Clinical Communication Platform

Abstract

Effective clinician–patient communication is a critical determinant of patient outcomes, clinician burnout, and overall quality of care, yet clinicians currently lack objective, scalable tools to evaluate their communication behaviors outside of subjective mentorship. To address this gap, our team is developing a post-encounter multi-modal analysis platform that provides clinicians with rapid, data-driven feedback on empathetic communication using recorded audio from real or simulated patient interactions. This solution was selected through a weighted engineering decision matrix, where it demonstrated superior feasibility, alignment with user needs, and compatibility with existing clinical workflows compared to real-time haptics and VR simulation concepts.

The system integrates engineering principles across speech processing, deep learning, and human-centered interface design. Audio is securely uploaded via a HIPAA-compliant pathway and processed through speaker diarization, automated speech-to-text transcription, and a multi-modal acoustic analysis pipeline. Objective measures such as pitch, intensity, speaking rate, and emotional tone are extracted to characterize “how” the clinician communicates, while a Large Language Model synthesizes these data streams to identify moments of empathetic phrasing, missed opportunities, and communication strengths. The platform returns actionable insights within 5–30 seconds, supporting immediate post-interaction reflection and longitudinal skill tracking.]

Critical performance specifications include >95% accuracy for empathetic statement detection, >90% accuracy for emotional cue classification, <5 seconds ideal feedback latency, high transcription accuracy, and full HIPAA compliance. As a Software as a Medical Device intended for educational use, the system is designed with secure data handling, robust reliability, and scalability for deployment across medical training programs. Manufacturing considerations center on cloud-based infrastructure, model optimization to reduce compute costs, and long-term maintainability through modular architecture.

By providing clinicians with objective, interpretable, and timely feedback, this platform aims to enhance empathetic communication, reduce cognitive load compared to real-time methods, and offer an accessible, evidence-based tool for clinical education and professional development.

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