{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_scribeberry","slug":"scribeberry","name":"Scribeberry","type":"product","url":"https://scribeberry.com","page_url":"https://unfragile.ai/scribeberry","categories":["text-writing"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_scribeberry__cap_0","uri":"capability://data.processing.analysis.real.time.clinical.speech.to.text.transcription.with.medical.vocabulary.recognition","name":"real-time clinical speech-to-text transcription with medical vocabulary recognition","description":"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.","intents":["Reduce time spent typing clinical notes by dictating directly into the EHR","Capture clinical documentation in real-time during patient encounters without post-visit transcription delays","Minimize manual typing effort for physicians with high documentation burden","Enable voice-first documentation workflows for clinicians with mobility constraints or ergonomic concerns"],"best_for":["Solo practitioners and small clinics with 1-20 providers seeking faster note completion","Physicians with high patient volume requiring rapid documentation turnaround","Clinicians using Epic or Cerner EHR systems with native integration requirements"],"limitations":["Accuracy degrades significantly with heavy accents, regional dialects, or non-native English speakers — error rates increase 15-25% in these scenarios","Background noise in clinical environments (monitors beeping, staff conversations) causes transcription errors requiring manual correction","Specialized terminology outside training data (rare diseases, new drug names, institutional jargon) requires manual correction or custom vocabulary training","Real-time processing latency of 2-5 seconds may feel sluggish in fast-paced clinical workflows"],"requires":["Microphone or audio input device with acceptable signal-to-noise ratio","Integration with Epic, Cerner, or other supported EHR system via API or direct plugin","Internet connectivity for cloud-based speech recognition processing","Active Scribeberry subscription (freemium tier available with limitations)"],"input_types":["audio stream (WAV, MP3, PCM formats)","real-time microphone input","pre-recorded dictation files"],"output_types":["plain text transcription","structured clinical note (when integrated with EHR templates)","timestamped transcript with speaker identification"],"categories":["data-processing-analysis","medical-documentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_1","uri":"capability://data.processing.analysis.ehr.native.note.template.mapping.and.structured.documentation.generation","name":"ehr-native note template mapping and structured documentation generation","description":"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.","intents":["Automatically populate EHR note templates from dictation without manual field-by-field entry","Ensure compliance with EHR documentation standards and required fields for billing and legal purposes","Reduce time spent navigating EHR interfaces to complete structured notes","Enable consistent note formatting across a clinic or health system"],"best_for":["Small-to-medium clinics using Epic or Cerner with standardized note templates","Practices seeking to improve documentation compliance without custom development","Clinicians in high-volume settings where template consistency matters for billing accuracy"],"limitations":["Limited customization of medical templates compared to enterprise solutions — specialty-specific fields may not be supported","Requires manual correction when dictation doesn't map cleanly to template structure (e.g., complex multi-system assessments)","Template mapping rules are opaque to end users — difficult to debug why a field was populated incorrectly","No support for custom institutional templates or non-standard EHR configurations"],"requires":["Integration with supported EHR system (Epic, Cerner, or other via API)","Pre-configured note templates within the EHR","Active Scribeberry subscription with EHR integration tier"],"input_types":["transcribed clinical dictation (text)","EHR template schema (XML, JSON, or proprietary format)"],"output_types":["populated EHR note template","structured clinical data (diagnoses, medications, procedures as discrete fields)","audit log of template field mappings"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_2","uri":"capability://data.processing.analysis.medical.vocabulary.customization.and.specialty.specific.terminology.training","name":"medical vocabulary customization and specialty-specific terminology training","description":"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.","intents":["Improve transcription accuracy for specialty-specific terminology (e.g., rare diseases, institutional drug names, procedural terminology)","Train the system on institutional jargon and abbreviations used within a specific clinic or health system","Reduce manual correction time for specialized documentation by pre-training the model on custom vocabulary","Enable multi-specialty deployments where different clinics use different terminology"],"best_for":["Specialty clinics (orthopedics, cardiology, oncology) with domain-specific terminology outside general medical vocabulary","Health systems with institutional abbreviations or naming conventions for procedures and medications","Practices seeking to reduce manual correction rates for specialized documentation"],"limitations":["Custom vocabulary training requires manual curation and may not scale to hundreds of terms without significant effort","No automated detection of new terminology — requires manual addition of new terms as they emerge","Training on custom vocabulary may degrade accuracy on general medical terms if not carefully balanced","Unclear how many custom terms can be added before performance degrades or latency increases"],"requires":["Access to Scribeberry admin interface or API for vocabulary management","Manual curation of specialty-specific terminology list","Active Scribeberry subscription with customization