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Parses complex eligibility requirements and patient data to surface appropriate trial opportunities without manual screening.","intents":["Find clinical trials my patient qualifies for without manually reviewing each trial's eligibility criteria","Quickly identify which of our patients are candidates for specific ongoing trials","Reduce the time oncologists spend on administrative trial eligibility screening"],"best_for":["Oncologists and cancer center staff","Clinical research coordinators","Cancer centers with high patient volume"],"limitations":["Accuracy depends on completeness and quality of patient data in EHR","May miss trials if eligibility criteria are ambiguously written or non-standard","Cannot match against trials not in the platform's database"],"requires":["Structured patient medical records in EHR system","Integration with hospital EHR infrastructure","Access to comprehensive clinical trial protocol database"],"input_types":["EHR patient records (structured and unstructured text)","Clinical trial protocol documents"],"output_types":["Ranked list of matching trials","Eligibility match scores","Specific criteria met/unmet for each trial"],"categories":["healthcare","clinical-decision-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_1","uri":"capability://healthcare.medical.record.parsing.and.extraction","name":"medical-record-parsing-and-extraction","description":"Extracts and structures relevant clinical information from unstructured medical records, including diagnoses, biomarkers, treatment history, and lab results. Converts free-text notes into machine-readable data for matching algorithms.","intents":["Automatically extract key clinical data from patient charts without manual data entry","Standardize patient information across different EHR formats and documentation styles","Identify specific biomarkers and genetic mutations relevant to trial eligibility"],"best_for":["Large cancer centers with high volume of unstructured clinical notes","Healthcare systems with multiple EHR platforms","Research organizations processing many patient records"],"limitations":["May struggle with handwritten notes or scanned documents","Accuracy varies based on documentation quality and clinical note structure","Requires training on institution-specific abbreviations and terminology"],"requires":["Access to patient medical records","EHR system integration","Sufficient clinical documentation in patient charts"],"input_types":["Unstructured clinical notes (text)","Lab results (structured and unstructured)","Pathology reports","Imaging reports"],"output_types":["Structured patient data (JSON/database format)","Extracted biomarkers and mutations","Standardized clinical variables"],"categories":["healthcare","data-extraction","NLP"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_2","uri":"capability://healthcare.personalized.treatment.recommendation","name":"personalized-treatment-recommendation","description":"Suggests personalized treatment options and clinical trials based on patient's specific cancer type, stage, biomarkers, and medical history. Leverages AI to match patient characteristics against treatment protocols and trial designs.","intents":["Get evidence-based treatment recommendations tailored to my patient's unique profile","Discover cutting-edge treatment options beyond standard-of-care therapies","Present patients with comprehensive treatment alternatives including clinical trial options"],"best_for":["Oncologists seeking treatment options beyond standard protocols","Patients with rare cancer subtypes or complex presentations","Cancer centers offering precision medicine programs"],"limitations":["Recommendations depend on quality of patient data and completeness of biomarker testing","May not account for patient preferences, comorbidities, or social factors","Requires oncologist review and clinical judgment before implementation"],"requires":["Comprehensive patient clinical profile","Biomarker and genetic testing results","Access to treatment protocol and trial database","Integration with clinical decision support workflows"],"input_types":["Patient medical records","Genomic/biomarker test results","Cancer diagnosis and staging information","Prior treatment history"],"output_types":["Ranked list of treatment recommendations","Clinical trial suggestions with match scores","Evidence summaries for each recommendation","Links to relevant clinical trials"],"categories":["healthcare","clinical-decision-support","precision-medicine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_3","uri":"capability://healthcare.ehr.system.integration","name":"ehr-system-integration","description":"Integrates with existing hospital and cancer center EHR systems to automatically pull patient data and push trial matching results back into clinical workflows. Enables seamless data exchange without requiring manual data export/import.","intents":["Access patient data directly from our EHR without manual export steps","Have trial matching results appear in our existing clinical workflows","Reduce friction in adopting a new trial matching tool"],"best_for":["Large hospital systems with established EHR infrastructure","Cancer centers using major EHR platforms (Epic, Cerner, etc.)","Healthcare organizations prioritizing workflow integration"],"limitations":["Integration complexity varies by EHR vendor and version","May require IT support and security review for data access","Data access permissions must be properly configured"],"requires":["Compatible EHR system (Epic, Cerner, or other major platform)","API access or HL7 integration capabilities","IT infrastructure support for integration setup","HIPAA-compliant data exchange protocols"],"input_types":["EHR API connections","HL7 messages","Patient identifiers and data requests"],"output_types":["Patient data from EHR","Trial matching results back to EHR","Clinical decision support alerts"],"categories":["healthcare","integration","workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_4","uri":"capability://healthcare.trial.database.management","name":"trial-database-management","description":"Maintains and updates a comprehensive database of clinical trials with detailed protocol information, eligibility criteria, and enrollment status. Ensures trial data is current and searchable for matching algorithms.","intents":["Ensure we're matching patients against current, active clinical trials","Access detailed trial protocol information for patient counseling","Know which trials are actively enrolling in our geographic area"],"best_for":["Cancer centers needing access to comprehensive trial databases","Research organizations tracking multiple ongoing trials","Healthcare systems serving patients in multiple geographic regions"],"limitations":["Database completeness depends on trial registration and reporting","May not include all investigator-initiated trials or early-phase studies","Trial status information may lag behind real-time enrollment changes"],"requires":["Access to clinical trial registries (ClinicalTrials.gov, etc.)","Automated data ingestion and update mechanisms","Database infrastructure for storing and indexing trial protocols"],"input_types":["Clinical trial registry data","Trial protocol documents","Enrollment status updates"],"output_types":["Searchable trial database","Trial protocol summaries","Eligibility criteria in structured format","Trial status and enrollment information"],"categories":["healthcare","data-management","clinical-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_5","uri":"capability://healthcare.patient.trial.enrollment.acceleration","name":"patient-trial-enrollment-acceleration","description":"Streamlines the patient enrollment process by reducing time spent on manual eligibility screening and documentation. Automates identification of eligible patients and prepares enrollment documentation.","intents":["Speed up the time from trial identification to patient enrollment","Reduce administrative burden on research coordinators","Increase trial enrollment rates by identifying eligible patients faster"],"best_for":["Clinical research organizations with enrollment targets","Cancer centers managing multiple concurrent trials","Research coordinators managing high patient volumes"],"limitations":["Still requires patient consent and clinical review before enrollment","Cannot overcome patient unwillingness to participate in trials","Enrollment timelines also depend on trial-specific requirements and approvals"],"requires":["Automated trial matching capability","Integration with enrollment workflow systems","Patient communication infrastructure"],"input_types":["Patient medical records","Trial eligibility criteria","Enrollment status and requirements"],"output_types":["Pre-screened eligible patient lists","Enrollment readiness assessments","Patient notification/outreach lists"],"categories":["healthcare","clinical-research","workflow-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_6","uri":"capability://healthcare.biomarker.and.mutation.identification","name":"biomarker-and-mutation-identification","description":"Identifies and extracts relevant biomarkers, genetic mutations, and molecular characteristics from patient records and genomic test results. Flags clinically significant findings relevant to trial eligibility and treatment selection.","intents":["Automatically identify which biomarkers my patient has tested for","Find trials that match my patient's specific genetic mutations","Ensure we're not missing relevant genomic findings in patient records"],"best_for":["Cancer centers with genomic testing programs","Precision medicine practices","Oncology practices treating molecularly-driven cancers"],"limitations":["Accuracy depends on quality of genomic testing and reporting","May not identify novel or rare mutations not in standard databases","Requires standardized biomarker nomenclature in medical records"],"requires":["Genomic test results in patient records","Access to biomarker/mutation databases","Standardized mutation nomenclature (HGVS, etc.)"],"input_types":["Genomic test reports","Pathology reports with molecular findings","Lab results with biomarker values"],"output_types":["Structured biomarker data","Identified mutations with clinical significance","Biomarker-matched trial recommendations"],"categories":["healthcare","genomics","precision-medicine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_7","uri":"capability://healthcare.trial.eligibility.criteria.parsing","name":"trial-eligibility-criteria-parsing","description":"Automatically parses and structures complex clinical trial eligibility criteria from protocol documents. Converts narrative eligibility requirements into machine-readable format for matching against patient data.","intents":["Automatically understand trial eligibility requirements without manual review","Standardize how eligibility criteria are interpreted across different trials","Enable consistent matching of patients against complex eligibility rules"],"best_for":["Research organizations managing many trials with varying protocols","Cancer centers