Scribeberry

Converts physician dictation into text using advanced speech recognition models trained on medical terminology, clinical speech patterns, and domain-specific vocabulary. The system processes audio streams in real-time, applying medical language models to disambiguate clinical terms (e.g., 'lesion' vs 'legion') and maintain accuracy across diverse medical specialties. Integration with EHR systems (Epic, Cerner) enables direct insertion of transcribed text into patient notes without manual copy-paste workflows.

Automatically maps transcribed dictation to structured clinical note templates within Epic, Cerner, or other EHR systems, populating assessment/plan sections, vital signs, and other standardized fields. The system uses pattern matching and NLP to extract clinical entities (diagnoses, medications, procedures) from free-text dictation and insert them into the correct EHR template fields, reducing manual template navigation and field-by-field data entry.

Allows clinicians or administrators to define custom medical terminology, institutional jargon, and specialty-specific vocabulary that the speech recognition engine learns to recognize and transcribe accurately. The system maintains a custom vocabulary database per clinic or provider, enabling the model to disambiguate context-specific terms (e.g., 'Jones fracture' in orthopedics vs. generic 'fracture') and reduce transcription errors for domain-specific language.

Provides a freemium tier allowing clinicians to test Scribeberry without upfront commitment, with usage limits (e.g., minutes of transcription per month) and feature restrictions (e.g., no EHR integration). Paid tiers unlock full EHR integration, higher usage limits, and premium features. The system tracks usage per user or clinic and enforces quota limits, with transparent billing and upgrade paths.

Displays transcribed text in real-time with visual indicators (highlighting, confidence scores) for low-confidence words or phrases, allowing clinicians to immediately correct errors during or after dictation. Corrections are logged and can feed back into the model to improve future accuracy for that user or clinic. The system maintains a correction history and provides undo/redo functionality for rapid editing.

Supports deployment across multiple clinicians within a clinic or health system with role-based access control (admin, provider, staff). Administrators can manage user accounts, configure clinic-wide settings (EHR integration, custom vocabulary), and monitor usage across providers. Each provider has isolated transcription history and custom vocabulary, while admins have visibility into clinic-wide metrics and compliance.

Tracks transcription accuracy metrics (word error rate, confidence scores, error patterns) and provides analytics dashboards showing performance trends over time. The system identifies common error patterns (e.g., specific words or accents that are frequently misrecognized) and can surface recommendations for improvement (e.g., custom vocabulary additions, microphone upgrades). Accuracy is measured against manual corrections and can be compared across providers or specialties.

Processes audio and transcription data on secure cloud infrastructure with HIPAA-compliant encryption (in-transit and at-rest), access controls, and audit logging. Audio files are encrypted before transmission, processed in isolated environments, and deleted after transcription (with configurable retention policies). The system maintains audit logs of all data access and processing for compliance verification.