Non-attendance Prediction Models to Pediatric Outpatient Appointments
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Attended appointments An appointment scheduled by a patient that was attended |
Other: 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 |
Other: No intervention
There is no intervention, observational study
|
Outcome Measures
Primary Outcome Measures
- Predictive Model non-attendance diagnostic performance [12 months]
Generate non-attendance predictive models for outpatient appointments using traditional logistic regression and machine learning techniques and compare their diagnostic performance
Secondary Outcome Measures
- Characterize the appointments misclassified by predictive models [12 months]
Using unsupervised machine learning techniques, identify and characterize patterns or clusters in the appointment population misclassified by the generated models.
Eligibility Criteria
Criteria
Inclusion Criteria:
- pediatric outpatient appointments
Exclusion Criteria:
- appointments generated for system benchmarking or appointments with missing data
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Hospital General de Niños Pedro de Elizalde
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 4084