Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Simple item page
dc.catalogadorjca
dc.contributor.authorDunstan Escudero, Jocelyn
dc.contributor.authorVillena, F.
dc.contributor.authorRiquelme, V.
dc.contributor.authorRamírez, H.
dc.contributor.authorHoyos, J.P.
dc.contributor.authorRoyer, M.
dc.contributor.authorPeypouquet, J.
dc.date.accessioned2024-07-03T14:15:26Z
dc.date.available2024-07-03T14:15:26Z
dc.date.issued2023
dc.description.abstractThe Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients’ historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.
dc.description.funderIMFD
dc.description.funderFondecyt
dc.description.funderFondef
dc.fechaingreso.objetodigital2024-07-03
dc.fuente.origenScopus
dc.identifier.doi10.1007/s10729-022-09626-z
dc.identifier.eissn1572-9389
dc.identifier.issn1386-9620
dc.identifier.scopusidSCOPUS_ID:85146958799
dc.identifier.urihttps://doi.org/10.1007/s10729-022-09626-z
dc.identifier.urihttps://www.springer.com/journal/10729
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86961
dc.information.autorucEscuela de Ingeniería; Dunstan Escudero, Jocelyn; S/I; 1285723
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaHealth Care Management Science
dc.rightsacceso abierto
dc.subjectAppointments and schedules
dc.subjectMachine learning
dc.subjectMedical informatics
dc.subjectNo-show patients
dc.subjectPublic health
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titlePredicting no-show appointments in a pediatric hospital in Chile using machine learning
dc.typeartículo
sipa.codpersvinculados1285723
sipa.trazabilidadSCOPUS;02-03-2023
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
s10729-022-09626-z.pdf
Size:
2.65 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas