Non-attendance Prediction Models to Pediatric Outpatient Appointments

Study Overview
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Brief Summary
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Non-attendance to pediatric outpatient appointments is a frequent and relevant public health problem. Using different approaches it is possible to build non-attendance predictive models and these models can be used to guide strategies aimed at reducing no-shows. However, predictive models have limitations and it is unclear which is the best method to generate them. Regardless of the strategy used to build the predictive model, discrimination, measured as area under the curve, has a ceiling around 0.80. This implies that the models do not have a 100% discrimination capacity for no-show and therefore, in a proportion of cases they will be wrong. This classification error limits all models diagnostic performance and therefore, their application in real life situations. Despite all this, the limitations of predictive models are little explored. Taking into account the negative effects of non-attendance, the possibility of generating predictive models and using them to guide strategies to reduce non-attendance, we propose to generate non-attendance predictive models for outpatient appointments using traditional logistic regression and machine learning techniques, evaluate their diagnostic performance and finally, identify and characterize the population misclassified by predictive models.

Official Title
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Non-attendance to Pediatric Outpatient Appointments: Prevalence, Associated Factors and Prediction Models

Conditions
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Non-Attendance, Patient, No-Show Patients

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: pediatric outpatient appointments Exclusion Criteria: appointments generated for system benchmarking or appointments with missing data

Ages Eligible for Study
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Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Attended appointments<br>An appointment scheduled by a patient that was attended | Other: No intervention<br>* There is no intervention, observational study<br>|
| Not-attended appointments<br>An appointment scheduled by a patient that was not-attended, regardless of the cause | Other: No intervention<br>* There is no intervention, observational study<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Predictive Model non-attendance discrimination | Area Under the ROC Curve | 12 months | 
| Predictive Model non-attendance calibration | Calibration chart with predicted vs observed probability. | 12 months | 
| Predictive Model non-attendance diagnostic performance |  | 12 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Characterize the appointments misclassified by predictive models (FP) | False positive appointments prevalence | 12 months | 
| Characterize the appointments misclassified by predictive models (FN) | False negative appointments prevalence | 12 months |