Causes, impacts and possible mitigation of non-attendance of appointments within the National Health Service: a literature review

Wilson, Rebecca; Winnard, Yvette

doi:10.1108/jhom-11-2021-0425

Cited by 23 publications

(27 citation statements)

References 51 publications

(363 reference statements)

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“…Missed appointments result in delays in care, inefficient resource use, and worse health outcomes (1)(2)(3), particularly in patients with poorer health and more complex social needs (4)(5)(6). The association between missed appointments and health inequalities is well established (2,7).…”

Section: Introductionmentioning

confidence: 99%

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

Kerr,

Greenfield,

Hayhoe

et al. 2023

Preprint

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IntroductionWith the growing use of remote appointments within the National Health Service, there is a need to understand potential barriers of access to care for some patients. In this observational study we examined missed appointments rates, comparing remote and face-to-face appointments among different patient groups.MethodsWe analysed adult outpatient appointments at Imperial College Healthcare NHS Trust in Northwest London in 2021. Rates of missed appointments per patient were compared between remote vs. face-to-face appointments using negative binomial regression models. Models were stratified by appointment type (first or a follow-up).ResultsThere were 874,659 outpatient appointments for 189,882 patients, 29.5% of whom missed at least one appointment. Missed rates were 12.5% for remote first appointments and 9.2% for face-to-face first appointment. Remote and face-to-face follow-up appointments were missed at similar rates (10.4% and 10.7%, respectively). For remote and face-to-face appointments, younger patients, residents of more deprived areas, and patients of Black, Mixed, and ‘other’ ethnicities missed more appointments. Male patients missed more face-to-face appointments, particularly at younger ages, but gender differences were minimal for remote appointments. Patients with long-term conditions (LTCs) missed more first appointments, whether face-to-face or remote. In follow-up appointments, patients with LTCs missed more face-to-face appointments but fewer remote appointments.DiscussionRemote face-to-face appointments were missed more often than face-to-face first appointments, follow-ups appointments had similar attendance rates for both modalities. Sociodemographic differences in outpatient appointment attendance were largely similar between face-to-face and remote appointments, indicating no widening of inequalities in attendance due to appointment modality.

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Section: Introductionmentioning

confidence: 99%

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

Kerr,

Greenfield,

Hayhoe

et al. 2023

Preprint

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“…1 In 2019/2020, there were 5 656 365 outpatient no-shows across the National Health Service (NHS) in the United Kingdom, 2 with an estimated annual cost as high as £750 million. 3 Outpatient no-shows are associated with worse health outcomes 4 and are an independent predictor of increased subsequent acute care utilization. 5,6 Outpatient no-shows are a complex, multifactorial phenomenon, associated with patient characteristics, including demographics relevant to equity (eg, receipt of welfare payments, travel time to appointment) and characteristics of the appointment and the healthcare system (eg, lead time).…”

Section: Introductionmentioning

confidence: 99%

“…5,6 Outpatient no-shows are a complex, multifactorial phenomenon, associated with patient characteristics, including demographics relevant to equity (eg, receipt of welfare payments, travel time to appointment) and characteristics of the appointment and the healthcare system (eg, lead time). 1,4,7,8 Organizational interventions, such as overbooking, and interventions facilitating access to appointments, such as reminders, [9][10][11][12][13] transportation to appointments, and financial incentives, 14 have been developed to increase attendance, reduce noshows, or increase timely cancelations and reschedules. These goals are distinct, and preference for one over the other may depend on the clinical context.…”

Section: Introductionmentioning

confidence: 99%

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

Oikonomidi

Norman

McGarrigle

et al. 2022

Journal of the American Medical Informatics Association

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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.

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“…Research has indicated major predictors of first appointment non-attendance can include clients with a lower non-risk score on the CORE-34 (Evans et al , 2000), regular thoughts of “being better off dead” (as indicated on the CORE-34; Evans et al ) or recent onset of a mental health difficulty (one month or less) or long-term condition (more than two years; Di Bona et al , 2014). Clients from lower socioeconomic backgrounds and those with multiple co-morbidities are also reportedly more likely to miss appointments, often citing forgetfulness, transportation difficulties and family commitments (Wilson and Winnard, 2022). Conversely, clients found to most likely attend their first appointment included those who self-referred and those with fewer previous referrals (Davis et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

A service evaluation of the assessment process in a Step4 Psychological Therapies Service

Robinson

Boos

Moghaddam

2022

MHRJ

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Purpose This study aims to improve the efficiency of the assessment process within a Step4 Psychological Therapies Service by identifying factors related to assessment non-attendance and service suitability for referred clients. Design/methodology/approach Referral and assessment information was accessed between October 2019 and March 2020 from Step4 routine service data, electronic client records where necessary and Step4 staff self-report questionnaires. Findings All clients offered an assessment during this time attended. Findings indicated several factors could influence service suitability in meeting client need. These included individual differences such as readiness to change, which was not necessarily identified at referral or prior to assessment, and potential systemic factors, such as the opt-in procedure, which possibly impeded access. Though the necessity for assessment in clarifying client needs and treatment was indicated, an assessment (from referral to assessment appointment) that led to discharge could take an excess of one working day of service time, associated with considerable opportunity cost to other clients awaiting assessment. Recommendations are made for improving assessment efficiency. Originality/value With a high prevalence of poor mental health in the UK, efforts must be made to identify and reduce additional demand upon service time and resources within mental health services to effectively meet people’s needs. Recommendations to improve assessment process efficiency include the use of a standardised referral form, offer of follow-up support procedures, increased client involvement, a streamlined opt-in battery and ongoing monitoring to ensure shared practice between clinicians. These are transferable to other mental health services, with implications for subsequent quality and timeliness of care.

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Causes, impacts and possible mitigation of non-attendance of appointments within the National Health Service: a literature review

Cited by 23 publications

References 51 publications

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

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

A service evaluation of the assessment process in a Step4 Psychological Therapies Service

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