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The nursing process uses scientific reasoning, problem-solving, and critical thinking to guide nurses in providing patients with appropriate care. This process is a systematic approach to recognize, avoid, and treat current or potential health issues while promoting the patient's well-being.
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An interdisciplinary team includes many healthcare professionals working together and utilizing their skills, knowledge, and expertise to provide holistic and quality patient care. Here are a few more healthcare professionals.
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Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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Video Experimental Relacionado

Updated: Feb 28, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Identificación del Rol del Hablante en Conversaciones Clínicas

Andrew Zolensky1, Kuk Jin Jang2, Janice Sabin3

  • 1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA, Andrew.Zolensky@PennMedicine.upenn.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
Resumen

Los modelos de lenguaje grandes (LLM) ahora pueden identificar automáticamente los roles de los hablantes en las conversaciones clínicas. Esta tecnología mejora el análisis de la comunicación entre pacientes y médicos al distinguir con precisión entre médicos, pacientes y otros cuidadores.

Palabras clave:
identificación del rol del hablanteconversaciones clínicasmodelos de lenguaje grandesBERTanálisis de la comunicacióndiarizacióninformática médicalingüística computacionalinteligencia artificial en la atención médica

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Assessment and Communication for People with Disorders of Consciousness
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Área de la Ciencia:

  • Informática Médica
  • Lingüística Computacional
  • Inteligencia Artificial en la Atención Médica

Sus antecedentes:

  • La comunicación entre pacientes y médicos es vital para comprender las interacciones y los resultados de la atención médica.
  • El análisis manual del discurso clínico es lento y desafiante, particularmente para la Identificación del Rol del Hablante (SRI).
  • Los sistemas de reconocimiento automático de voz existentes con diarización carecen de la capacidad de asignar roles específicos a los hablantes.

Objetivo del estudio:

  • Investigar la efectividad de los Modelos de Lenguaje Grandes (LLM) para la Identificación del Rol del Hablante (SRI) en entornos clínicos.
  • Evaluar el rendimiento de SRI utilizando solo características lingüísticas en comparación con la incorporación de identificadores de diarización.
  • Evaluar el impacto de la corrupción de identificadores en la precisión de SRI basada en LLM.

Principales métodos:

  • Se utilizó BERT, un Modelo de Lenguaje Grande, para la Identificación del Rol del Hablante (SRI) en transcripciones clínicas.
  • Se emplearon la segmentación de turnos veridical y los identificadores de diarización.
  • Se ajustó el modelo BERT con diferentes niveles de corrupción de identificadores para probar la robustez del rendimiento.

Principales resultados:

  • BERT logró una precisión del 82% y una puntuación F1 del 82% para SRI utilizando solo señales lingüísticas.
  • La incorporación de identificadores de diarización precisos mejoró significativamente el rendimiento a una precisión del 95% y una puntuación F1 del 95%.
  • El estudio demostró la capacidad de los LLM en SRI dentro de contextos clínicos.

Conclusiones:

  • Los Modelos de Lenguaje Grandes (LLM) ajustados son muy efectivos para la Identificación Automática del Rol del Hablante (SRI) en transcripciones clínicas.
  • Los LLM ofrecen una solución robusta para analizar la dinámica de la comunicación entre pacientes y médicos.
  • La combinación de LLM con una diarización precisa mejora aún más la precisión de SRI en entornos de atención médica.