NCT06077630

Non-attendance Prediction Models to Pediatric Outpatient Appointments

Completed Last updated 8 November 2023

What this trial tests

trial testing No intervention in Non-Attendance, Patient in 300,000 participants. Completed in 31 December 2018.

Timeline

1 January 2017

Primary endpoint
31 December 2018

31 December 2018

Quick facts

Lead sponsor	Hospital General de Niños Pedro de Elizalde
Status	Completed
Study type	OBSERVATIONAL
Enrollment	300,000
Start date	1 January 2017
Primary completion	31 December 2018
Estimated completion	31 December 2018

Drugs / interventions tested

• No intervention

Conditions studied

• Non-Attendance, Patient — all drugs for Non-Attendance, Patient →
• No-Show Patients — all drugs for No-Show Patients →

Sponsor

Hospital General de Niños Pedro de Elizalde

Who can join

Under 18, any sex, with Non-Attendance, Patient or No-Show Patients. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.