Nudge AI

Captures unstructured spoken clinical interactions (patient-provider conversations, examinations, procedures) via ambient microphone input and converts them to structured clinical notes using speech-to-text with medical vocabulary optimization. The system processes audio streams in real-time, applies domain-specific language models trained on clinical terminology and EHR note patterns, and outputs formatted documentation without requiring manual dictation or pause-and-record workflows.

Transforms raw transcribed text into properly formatted clinical notes aligned with EHR schema and clinical documentation standards (SOAP, HPI, Assessment/Plan). Uses rule-based and ML-based segmentation to identify clinical sections (subjective, objective, assessment, plan), extract key clinical entities (diagnoses, medications, vital signs), and populate structured fields. The system learns from provider editing patterns to improve formatting accuracy over time.

Analyzes documented clinical encounters to suggest appropriate diagnostic codes (ICD-10), procedure codes (CPT), and billing modifiers based on documented findings and procedures. Uses NLP to extract clinical concepts from notes, maps them to standardized coding taxonomies, and flags potential compliance issues (missing documentation for billed codes, undercoding, overcoding). Integrates with EHR coding workflows to surface suggestions at point of documentation.

Learns individual clinician documentation patterns, preferences, and terminology through analysis of historical notes and real-time editing feedback. Adapts transcription processing, note structuring, and code suggestions to match each provider's style, abbreviations, and documentation conventions. Uses feedback loops (provider edits, code selections, note approvals) to continuously refine models at the individual provider level.

Monitors documented clinical information in real-time to identify potential safety issues, drug interactions, contraindications, and guideline deviations. Integrates with clinical knowledge bases (drug formularies, clinical guidelines, allergy databases) to flag issues as they are documented. Generates contextual alerts and recommendations that surface at point of documentation without interrupting workflow.

Adapts transcription, note structuring, and coding suggestion to specialty-specific documentation standards, terminology, and workflows. Supports multiple clinical specialties (primary care, cardiology, orthopedics, etc.) with specialty-specific language models, coding rules, and documentation templates. Also supports multilingual documentation for diverse patient and provider populations, with medical terminology translation and localization.

Integrates with major EHR systems (Epic, Cerner, Athena, etc.) via HL7, FHIR, or vendor-specific APIs to enable seamless data flow. Synchronizes patient context (demographics, allergies, medications, problem list) from EHR to inform documentation, and writes generated notes back to EHR in native format. Handles authentication, data validation, and error handling to ensure data integrity and compliance.

Maintains comprehensive audit logs of all documentation activities, including transcription source, AI-generated content, provider edits, code selections, and final note approval. Generates compliance reports demonstrating documentation accuracy, coding compliance, and adherence to clinical guidelines. Supports regulatory requirements (HIPAA, state medical board rules, payer audits) by providing detailed documentation of the documentation process.