{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_hona-ai","slug":"hona-ai","name":"Hona AI","type":"product","url":"https://hona.ai","page_url":"https://unfragile.ai/hona-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_hona-ai__cap_0","uri":"capability://text.generation.language.medical.context.aware.patient.record.summarization","name":"medical-context-aware patient record summarization","description":"Automatically generates concise clinical summaries from verbose patient records by applying domain-specific NLP models trained on medical terminology, clinical abbreviations, and healthcare documentation standards. The system identifies clinically relevant information (diagnoses, medications, allergies, procedures) and filters noise from administrative boilerplate, producing structured summaries that preserve clinical accuracy while reducing length by 60-80%. Uses medical entity recognition and relationship extraction to understand clinical context rather than generic text compression.","intents":["I need to quickly understand a patient's medical history without reading through 50 pages of unstructured notes","I want to generate a standardized clinical summary that highlights critical information for handoffs between providers","I need to extract key clinical facts from legacy paper or scanned records to populate structured EHR fields"],"best_for":["Hospital systems and clinics with high documentation volume seeking to reduce clinician time spent reading records","Healthcare practices managing complex patient histories across multiple encounters","Administrative staff preparing patient summaries for referrals or insurance submissions"],"limitations":["Accuracy degrades on handwritten or heavily scanned records with OCR errors — requires clean digital text input","May miss rare or atypical diagnoses if training data underrepresents edge cases in medical literature","No real-time learning from corrections — requires manual retraining to improve domain-specific accuracy","Cannot infer causality between clinical events; produces summaries of facts, not clinical narratives"],"requires":["Patient records in digital text format (PDF, HL7, CCD, or plain text)","HIPAA-compliant data handling infrastructure on customer side","Integration with existing EHR system or document management platform"],"input_types":["unstructured clinical notes (progress notes, discharge summaries, consultation letters)","semi-structured records (HL7 CCD, FHIR documents)","scanned/OCR'd documents (with quality caveats)"],"output_types":["structured clinical summary (text with tagged entities)","key clinical facts (JSON with diagnoses, medications, allergies, procedures)","formatted summary for EHR import"],"categories":["text-generation-language","data-processing-analysis","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_1","uri":"capability://data.processing.analysis.patient.record.format.transformation.and.normalization","name":"patient record format transformation and normalization","description":"Converts patient records from multiple source formats (unstructured notes, HL7 v2, FHIR, CCD, proprietary EHR exports) into a standardized internal representation, then outputs to target formats required by downstream systems. Uses schema mapping and field extraction to normalize inconsistent data structures (e.g., different date formats, medication naming conventions, provider identifiers) and resolve semantic equivalences across systems. Handles missing or malformed fields gracefully with fallback rules and validation.","intents":["I need to ingest patient records from multiple hospital systems with different EHR platforms and normalize them into a single format","I want to export patient summaries in FHIR format for interoperability with other healthcare applications","I need to clean and standardize legacy patient data before importing into a new EHR system"],"best_for":["Health systems integrating data from multiple EHR vendors or legacy systems","Healthcare data warehouses and analytics platforms requiring normalized input","Practices migrating from one EHR to another and needing data transformation"],"limitations":["Requires pre-defined schema mappings for each source format — new formats require manual configuration","Cannot infer missing critical fields (e.g., if a record lacks a date, system cannot guess it)","Lossy transformation: unstructured narrative data may lose clinical nuance when forced into structured fields","No bidirectional sync — transformations are one-way; updates in source systems require re-transformation"],"requires":["Source records in supported formats (HL7 v2, FHIR R4, CCD, or proprietary EHR export formats)","Target system API or import specification (FHIR endpoint, HL7 receiver, database schema)","Data mapping configuration (may be provided by Hona or customer-defined)"],"input_types":["HL7 v2 messages","FHIR JSON/XML documents","CCD (Continuity of Care Document) XML","unstructured clinical notes","proprietary EHR export formats"],"output_types":["normalized FHIR resources (Patient, Condition, Medication, Procedure, etc.)","HL7 v2 messages","structured JSON/CSV for data warehouse import","CCD documents"],"categories":["data-processing-analysis","tool-use-integration","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_2","uri":"capability://automation.workflow.batch.patient.record.processing.with.workflow.orchestration","name":"batch