][23] There is a lack of information about the frequency and drivers of missed hospital appointments in non-Western countries such as Oman. The limited knowledge available tends to focus on overall rates of missed hospital appointments within one specialty.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

2021

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ObjectivesMissed hospital appointments pose a major challenge for healthcare systems. There is a lack of information about drivers of missed hospital appointments in non-Western countries and extent of variability between different types of clinics. The aim was to evaluate the rate and predictors of missed hospital appointments and variability in drivers between multiple outpatient clinics.SettingOutpatient clinics in the Royal hospital (tertiary referral hospital in Oman) between 2014 and 2018.ParticipantsAll patients with a scheduled outpatient clinic appointment (N=7 69 118).Study designRetrospective cross-sectional analysis.Primary and secondary outcome measuresA missed appointment was defined as a patient who did not show up for the scheduled hospital appointment without notifying or asking for the appointment to be cancelled or rescheduled. The outcomes were the rate and predictors of missed hospital appointments overall and variations by clinic. Conditional logistic regression compared patients who attended and those who missed their appointment.ResultsThe overall rate of missed hospital appointments was 22.3%, which varied between clinics (14.0% for Oncology and 30.3% for Urology). Important predictors were age, sex, service costs, patient’s residence distance from hospital, waiting time and appointment day and season. Substantive variability between clinics in ORs for a missed appointment was present for predictors such as service costs and waiting time. Patients aged 81–90 in the Diabetes and Endocrine clinic had an adjusted OR of 0.53 for missed appointments (95% CI 0.37 to 0.74) while those in Obstetrics and Gynaecology had OR of 1.70 (95% CI 1.11 to 2.59). Adjusted ORs for longer waiting times (>120 days) were 2.22 (95% CI 2.10 to 2.34) in Urology but 1.26 (95% CI 1.18 to 1.36) in Oncology.ConclusionPredictors of a missed appointment varied between clinics in their effects. Interventions to reduce the rate of missed appointments should consider these factors and be tailored to clinic.

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Section: Introductionmentioning

confidence: 99%

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

2021

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“…Guy's and St Thomas NHS Foundation Trust (GSTT) is a large multisite teaching hospital in central London with a comprehensive range of secondary and tertiary care specialties. The local population for the hospital includes areas with high levels of deprivation and the mean outpatient DNA rate is higher than NHS average (9.8% in 2017 [9]).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hospital Outpatient Attendance in UK Hospitals: A Retrospective Study Applying Machine Learning to Routinely Collected Data for Patients of All Ages

Holdship

Dhanoa

Hopper

et al. 2022

Preprint

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Objectives Patient non-attendance at outpatient appointments (DNA) is a major concern for healthcare providers. Non-attendances increase waiting lists, reduce access to care and may be detrimental not for the patient who did not attend. However, non-targeted interventions to reduce the DNA rate may not be effective and thus we aim to produce a model which can accurately predict which appointments will be attended. Methods In this work, a random forest classification algorithm was trained to predict whether an appointment will be missed using 7 million past outpatient appointments. The model was applied to patients of all ages and appointments with all specialties at a major London teaching hospital including a validation set covering the COVID-19 pandemic. Results The model achieves an AUROC score of 0.76 and accuracy of 73% on test data. We find that the waiting period between booking an the appointment, the patient's past DNA behaviour, and the levels of deprivation in their local area are important factors in predicting future DNAs. Discussion Our model is strongly predictive of whether a hospital outpatient appointment will be attended. Its performance on both patients who did not appear in the training data and appointments from a different time period which covers the Covid-19 pandemic indicate it generalized well across both face to face and virtual appointments and could be used to target resources and intervention towards those patients who are likely to miss an appointment. Moreover, it highlights the impact of deprivation on patient access to healthcare Conclusion Our model successfully predicts patient attendance at outpatient appointments.

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Digital health solutions for reducing the impact of non-attendance: A scoping review

Selim

Kularatna

Carter

et al. 2023

Health Policy and Technology

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The power of digital communications: improving outpatient attendances in south London

Cited by 5 publications

References 7 publications

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

Prediction of Hospital Outpatient Attendance in UK Hospitals: A Retrospective Study Applying Machine Learning to Routinely Collected Data for Patients of All Ages

Digital health solutions for reducing the impact of non-attendance: A scoping review

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