Factors Associated With Missed Appointments at an Academic Pain Treatment Center: A Prospective Year-Long Longitudinal Study

Odonkor, Charles A.; Christiansen, Sandy; Chen, Yi‐An; Sathiyakumar, Asmitha; Chaudhry, H. S.; Cinquegrana, Denise; Lange, Jessica; He, Cathy; Cohen, Steven P.

doi:10.1213/ane.0000000000001794

Cited by 27 publications

(24 citation statements)

References 27 publications

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“…Secondly, in G&D (between 0 and 13 years) and YAP (between 14 and 44 years), older patients are more likely to miss their appointments. This result is consistent with previously reported findings in primary care and paediatrics settings[20,73,76]. Finally, age seems to have less impact among SP patients.…”

supporting

confidence: 93%

“…On the one hand, this result is highly context-dependent. Whereas some studies have reached the same conclusion [71,72], others have reported that males have lower no-show rates [19,73,74], or concluded that gender does not have impact in no-show probabilities [75,76]. On the other hand, in developing countries it has been argued that, among socio-economically disadvantaged females, high no-show rates might be related to a lack of support from social networks and their responsibilities as caregivers [77][78][79].…”

Section: Lasso Regression Model: Variables Affecting No-show Probabilmentioning

confidence: 99%

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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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supporting

confidence: 93%

Section: Lasso Regression Model: Variables Affecting No-show Probabilmentioning

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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“…14,50,61 This helps to counter the problem of patient nonattendance, which is well known in most chronic conditions, including pain. 21,37,44 The group assessment has shown comparable clinical outcomes with individual assessment. 50…”

Section: Discussionmentioning

confidence: 98%

Prioritizing a sequence of short-duration groups as the standardized pathway for chronic noncancer pain at an Australian tertiary multidisciplinary pain service: preliminary outcomes

Rajappa

Wilson

White

et al. 2019

PR9

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“…While time of the day is often considered a factor explaining attendance rates, as arguably people with chronic pain may find it more difficult to attend centres in the morning, it has been found that this is not always the case. 30 Therefore, the time of the appointment may become a predictor of attendance because of different variables intrinsic to the specific centres analysed as well as a characteristic of patients with chronic pain. Practical issues, such as parking or public transport accessibility, may explain this.…”

Section: Discussionmentioning

confidence: 99%

Exploring predictors of first appointment attendance at a pain management service

Monastra

White

Simpson

2020

British Journal of Pain

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Background: Individual characteristics such as gender, employment and age have been shown to predict attendance at pain management services (PMS). The characteristics of those who drop out of pain management programmes have also been explored, but as yet no studies have analysed the characteristics of those who do not attend the service following referral. Purpose: To explore the characteristics and predictors of those who attend and those who do not attend their first appointment with a PMS. Method: Predictive factors in the two groups – attenders ( n = 425) and non-attenders ( n = 69) – were explored using logistic regression. Results: Non-attendance was significantly predicted by the patient being a smoker and the appointment being in the morning. Non-attenders also scored higher on the Modified Somatic Perception Questionnaire, indicating higher levels of somatic pain. Discussion: Predictors of non-attendance were different from those for individuals who drop out of pain services. Implications and recommendations are made for PMS.

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Factors Associated With Missed Appointments at an Academic Pain Treatment Center: A Prospective Year-Long Longitudinal Study

Cited by 27 publications

References 27 publications

Improving healthcare access management by predicting patient no-show behaviour

Improving healthcare access management by predicting patient no-show behaviour

Prioritizing a sequence of short-duration groups as the standardized pathway for chronic noncancer pain at an Australian tertiary multidisciplinary pain service: preliminary outcomes

Exploring predictors of first appointment attendance at a pain management service

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