tier (if available)"],"input_types":["CSV or JSON file with custom terminology and definitions","manual entry of terms via admin interface","example sentences or usage context for each term"],"output_types":["updated speech recognition model with custom vocabulary","vocabulary management dashboard showing active custom terms","accuracy metrics for custom vs. standard vocabulary"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_3","uri":"capability://automation.workflow.freemium.trial.and.usage.based.subscription.management","name":"freemium trial and usage-based subscription management","description":"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.","intents":["Test Scribeberry's transcription quality and EHR integration before committing to paid subscription","Start with low-cost freemium tier and upgrade as usage grows","Understand pricing and ROI before deploying across a clinic or health system","Reduce friction for solo practitioners or small clinics with limited budgets"],"best_for":["Solo practitioners and small clinics with budget constraints seeking to evaluate the platform","Clinicians wanting to test transcription quality on their own dictation before purchasing","Organizations piloting the tool with a subset of providers before full deployment"],"limitations":["Freemium tier has limited transcription minutes (exact limit unclear from documentation), requiring upgrade for regular use","EHR integration only available on paid tiers, limiting freemium users to standalone transcription","No clear upgrade path or pricing transparency — requires contacting sales for enterprise quotes","Usage quotas may reset monthly, creating unpredictable costs for variable-usage clinics"],"requires":["Email address and account creation","Internet connectivity to access Scribeberry platform","Credit card for paid tier upgrades (freemium may not require payment)"],"input_types":["user account and subscription tier selection","usage tracking data (transcription minutes, API calls)"],"output_types":["subscription status and usage dashboard","billing statements and invoice records","upgrade recommendations based on usage patterns"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_4","uri":"capability://data.processing.analysis.real.time.transcription.quality.feedback.and.manual.correction.workflow","name":"real-time transcription quality feedback and manual correction workflow","description":"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.","intents":["Quickly identify and correct transcription errors before finalizing the note","Understand which words or phrases the system struggles with (low confidence indicators)","Improve future transcription accuracy by providing correction feedback to the model","Minimize time spent on post-dictation editing by catching errors in real-time"],"best_for":["Clinicians dictating in noisy environments or with accents where errors are common","Specialty practices with terminology outside the standard training data","Users seeking to improve model accuracy over time through active correction feedback"],"limitations":["Real-time correction workflow adds cognitive load during patient encounters — may slow down dictation","Unclear how correction feedback is used to improve the model — no transparency on retraining frequency or methodology","Confidence scores may be poorly calibrated, leading to false positives (highlighting correct words) or false negatives (missing errors)","Manual correction is still required for complex medical terminology, limiting time savings"],"requires":["Real-time display interface (web browser, mobile app, or EHR plugin)","Ability to edit transcribed text in real-time","Optional: feedback mechanism to log corrections for model improvement"],"input_types":["real-time audio stream","user corrections and edits to transcribed text","confidence scores from speech recognition model"],"output_types":["real-time transcription display with confidence indicators","corrected transcription text","correction history and audit log","feedback data for model retraining"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_5","uri":"capability://automation.workflow.multi.provider.clinic.deployment.and.user.role.management","name":"multi-provider clinic deployment and user role management","description":"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.","intents":["Deploy Scribeberry across a clinic with multiple providers without managing individual accounts","Configure clinic-wide EHR integration and custom vocabulary once, applied to all providers","Monitor transcription usage and accuracy metrics across the clinic for compliance and billing","Manage user access and permissions for different roles (admin, provider, staff)"],"best_for":["Small-to-medium clinics (5-50 providers) seeking centralized deployment and management","Health systems requiring clinic-wide configuration and compliance monitoring","Administrators needing visibility into usage and cost allocation across providers"],"limitations":["Role-based access control may be limited — unclear if granular permissions (e.g., read-only access to usage reports) are supported","No clear multi-clinic or multi-health-system management interface — may require separate deployments per clinic","User provisioning and deprovisioning workflows unclear — may require manual admin intervention","No integration with institutional identity management (LDAP, Active Directory, SAML) — requires manual account creation"],"requires":["Scribeberry admin account with clinic management permissions","Access to clinic-wide settings and user management interface","Active Scribeberry subscription with multi-user tier"],"input_types":["user account creation and role assignment","clinic-wide configuration settings (EHR integration, custom vocabulary)","usage and compliance data for monitoring"],"output_types":["user account management dashboard","clinic-wide usage and compliance reports","audit logs of configuration changes and user actions","billing and cost allocation reports per provider or