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Alineación de los objetivos de predicción de inteligencia artificial con los flujos de trabajo clínicos mediante

Mark V Mai, H Stella Shin, Naveen Muthu

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    Resumen
    Este resumen es generado por máquina.

    El diseño centrado en el ser humano mejora la IA en la atención médica al alinear los modelos predictivos con los flujos de trabajo clínicos. Este enfoque garantiza que las herramientas de inteligencia artificial (IA) respalden intervenciones procesables, mejorando los resultados de los pacientes.

    Palabras clave:
    diseño centrado en el ser humanointeligencia artificial en la atención médicaflujos de trabajo clínicosresultados de los pacientesintervenciones procesableslesión renal aguda pediátricafactores sociotécnicosutilidad clínica de la IA

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    Área de la Ciencia:

    • Informática de la atención médica
    • Sistemas de apoyo a la decisión clínica
    • Interacción humano-computadora

    Sus antecedentes:

    • Los modelos de inteligencia artificial (IA) en la atención médica a menudo muestran una gran precisión predictiva, pero no mejoran los resultados de los pacientes debido a una mala integración con los flujos de trabajo clínicos.
    • Existe una brecha entre el rendimiento técnico de los modelos de IA y su utilidad clínica en el mundo real.

    Objetivo del estudio:

    • Demostrar un enfoque de diseño centrado en el ser humano para desarrollar modelos de IA en la atención médica.
    • Garantizar que los objetivos de predicción de la IA se alineen con las intervenciones clínicas procesables y mejoren los resultados de los pacientes.

    Principales métodos:

    • Se utilizó un estudio de caso de lesión renal aguda pediátrica.
    • Un grupo de trabajo multidisciplinario empleó historias de usuarios, People, Environment, Technology, and Tasks (PETT) Scan y mapeo de procesos.
    • Se analizaron factores sociotécnicos y puntos de apalancamiento del flujo de trabajo antes del desarrollo del modelo de IA.

    Principales resultados:

    • Se identificaron objetivos de predicción distintos para diferentes roles clínicos (médicos de hospital, nefrólogos, intensivistas).
    • Las barreras clave incluyeron una monitorización inadecuada, una mala visibilidad de los pacientes en riesgo y una progresión poco clara de la lesión.
    • Se definieron objetivos de predicción de alto impacto para respaldar intervenciones procesables.

    Conclusiones:

    • La integración del contexto clínico y el diseño centrado en el ser humano antes del desarrollo de la IA es crucial.
    • Este enfoque cierra la brecha entre el rendimiento del modelo de IA y la utilidad clínica.
    • La metodología puede mejorar la eficacia de la IA para mejorar la atención al paciente.