needing to quickly onboard new trials","Healthcare systems seeking standardized trial evaluation"],"limitations":["May struggle with ambiguous or non-standard eligibility language","Requires manual review to ensure accurate interpretation of complex criteria","Cannot handle subjective clinical judgment criteria (e.g., 'good performance status')"],"requires":["Trial protocol documents in accessible format","NLP capability to parse clinical language","Database to store structured eligibility criteria"],"input_types":["Clinical trial protocol documents (PDF, text)","Eligibility criteria sections from trial registries"],"output_types":["Structured eligibility criteria (JSON/database format)","Inclusion/exclusion rules in machine-readable format","Flagged ambiguous or subjective criteria requiring manual review"],"categories":["healthcare","NLP","data-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_8","uri":"capability://healthcare.patient.cohort.analysis","name":"patient-cohort-analysis","description":"Analyzes patient populations to identify cohorts eligible for specific trials or treatment categories. Enables bulk screening of patient databases to surface trial opportunities across multiple patients.","intents":["Identify all patients in our system who might be eligible for a specific trial","Understand what percentage of our patient population could benefit from certain treatments","Prioritize which trials to focus enrollment efforts on based on patient population"],"best_for":["Large cancer centers with diverse patient populations","Research organizations managing multiple trials","Healthcare systems conducting population health analysis"],"limitations":["Requires access to large patient databases","Results depend on data quality across many patient records","Privacy and security considerations for bulk patient analysis"],"requires":["Access to patient database with sufficient clinical detail","Ability to run bulk queries against EHR","Data governance and privacy compliance infrastructure"],"input_types":["Patient population data from EHR","Trial eligibility criteria","Demographic and clinical filters"],"output_types":["Patient cohort lists matching trial criteria","Cohort size and demographic summaries","Trial enrollment potential assessments"],"categories":["healthcare","analytics","clinical-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_triomics__cap_9","uri":"capability://healthcare.treatment.outcome.tracking","name":"treatment-outcome-tracking","description":"Tracks and analyzes outcomes for patients enrolled in clinical trials or receiving recommended treatments. Provides feedback on treatment effectiveness and trial outcomes to inform future recommendations.","intents":["Monitor how well our patients are doing on recommended treatments","Understand which trial recommendations lead to successful outcomes","Use outcome data to improve future treatment recommendations"],"best_for":["Cancer centers with mature clinical trial programs","Healthcare systems implementing precision medicine","Research organizations studying treatment effectiveness"],"limitations":["Requires long-term follow-up data which may not be available","Outcome attribution is complex with multiple confounding factors","May require integration with external data sources for complete outcome tracking"],"requires":["Longitudinal patient follow-up data in EHR","Outcome measurement infrastructure","Data linkage to trial registries or external outcome databases"],"input_types":["Patient follow-up clinical data","Trial outcome reports","Survival and progression data"],"output_types":["Treatment outcome summaries","Effectiveness metrics by treatment type","Feedback for recommendation algorithm improvement"],"categories":["healthcare","analytics","clinical-research"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Structured patient medical records in EHR system","Integration with hospital EHR infrastructure","Access to comprehensive clinical trial protocol database","Access to patient medical records","EHR system integration","Sufficient clinical documentation in patient charts","Comprehensive patient clinical profile","Biomarker and genetic testing results","Access to treatment protocol and trial database","Integration with clinical decision support workflows"],"failure_modes":["Accuracy depends on completeness and quality of patient data in EHR","May miss trials if eligibility criteria are ambiguously written or non-standard","Cannot match against trials not in the platform's database","May struggle with handwritten notes or scanned documents","Accuracy varies based on documentation quality and clinical note structure","Requires training on institution-specific abbreviations and terminology","Recommendations depend on quality of patient data and completeness of biomarker testing","May not account for patient preferences, comorbidities, or social factors","Requires oncologist review and clinical judgment before implementation","Integration complexity varies by EHR vendor and version","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.540Z","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=triomics","compare_url":"https://unfragile.ai/compare?artifact=triomics"}},"signature":"7kPMMEYnowBnfjqDOiasFMZMCgtlRwt5R5n+R1ZFDuQcN9wc+jRBW1egvZxDziDKhL5VbXFQjzWi4nBGFY5yDA==","signedAt":"2026-06-21T22:14:02.412Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/triomics","artifact":"https://unfragile.ai/triomics","verify":"https://unfragile.ai/api/v1/verify?slug=triomics","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"}}