Non-attendance Prediction Models to Pediatric Outpatient Appointments
NCT ID: NCT06077630
Last Updated: 2023-11-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
300000 participants
OBSERVATIONAL
2017-01-01
2018-12-31
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Attended appointments
An appointment scheduled by a patient that was attended
No intervention
There is no intervention, observational study
Not-attended appointments
An appointment scheduled by a patient that was not-attended, regardless of the cause
No intervention
There is no intervention, observational study
Interventions
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No intervention
There is no intervention, observational study
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Hospital General de Niños Pedro de Elizalde
OTHER
Responsible Party
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Mariano Esteban Ibarra
Staff Pediatrician
Principal Investigators
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Mariano Ibarra, MD, Mag
Role: PRINCIPAL_INVESTIGATOR
Hospital General de Niños Pedro de Elizalde
Diego H Giunta, MD, MPH, PhD
Role: PRINCIPAL_INVESTIGATOR
Hospital Italiano de Buenos Aires
Arda Yilal, Engineer
Role: PRINCIPAL_INVESTIGATOR
Karolinska Institutet
Leticia Peroni, MD, Mag
Role: PRINCIPAL_INVESTIGATOR
Hospital Italiano de Buenos Aires
Lucia Perez, MD
Role: PRINCIPAL_INVESTIGATOR
Hospital Italiano de Buenos Aires
Other Identifiers
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4084
Identifier Type: -
Identifier Source: org_study_id
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