patient record processing with workflow orchestration","description":"Processes large volumes of patient records (hundreds to thousands) through a multi-step pipeline: ingestion → validation → summarization → transformation → export. Implements asynchronous job queuing with progress tracking, error handling, and retry logic for failed records. Supports scheduled batch jobs (e.g., nightly imports) and on-demand processing. Provides audit logging of all transformations for compliance and debugging.","intents":["I need to process 10,000 patient records overnight and have summaries ready for clinicians by morning","I want to set up a recurring daily import of new patient records from our hospital's EHR","I need to track which records failed processing and why so I can fix them"],"best_for":["Large healthcare systems with high-volume record processing needs","Practices implementing regular data synchronization between systems","Healthcare IT teams managing data pipelines and requiring audit trails"],"limitations":["Batch processing introduces latency — records are not available immediately after upload; typical processing time is hours, not minutes","No real-time streaming mode — designed for bulk operations, not live record updates","Error handling is record-level; if one record fails, it doesn't block the batch, but manual intervention is required to retry","Audit logs may grow large for high-volume systems; retention policies and log archival are customer responsibility"],"requires":["API access to Hona AI batch processing service or on-premises deployment","Source records in supported formats (HL7, FHIR, CCD, or plain text)","Destination system API or database connection for output","Sufficient storage for temporary processing artifacts and audit logs"],"input_types":["bulk file uploads (CSV, JSON, HL7 batch files)","API-driven record submission","scheduled imports from source EHR systems"],"output_types":["processed records in target format","batch processing report (success/failure counts, error details)","audit log entries"],"categories":["automation-workflow","data-processing-analysis","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_3","uri":"capability://data.processing.analysis.clinical.entity.extraction.and.tagging.from.unstructured.notes","name":"clinical entity extraction and tagging from unstructured notes","description":"Identifies and tags clinical entities (diagnoses, medications, allergies, procedures, lab results, vital signs) within unstructured clinical notes using medical NLP and named entity recognition (NER) models. Extracts relationships between entities (e.g., 'patient is allergic to penicillin') and normalizes entity references to standard medical codes (ICD-10, SNOMED CT, RxNorm). Outputs structured data suitable for EHR import or downstream analytics.","intents":["I want to automatically extract all medications and dosages from a discharge summary without manual review","I need to identify all documented allergies in a patient's notes and flag any drug-allergy conflicts","I want to populate structured EHR fields (diagnosis, medication lists) from free-text clinical notes"],"best_for":["Healthcare systems with legacy unstructured notes that need to be converted to structured data","Clinical decision support systems requiring structured clinical facts as input","Healthcare analytics teams building datasets from clinical narratives"],"limitations":["Accuracy varies by entity type: medication extraction is ~95% accurate, but rare diagnoses or non-standard abbreviations may be missed","Negation handling is imperfect: system may extract 'no history of diabetes' as a positive diabetes diagnosis if negation is implicit or ambiguous","Requires clean text input; heavily abbreviated or non-standard clinical shorthand reduces accuracy","No temporal reasoning: cannot determine if an entity is current, historical, or ruled out based on context alone"],"requires":["Unstructured clinical text in English (other languages not supported)","Medical coding system access (ICD-10, SNOMED CT, RxNorm) for entity normalization","Downstream system capable of ingesting structured entity data (JSON or database)"],"input_types":["clinical progress notes","discharge summaries","consultation letters","free-text clinical narratives"],"output_types":["structured entity list (JSON with entity type, text span, normalized code)","relationship triples (subject-predicate-object, e.g., 'patient-allergic_to-penicillin')","tagged clinical text (with entity annotations)"],"categories":["data-processing-analysis","text-generation-language","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_4","uri":"capability://safety.moderation.hipaa.compliant.data.handling.and.encryption","name":"hipaa-compliant data handling and encryption","description":"Implements end-to-end encryption for patient data in transit (TLS 1.2+) and at rest (AES-256), with key management and access controls to ensure only authorized users can decrypt PHI. Provides audit logging of all data access and processing, with immutable logs for compliance verification. Supports data retention policies and secure deletion (cryptographic erasure) to meet HIPAA requirements. May include on-premises deployment options for customers requiring data residency.","intents":["I need to ensure