department"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_6","uri":"capability://data.processing.analysis.transcription.accuracy.monitoring.and.performance.analytics","name":"transcription accuracy monitoring and performance analytics","description":"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.","intents":["Monitor transcription accuracy over time to identify degradation or improvement","Identify common error patterns and root causes (e.g., background noise, specific terminology)","Benchmark accuracy across providers or specialties to identify outliers or best practices","Make data-driven decisions about custom vocabulary additions or workflow improvements"],"best_for":["Clinic administrators seeking to monitor transcription quality and ROI","Quality assurance teams validating transcription accuracy for compliance","Practices using Scribeberry across multiple providers and wanting to compare performance"],"limitations":["Accuracy metrics depend on manual corrections being logged — if clinicians don't correct errors, metrics are unreliable","Word error rate (WER) may not correlate with clinical impact — a misrecognized medication name is more serious than a misrecognized adjective","No clear methodology for calculating accuracy or confidence scores — difficult to interpret results","Analytics dashboard may lack drill-down capabilities to investigate specific error patterns or providers"],"requires":["Access to Scribeberry analytics dashboard (admin or provider role)","Sufficient transcription history to generate meaningful metrics (weeks to months of data)","Manual correction logging enabled to track accuracy improvements"],"input_types":["transcription history and manual corrections","audio metadata (background noise levels, speaker characteristics)","custom vocabulary usage and effectiveness data"],"output_types":["accuracy metrics dashboard (WER, confidence scores, error patterns)","trend analysis showing accuracy over time","provider or specialty comparison reports","recommendations for improvement (custom vocabulary, workflow changes)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scribeberry__cap_7","uri":"capability://safety.moderation.secure.cloud.based.transcription.processing.with.hipaa.compliance","name":"secure cloud-based transcription processing with hipaa compliance","description":"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.","intents":["Process sensitive patient health information (PHI) in audio and transcription form while maintaining HIPAA compliance","Ensure audio data is encrypted and not accessible to unauthorized parties during transmission and storage","Maintain audit logs of data access for compliance audits and breach investigations","Configure data retention policies to meet institutional or regulatory requirements"],"best_for":["Healthcare providers and clinics required to maintain HIPAA compliance","Organizations with strict data governance requirements for PHI handling","Practices in regulated jurisdictions requiring audit trails and data retention policies"],"limitations":["Cloud-based processing means audio data leaves the clinic's infrastructure — may violate some institutional policies requiring on-premises processing","HIPAA compliance is claimed but not independently verified — no public SOC 2 or HITRUST certification mentioned","Encryption and access control details are not publicly documented — difficult to verify security claims","Data retention policies may not be flexible enough for all institutional requirements (e.g., some clinics may require longer retention)"],"requires":["HIPAA Business Associate Agreement (BAA) with Scribeberry","Secure internet connectivity for audio transmission","Compliance with institutional data governance policies"],"input_types":["audio files containing patient health information (PHI)","patient identifiers and metadata"],"output_types":["encrypted transcription data","audit logs of data access and processing","compliance reports for regulatory audits"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Microphone or audio input device with acceptable signal-to-noise ratio","Integration with Epic, Cerner, or other supported EHR system via API or direct plugin","Internet connectivity for cloud-based speech recognition processing","Active Scribeberry subscription (freemium tier available with limitations)","Integration with supported EHR system (Epic, Cerner, or other via API)","Pre-configured note templates within the EHR","Active Scribeberry subscription with EHR integration tier","Access to Scribeberry admin interface or API for vocabulary management","Manual curation of specialty-specific terminology list","Active Scribeberry subscription with customization tier (if available)"],"failure_modes":["Accuracy degrades significantly with heavy accents, regional dialects, or non-native English speakers — error rates increase 15-25% in these scenarios","Background noise in clinical environments (monitors beeping, staff conversations) causes transcription errors requiring manual correction","Specialized terminology outside training data (rare diseases, new drug names, institutional jargon) requires manual correction or custom vocabulary training","Real-time processing latency of 2-5 seconds may feel sluggish in fast-paced clinical workflows","Limited customization of medical templates compared to enterprise solutions — specialty-specific fields may not be supported","Requires manual correction when dictation doesn't map cleanly to template structure (e.g., complex multi-system assessments)","Template mapping rules are opaque to end users — difficult to debug why a field was populated incorrectly","No support for custom institutional templates or non-standard EHR configurations","Custom vocabulary training requires manual curation and may not scale to hundreds of terms without significant effort","No automated detection of new terminology — requires manual addition of new terms as they emerge","builder identity is not verified yet","no observed match outcomes 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