Non-attendance Prediction Models to Pediatric Outpatient Appointments

No-Show Patients Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06077630
• Other study ID #: 4084
• Status: Completed
• Start date: January 1, 2017
• Est. completion date: December 31, 2018

Study information

• Verified date: November 2023
• Source: Hospital General de Niños Pedro de Elizalde
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 300000
• Est. completion date: December 31, 2018
• Est. primary completion date: December 31, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 18 Years

Eligibility

Inclusion Criteria:

• pediatric outpatient appointments

Exclusion Criteria:

• appointments generated for system benchmarking or appointments with missing data

Related Conditions & MeSH terms

• No-Show Patients

Intervention

Other:
• No intervention
There is no intervention, observational study

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Hospital General de Niños Pedro de Elizalde

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Predictive Model non-attendance discrimination	Area Under the ROC Curve	12 months
Primary	Predictive Model non-attendance calibration	Calibration chart with predicted vs observed probability.	12 months
Primary	Predictive Model non-attendance diagnostic performance		12 months
Secondary	Characterize the appointments misclassified by predictive models (FP)	False positive appointments prevalence	12 months
Secondary	Characterize the appointments misclassified by predictive models (FN)	False negative appointments prevalence	12 months