3M M*Modal Fluency

A clinician engages in a natural conversation with a patient while 3M M*Modal Fluency's ambient AI (e.g., Fluency for Imaging) passively and securely listens and processes the dialogue.

The AI leverages advanced speech recognition and NLU to identify key medical concepts, diagnoses, procedures, and treatment plans from the conversation, generating a preliminary clinical note.

The clinician reviews the AI-generated note, making any necessary minor edits or additions directly within their Electronic Health Record (EHR) interface, and then signs off.

Result: Documentation time is dramatically reduced, allowing clinicians to focus more on patient interaction and improving overall workflow efficiency.

3M M*Modal Fluency's cNLU engine analyzes clinician documentation in real-time or asynchronously for specificity, completeness, and consistency against coding guidelines and clinical best practices.

The system proactively identifies documentation gaps, ambiguous language, or missing details that could impact coding, and generates intelligent, context-aware queries or prompts for clinicians.

CDI specialists can utilize the platform's insights to prioritize high-impact charts and provide targeted education, while clinicians receive feedback directly at the point of care to clarify or enhance documentation.

Result: Improved documentation quality, accurate coding, reduced audit risks, and optimized reimbursement, leading to a stronger revenue cycle.

M*Modal Fluency's NLP engine automatically extracts all relevant clinical information (diagnoses, procedures, conditions, etc.) from the physician's clinical note, whether generated by ambient AI or dictation.

Based on the extracted information and integrated coding guidelines, the system intelligently suggests appropriate medical codes (e.g., ICD-10, CPT, DRG, HCCs) to professional coders.

Coders review the AI-suggested codes and supporting documentation, validating their accuracy and making any necessary adjustments, benefiting from the platform's audit trails and explanations for code rationale.

Result: Accelerated coding processes, significant reduction in coding errors, improved claims submission efficiency, and enhanced compliance with coding regulations.