patient data is encrypted and cannot be accessed by unauthorized users","I want to maintain audit logs proving that only authorized staff accessed patient records","I need to delete patient data securely when requested (right to be forgotten)"],"best_for":["Healthcare organizations subject to HIPAA compliance requirements","Practices handling sensitive patient data and requiring strong data protection","Healthcare systems with data residency or sovereignty requirements"],"limitations":["Encryption adds latency to data processing — typical overhead is 50-200ms per operation","Key management is customer responsibility; Hona AI does not manage encryption keys (customer must provide or manage via HSM)","Audit logs themselves are PHI and must be protected; customer is responsible for secure log storage and retention","On-premises deployment requires customer infrastructure and operational overhead; cloud deployment may not meet data residency requirements for some jurisdictions"],"requires":["TLS 1.2+ support in all client systems","Key management infrastructure (customer-managed or HSM-based)","Audit log storage with sufficient capacity and retention policies","HIPAA Business Associate Agreement (BAA) with Hona AI"],"input_types":["patient data in any supported format (HL7, FHIR, unstructured notes, etc.)"],"output_types":["encrypted data at rest","encrypted data in transit","audit logs (encrypted or access-controlled)"],"categories":["safety-moderation","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_5","uri":"capability://tool.use.integration.integration.with.ehr.systems.via.api.and.hl7.fhir.standards","name":"integration with ehr systems via api and hl7/fhir standards","description":"Provides REST API and HL7/FHIR endpoints for bidirectional integration with EHR systems, allowing real-time or batch data exchange. Supports OAuth 2.0 authentication and role-based access control (RBAC) to ensure only authorized EHR users can trigger processing. Implements standard healthcare data exchange protocols (HL7 v2, FHIR R4) with validation to ensure data integrity. May include pre-built connectors for major EHR vendors (Epic, Cerner, Athena, etc.).","intents":["I want to automatically send patient records from our Epic EHR to Hona AI for summarization and get results back","I need to expose Hona AI's summarization capability as a FHIR endpoint so other healthcare apps can call it","I want to set up a secure API connection between our EHR and Hona AI with role-based access control"],"best_for":["Healthcare IT teams integrating Hona AI into existing EHR workflows","Health systems with multiple EHR platforms requiring a unified integration layer","Healthcare developers building custom applications that need to call Hona AI services"],"limitations":["API rate limits apply; high-volume requests may be throttled or require premium tier","Pre-built connectors only available for major EHR vendors; custom EHR systems require manual API integration","FHIR endpoint validation is strict; non-compliant data will be rejected, requiring upstream systems to fix data quality","Authentication setup requires IT coordination; OAuth 2.0 configuration is not trivial for non-technical users"],"requires":["REST API client library or HTTP client (curl, Postman, etc.)","OAuth 2.0 credentials (client ID, client secret) from Hona AI","EHR system with API or HL7/FHIR export capability","Network connectivity between EHR and Hona AI (may require firewall rules or VPN)"],"input_types":["FHIR JSON/XML documents","HL7 v2 messages","REST API JSON payloads"],"output_types":["FHIR resources (Patient, Condition, Medication, etc.)","HL7 v2 messages","REST API JSON responses"],"categories":["tool-use-integration","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_6","uri":"capability://text.generation.language.configurable.summarization.templates.and.output.formats","name":"configurable summarization templates and output formats","description":"Allows healthcare organizations to define custom summarization templates that specify which clinical information to include, in what order, and in what format. Supports multiple output formats (plain text, structured JSON, FHIR ClinicalDocument, proprietary EHR formats) so summaries can be directly imported into downstream systems. Templates can be versioned and audited for compliance. Enables organizations to enforce consistent documentation standards across providers.","intents":["I want summaries to follow our hospital's standard format for discharge summaries (problem list, medications, follow-up instructions, etc.)","I need to generate summaries in different formats for different use cases (referral letters, insurance submissions, internal handoffs)","I want to ensure all summaries include specific sections required by our quality assurance team"],"best_for":["Healthcare organizations with standardized documentation requirements","Practices wanting to enforce consistent clinical documentation across providers","Health systems with multiple departments requiring different summary formats"],"limitations":["Template configuration requires clinical and IT expertise; non-technical users may struggle to define complex templates","Templates are static; they cannot adapt dynamically based on patient-specific factors (e.g., different summary for pediatric vs. adult patients)","Output format