Ítem
Acceso Abierto

Detecting unexpected growths in health technologies expenditures

Título de la revista
Autores
Espinosa, Oscar
Bejarano, Valeria
Sanabria, Cristian
Rodríguez, Jhonathan
Basto, Sergio
Rodríguez Lesmes, Paul
Robayo, Adriana

Fecha
2023-12-01

Directores

ISSN de la revista
Título del volumen
Editor
Universidad del Rosario


Buscar en:

Métricas alternativas

Resumen
We developed an algorithm to explore unexpected growth in the usage and costs of health technologies. We exploit data from the expenditures on technologies funded by the Colombian government under the compulsory insurance system, where all prescriptions for technologies not included in an explicit list must be registered in a centralized information system, covering the period from 2017 to 2022. The algorithm consists of two steps: an outlier detection method based on the density of the expenditures for selecting a frst set of technologies to consider (39 technologies out of 106,957), and two anomaly detection models for time series to determine which insurance companies, health providers, and regions have the most notorious increases. We have found that most medicines associated with atypi?cal behavior and signifcant monetary growth could be linked to the use of recently introduced drugs in the market. These drugs have valid patents and very specifc clinical indications, often involving high-cost pharmacological treat?ments. The most relevant case is the Burosumab, approved in 2018 to treat a rare genetic disorder afecting skeletal growth. Secondly, there is clear evidence of anomalous increasing trend evolutions in the identifed enteral nutritional support supplements or Food for Special Medical Purposes. The health system did not purchase these products before July 2021, but in 2022 they represented more than 500,000 USD per month.
Abstract
Palabras clave
Health technologies , Health expenditures , High-cost technology , Enteral nutritional , Statistical data analysis , Data analytics
Keywords
Buscar en:
Enlaces relacionados
Set de datos
Colecciones