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Triaje médico e inteligencia artificial: Un binomio sorprendente

Carlos

12 December 2021

Reading time: 3 minutes

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Si una cosa, entre otras muchas, ha puesto evidentemente clara la pandemia del Covid-19 es la total y absoluta necesidad de un adecuado sistema de triaje o cribado médico.

Gran cantidad de retrasos y aglomeraciones han sido las características básicas del sistema sanitario en este último periodo de tiempo desde el 2018 hasta nuestros días.

Una de las claras consecuencias de ello es la gran cantidad de pacientes que han tenido una evaluación deficiente con las graves consecuencias que ello puede implicar para su salud. Quizá esto con (AI) inteligencia artificial se podría solucionar…

Una consecuencia negativa es la obtención de un mal diagnostico originado por la realización de un mal triaje o sub-triaje. Éste repercute directamente en el sistema sanitario, ya de por sí muy sobrecargado, provocando probablemente el retorno del paciente, con la misma sintomatología que ya presentaba o incluso sintomatología o condiciones médicas nuevas y peores producidas por este mal diagnóstico.

De todas estas cuestiones se deriva que un triaje adecuado, sobre todo ante los sistemas de urgencia, asegura una mayor cantidad de diagnósticos acertados así como un funcionamiento óptimo del propio sistema sanitario. Tiempos de espera y atenciones adecuadas repercuten en el sistema sanitario reduciendo considerablemente el gasto médico general.

Esquema de triaje
Esquema de triaje. En el esquema están presentas distintas variables. RR: frecuencia respiratoria; SpO2: saturación de oxígeno periférico; HR: frecuencia cardíaca; GCS: Score de Glasgow Coma; Tp: temperatura. Estas variables son fuertes predictores de ingreso en la unidad de cuidados intensivos y mortalidad hospitalaria.

La (AI) Inteligencia Artificial como el gran aliado del cribado y triaje médico

El impulso de la inteligencia artificial, con sus algoritmos predictivos, ha permitido encontrar una relación interesante con respecto a la atención sanitaria. Nos referimos a la cuestión de la priorización de la atención.

Los tríales a través de soluciones basadas en inteligencia artificial nos permiten alimentar estas decisiones de priorización con un conjunto de datos mucho mayor (por ejemplo en el triaje Manchester se categoriza la consulta en 52 categorías con solo 4 o 5 preguntas al paciente).

Debido al estado prematuro de la técnica estos sistemas se han convertido (en estos momentos) en grandes sistemas de apoyo a las decisiones médicas, ya que en última instancia va a ser el profesional el que adopte la decisión más adecuada para cada paciente.

Como ya se ha comentando anteriormente los algoritmos de inteligencia artificial que se están aplicando son capaces, a diferencia del personal sanitario, de contemplar y analizar grandes cantidades de información en periodos de tiempo muy cortos, hojas e historias clínicas, patologías previas, condiciones previas, valoraciones de otros profesionales anteriores. Con todos estos datos pueden clasificar al paciente de forma rápida y automática en categorías con distinta prioridad de atención.

Por poner un ejemplo, a través de un sistema de IA podemos realizar un triaje de manera remota a un paciente, explicar la sintomatología que refiere, incluso asignar la atención médica adecuada de manera remota, si necesita una visita domiciliaria, una ambulancia o cualquier otro recurso sin que el propio paciente haya tenido que acudir al servicio de urgencias contribuyendo a su congestión.

Beneficios del machine learning (IA) en el triaje médico

Finalizamos el artículo comentando que en la actualidad varios modelos de machine learning, es decir artificial intelligence, está mostrando resultados muy prometedores en distintos campos del triaje médico, facilitando de este modo enormemente la labor de los profesionales sanitarios. Un ejemplo de estos estudios lo podemos encontrar en el siguiente artículo.

Conclusiones finales de la aparición del uso de AI en los sistemas de triaje

Las conclusiones son claras y contundentes, con el avance tecnológico y la incorporación con ellos del machine learning, aprendizaje máquina o computacional al sistema de triaje, se produce una flexibilización y una mejora de la atención en cuanto a tiempos y gestión de recursos.

Un sistema sanitario, sobre todo a nivel de unidades de urgencias, que tiene sus recursos y tiempos optimizados se convierte en un sistema totalmente eficiente tanto para los pacientes como para la propia comunidad de profesionales sanitarios.

Carlos

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