Non-attendance Prediction Models to Pediatric Outpatient Appointments

Completed

• Conditions: Non-Attendance, Patient; No-Show Patients
• Interventions: Other: No intervention

• Registration Number: NCT06077630
• Lead Sponsor: Hospital General de Niños Pedro de Elizalde

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2017-01-01
• Primary Completion: 2018-12-31
• Study Completion: 2018-12-31
• Study First Submitted: 2023-10-05
• Study First Submitted QC: 2023-10-05
• Study First Posted: 2023-10-11
• Status Verified: 2023-11
• Last Update Submitted: 2023-11-06
• Last Update Posted: 2023-11-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 300000

Inclusion Criteria

• pediatric outpatient appointments

Exclusion Criteria

• appointments generated for system benchmarking or appointments with missing data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Attended appointments	No intervention	An appointment scheduled by a patient that was attended
Not-attended appointments	No intervention	An appointment scheduled by a patient that was not-attended, regardless of the cause

Primary Outcome Measures

Name	Time	Method
Predictive Model non-attendance calibration	12 months	Calibration chart with predicted vs observed probability.
Predictive Model non-attendance discrimination	12 months	Area Under the ROC Curve
Predictive Model non-attendance diagnostic performance	12 months

Secondary Outcome Measures

Name	Time	Method
Characterize the appointments misclassified by predictive models (FN)	12 months	False negative appointments prevalence
Characterize the appointments misclassified by predictive models (FP)	12 months	False positive appointments prevalence