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Habilitación de la transparencia a nivel de episodio en la atención basada en el valor a través de directorios de

Amol Kodan1

  • 1Public Health, Monroe University, New York City, USA.

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|February 25, 2026
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Resumen

Los modelos de lenguaje grandes (LLM) pueden mejorar los directorios de proveedores de atención médica para la atención basada en el valor (VBC). Los chatbots impulsados por LLM mejoran la transparencia y la navegación, aunque la precisión de la clasificación necesita optimización para su uso generalizado.

Palabras clave:
inteligencia artificialatención basada en episodiostransparencia sanitariamodelos de lenguaje grandesdirectorios de proveedoresatención basada en el valor

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Área de la Ciencia:

  • Informática de la Salud
  • Inteligencia Artificial en la Atención Médica

Sus antecedentes:

  • Los directorios de proveedores convencionales son componentes críticos pero frágiles del sistema de atención médica de EE. UU.
  • Las deficiencias en los directorios actuales, como la falta de contexto de costos/riesgos y datos inexactos, limitan la transparencia y la efectividad de la atención basada en el valor (VBC).
  • Los modelos de pago por episodio se ven obstaculizados por herramientas inadecuadas de selección de proveedores.

Objetivo del estudio:

  • Evaluar un chatbot de directorio de proveedores impulsado por un modelo de lenguaje grande (LLM) para la navegación de la atención basada en episodios.
  • Evaluar el rendimiento de cuatro LLM (GPT-3.5-turbo, GPT-4o-mini, GPT-4o, GPT-5.1) utilizando conjuntos de datos sintéticos estructurados.
  • Examinar el rendimiento de los LLM en términos de validez de la salida, identificación de episodios, clasificación de proveedores, fidelidad numérica y riesgo de alucinación.

Principales métodos:

  • Se utilizaron 87 escenarios de prueba de lenguaje natural con conjuntos de datos de costos y rendimiento estrictamente estructurados y sintéticos.
  • Se evaluaron cuatro LLM de uso común en condiciones deterministas idénticas.
  • Se introdujo una formulación revisada de la corrección de la clasificación que prioriza la identificación precisa del episodio.

Principales resultados:

  • Todos los LLM evaluados demostraron una alta precisión en la identificación de episodios, acercándose al 91%.
  • Se observó una variabilidad sustancial en la fiabilidad de la clasificación de proveedores posteriores y la precisión numérica entre los modelos.
  • Los directorios habilitados por LLM mostraron potencial para mejorar la transparencia y la experiencia del usuario en entornos de VBC.

Conclusiones:

  • Los directorios de proveedores impulsados por LLM muestran la promesa de mejorar la transparencia y la navegación dentro de la atención basada en el valor.
  • Si bien la identificación de episodios es sólida, se necesita una mayor optimización para la precisión de la clasificación de proveedores y la fidelidad numérica antes del despliegue a gran escala.
  • Estos hallazgos preliminares sugieren que los LLM pueden mejorar significativamente la VBC al abordar las limitaciones de los directorios de proveedores convencionales.