Evaluating Patient Navigation to Improve First Appointment No-show Rates in Uninsured Patients with Diabetes

Weaver, Kendra; Talley, Michele; Mullins, M. Raynor; Selleck, Cynthia S.

doi:10.1080/07370016.2018.1555315

Cited by 12 publications

(14 citation statements)

References 11 publications

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“…Thirty‐four studies of varying designs were identified (15 observational, 1 randomized control trial, 9 qualitative, 5 surveys, 4 service improvements). Sixteen were studies from the UK , four from Europe , six from North America and eight from the rest of the world . Six studies specifically focused on young adults in transition from paediatric to adult services, each of which had a mean participant age of >18 years (age range 15–30 years) .…”

Section: Resultsmentioning

confidence: 99%

“…The means of quantifying non‐attendance varied; some studies calculated the number of missed appointments as a percentage of total booked appointments . Non‐attendance was also determined as the number of people missing more than one appointment in a defined period or when there was no record of HbA 1c measurement in primary or secondary care in the previous 12–15 months . Re‐referred ‘lapsers’ made up 19% of the ‘new patient’ clinic load at one UK diabetes service and appointment cancellations were only defined as a separate entity in one study where they occurred more frequently than non‐attendance (18 vs. 12%) .…”

Section: Resultsmentioning

confidence: 99%

“…Patient navigators provide personal guidance to patients, helping them negotiate their way through healthcare systems. Two of the four studies reporting patient navigators were from North America, and focused on adults with diabetes . The other two, from Australia and Israel , were specific to people with type 1 diabetes transitioning to adult care.…”

Section: Resultsmentioning

confidence: 99%

“…Seven of the eight studies examining interventions to improve attendance were observational and one was a randomized controlled trial . The types of interventions varied and included patient navigators , web‐based consultations , and strategies to improve appointment management, service structure and patient information .…”

Section: Resultsmentioning

confidence: 99%

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Non‐attendance at diabetes outpatient appointments: a systematic review

et al. 2020

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Background Non-attendance at diabetes outpatient appointments is a sizeable problem worldwide and has been associated with suboptimal health outcomes. We aimed to describe the characteristics, health outcomes and reasons given for non-attendance at doctor-or nurse-led diabetes appointments, and interventions to improve attendance. Methods PubMed, EMBASE, CINAHL and PsychInfo were searched from database inception to February 2019. Included articles were peer-reviewed, published in English, related to adults or young people with type 1 or type 2 diabetes, and addressed one of the above aspects of non-attendance. Studies were excluded if reporting on other types of diabetes or reviewing attendance at structured education, retinal screening, paediatric, antenatal, podiatry or dietetic clinics. Results Thirty-four studies of varied designs were identified (15 observational, 1 randomized control trial, 9 qualitative, 5 surveys, 4 service improvements). The definition of non-attendance varied. Younger adults, smokers and those with financial pressures were less likely to attend. Non-attendance was associated with higher HbA 1c ; other outcomes were varied but typically worse in non-attenders. Reasons for non-attendance in qualitative studies fell into three categories: balancing the costs and benefits of attendance, coping strategies, and the relationships between the person with diabetes and healthcare professionals. Interventions included appointment management strategies, service improvements, patient navigators and WebCam appointments. Conclusions Non-attendance is only partially explained by logistical issues. Qualitative studies suggest complex psychosocial factors are involved. Interventions have progressed from simple appointment reminders in an attempt to address some of the psycho-social determinants, but more work is needed to improve attendance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Non‐attendance at diabetes outpatient appointments: a systematic review

et al. 2020

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“…On the other hand, high no-show rates reduce efficiency for service providers. When a patient fails to keep an appointment, it usually results in a vacant slot that might have been used by another patient [4], increases cost of care [5] and generates idle time for both physicians and consultancy rooms [6]. Consequently, there is a growing interest from the healthcare community in understanding the determinants of no-show behaviour [7] and minimizing its impact [8].…”

Section: Introductionmentioning

confidence: 99%

Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

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