conversion may lose information if target format is less expressive than source data","Template versioning adds complexity; managing multiple versions across an organization requires governance"],"requires":["Template definition language or UI (provided by Hona AI)","Clinical input to define what information should be included in summaries","Target system specifications for output format validation"],"input_types":["template definitions (JSON, YAML, or proprietary format)","clinical data in any supported format"],"output_types":["summaries in custom format","plain text","structured JSON","FHIR ClinicalDocument","proprietary EHR formats"],"categories":["text-generation-language","automation-workflow","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hona-ai__cap_7","uri":"capability://text.generation.language.multi.language.clinical.note.processing.with.terminology.mapping","name":"multi-language clinical note processing with terminology mapping","description":"Processes clinical notes in multiple languages (English, Spanish, French, German, etc.) and normalizes medical terminology across languages to standard medical codes (ICD-10, SNOMED CT). Handles language-specific clinical abbreviations and regional variations in medical terminology (e.g., 'hypertension' vs. 'high blood pressure'). Outputs summaries in requested language or in standardized medical codes for language-agnostic downstream systems.","intents":["I need to process patient records written in Spanish and generate summaries in English for our English-speaking clinicians","I want to normalize medical terminology across notes written in different languages so I can aggregate data for analytics","I need to handle regional variations in medical terminology (e.g., British vs. American English) in clinical notes"],"best_for":["Healthcare systems serving multilingual patient populations","International health networks with providers in different countries","Healthcare analytics platforms aggregating data from multilingual sources"],"limitations":["Language support is limited to pre-trained languages; rare languages or dialects are not supported","Translation quality varies by language pair and clinical domain; rare medical terms may be mistranslated","Terminology mapping assumes standard medical coding systems; regional variations or non-standard terminology may not map correctly","Processing time increases for multilingual documents; typical overhead is 20-50% per additional language"],"requires":["Clinical notes in supported languages (English, Spanish, French, German, etc.)","Target language for output (if translation is needed)","Medical coding system access (ICD-10, SNOMED CT) for terminology mapping"],"input_types":["clinical notes in supported languages","mixed-language documents"],"output_types":["summaries in requested language","standardized medical codes (language-agnostic)","terminology mappings (source term → standard code)"],"categories":["text-generation-language","data-processing-analysis","healthcare-automation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Patient records in digital text format (PDF, HL7, CCD, or plain text)","HIPAA-compliant data handling infrastructure on customer side","Integration with existing EHR system or document management platform","Source records in supported formats (HL7 v2, FHIR R4, CCD, or proprietary EHR export formats)","Target system API or import specification (FHIR endpoint, HL7 receiver, database schema)","Data mapping configuration (may be provided by Hona or customer-defined)","API access to Hona AI batch processing service or on-premises deployment","Source records in supported formats (HL7, FHIR, CCD, or plain text)","Destination system API or database connection for output","Sufficient storage for temporary processing artifacts and audit logs"],"failure_modes":["Accuracy degrades on handwritten or heavily scanned records with OCR errors — requires clean digital text input","May miss rare or atypical diagnoses if training data underrepresents edge cases in medical literature","No real-time learning from corrections — requires manual retraining to improve domain-specific accuracy","Cannot infer causality between clinical events; produces summaries of facts, not clinical narratives","Requires pre-defined schema mappings for each source format — new formats require manual configuration","Cannot infer missing critical fields (e.g., if a record lacks a date, system cannot guess it)","Lossy transformation: unstructured narrative data may lose clinical nuance when forced into structured fields","No bidirectional sync — transformations are one-way; updates in source systems require re-transformation","Batch processing introduces latency — records are not available immediately after upload; typical processing time is hours, not minutes","No real-time streaming mode — designed for bulk operations, not live record updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hona-ai","compare_url":"https://unfragile.ai/compare?artifact=hona-ai"}},"signature":"BsmqW7hX7mTlLLTEgTU4CjX0z169f8GtoahdVW4RfyQnm3MAglryZqFU/89iiOLZQbX/pN7LRzA72mgM/1xVAw==","signedAt":"2026-06-20T13:16:04.719Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hona-ai","artifact":"https://unfragile.ai/hona-ai","verify":"https://unfragile.ai/api/v1/verify?slug=hona-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}