Non-attendance Prediction Models to Pediatric Outpatient Appointments

NCT06077630 | Completed

• 1 other identifier: 4084
• Study Type: observational
• Target: 300,000
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Non-Attendance, Patient; No-Show Patients

Outcome Measures

Primary Outcomes (3)

• 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 Outcomes (2)

• Characterize the appointments misclassified by predictive models (FP)

  12 months

• Characterize the appointments misclassified by predictive models (FN)

  12 months

Study Arms (2)

Attended appointments

An appointment scheduled by a patient that was attended

Other: No intervention

Not-attended appointments

An appointment scheduled by a patient that was not-attended, regardless of the cause

Other: No intervention

Interventions

No intervention OTHER

There is no intervention, observational study

Attended appointments; Not-attended appointments

Eligibility Criteria

• Age: Up to 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

All scheduled pediatric outpatient appointments between January 1 2017 and 31 december 2018 will be included. The sample will be randomized to allocate a generation cohort (two-thirds of the sample) and a validation cohort (one third of the sample).

You may qualify if:

• pediatric outpatient appointments

You may not qualify if:

• appointments generated for system benchmarking or appointments with missing data

Sponsors & Collaborators

• Hospital General de Niños Pedro de Elizalde: lead

MeSH Terms

Conditions

No-Show Patients

Condition Hierarchy (Ancestors)

Patient Compliance; Patient Acceptance of Health Care; Treatment Adherence and Compliance; Health Behavior; Behavior

Study Officials

• Mariano Ibarra, MD, Mag

  Hospital General de Niños Pedro de Elizalde

  PRINCIPAL INVESTIGATOR

• Diego H Giunta, MD, MPH, PhD

  Hospital Italiano de Buenos Aires

  PRINCIPAL INVESTIGATOR

• Arda Yilal, Engineer

  Karolinska Institutet

  PRINCIPAL INVESTIGATOR

• Leticia Peroni, MD, Mag

  Hospital Italiano de Buenos Aires

  PRINCIPAL INVESTIGATOR

• Lucia Perez, MD

  Hospital Italiano de Buenos Aires

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Staff Pediatrician

Study Record Dates

• First Submitted: October 5, 2023
• First Posted: October 11, 2023
• Study Start: January 1, 2017
• Primary Completion: December 31, 2018
• Study Completion: December 31, 2018
• Last Updated: November 8, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share