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Coincidencia cliente-terapeuta con aprendizaje automático

Seth M Peacock1, Ian H Goodin1, Ryan K Wood1

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Resumen
Este resumen es generado por máquina.

Un nuevo marco utiliza datos de archivo y ciencias de la computación para emparejar clientes con terapeutas, mejorando los resultados de consejería. Este enfoque basado en datos mejora el éxito del cliente y al mismo tiempo respeta la disponibilidad del terapeuta.

Palabras clave:
aprendizaje automáticosalud mentalterapiacoincidencia cliente-terapeutaresultados del tratamiento

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Área de la Ciencia:

  • Psicología Clínica
  • Informática de la Salud
  • Ciencias de la Computación

Sus antecedentes:

  • La asignación del terapeuta impacta significativamente el éxito de la consejería.
  • Los métodos actuales a menudo se basan en la simple disponibilidad, lo que podría pasar por alto coincidencias óptimas.

Objetivo del estudio:

  • Desarrollar y evaluar un marco para emparejar clientes con terapeutas utilizando datos de archivo y algoritmos.
  • Mejorar los resultados del cliente en comparación con la asignación al terapeuta disponible a continuación.
  • Incorporar las restricciones de disponibilidad del terapeuta en el proceso de emparejamiento.

Principales métodos:

  • Se utilizaron datos de encuestas de alta dimensionalidad (CCAPS-62, OQ-45) de 10,100 cursos de terapia (2014-2019).
  • Se estimaron las asociaciones de resultados entre terapeuta y cliente utilizando modelado predictivo.
  • Se implementaron algoritmos de emparejamiento restringido que respetan la disponibilidad del terapeuta.
  • Se evaluó el rendimiento mediante simulaciones y pruebas estadísticas.

Principales resultados:

  • El modelo predijo pares de terapeutas-clientes asociados con una mejora promedio de 0.145-3.65 puntos OQ-45 (d=0.11).
  • Esta mejora fue estadísticamente significativa (intervalo de confianza del 95%).
  • El método demostró potencial para mejorar los resultados con un impacto mínimo en los tiempos de espera y los cambios administrativos.

Conclusiones:

  • La coincidencia de cliente-terapeuta basada en datos puede mejorar significativamente los resultados de la consejería.
  • Tener en cuenta las restricciones de disponibilidad del terapeuta es crucial para la implementación práctica.
  • Este enfoque ofrece una solución escalable para mejorar la efectividad de los servicios de salud mental.