What can Triomics do?

clinical-trial-eligibility-matching, medical-record-parsing-and-extraction, personalized-treatment-recommendation, ehr-system-integration, trial-database-management, patient-trial-enrollment-acceleration, biomarker-and-mutation-identification, trial-eligibility-criteria-parsing, patient-cohort-analysis, treatment-outcome-tracking

Triomics

ProductPaid

AI-driven oncology platform streamlines trial matching, improves...

Best for:Oncology practices, cancer centers, and clinical research organizations seeking to accelerate clinical trial enrollment and improve patient treatment matching efficiency.

/ 100

10 capabilities

Capabilities10 decomposed

clinical-trial-eligibility-matching

Medium confidence

Automatically matches patient medical records against clinical trial inclusion/exclusion criteria to identify eligible trials. Parses complex eligibility requirements and patient data to surface appropriate trial opportunities without manual screening.

Solves for

Find clinical trials my patient qualifies for without manually reviewing each trial's eligibility criteriaQuickly identify which of our patients are candidates for specific ongoing trialsReduce 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

Requires

Structured patient medical records in EHR system

Integration with hospital EHR infrastructure

Access to comprehensive clinical trial protocol database

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

medical-record-parsing-and-extraction

Medium confidence

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.

Solves for

Automatically extract key clinical data from patient charts without manual data entryStandardize patient information across different EHR formats and documentation stylesIdentify 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

Requires

Access to patient medical records

EHR system integration

Sufficient clinical documentation in patient charts

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

personalized-treatment-recommendation

Medium confidence

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.

Solves for

Get evidence-based treatment recommendations tailored to my patient's unique profileDiscover cutting-edge treatment options beyond standard-of-care therapiesPresent 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

Requires

Comprehensive patient clinical profile

Biomarker and genetic testing results

Access to treatment protocol and trial database

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

ehr-system-integration

Medium confidence

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.

Solves for

Access patient data directly from our EHR without manual export stepsHave trial matching results appear in our existing clinical workflowsReduce 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

Requires

Compatible EHR system (Epic, Cerner, or other major platform)

API access or HL7 integration capabilities

IT infrastructure support for integration setup

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

trial-database-management

Medium confidence

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.

Solves for

Ensure we're matching patients against current, active clinical trialsAccess detailed trial protocol information for patient counselingKnow 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

Requires

Access to clinical trial registries (ClinicalTrials.gov, etc.)

Automated data ingestion and update mechanisms

Database infrastructure for storing and indexing trial protocols

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

patient-trial-enrollment-acceleration

Medium confidence

Streamlines the patient enrollment process by reducing time spent on manual eligibility screening and documentation. Automates identification of eligible patients and prepares enrollment documentation.

Solves for

Speed up the time from trial identification to patient enrollmentReduce administrative burden on research coordinatorsIncrease 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

Requires

Automated trial matching capability

Integration with enrollment workflow systems

Patient communication infrastructure

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

biomarker-and-mutation-identification

Medium confidence

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.

Solves for

Automatically identify which biomarkers my patient has tested forFind trials that match my patient's specific genetic mutationsEnsure 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

Requires

Genomic test results in patient records

Access to biomarker/mutation databases

Standardized mutation nomenclature (HGVS, etc.)

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

trial-eligibility-criteria-parsing

Medium confidence

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.

Solves for

Automatically understand trial eligibility requirements without manual reviewStandardize how eligibility criteria are interpreted across different trialsEnable 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

Requires

Trial protocol documents in accessible format

NLP capability to parse clinical language

Database to store structured eligibility criteria

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')

patient-cohort-analysis

Medium confidence

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.

Solves for

Identify all patients in our system who might be eligible for a specific trialUnderstand what percentage of our patient population could benefit from certain treatmentsPrioritize 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

Requires

Access to patient database with sufficient clinical detail

Ability to run bulk queries against EHR

Data governance and privacy compliance infrastructure

Limitations

Requires access to large patient databases

Results depend on data quality across many patient records

Privacy and security considerations for bulk patient analysis

treatment-outcome-tracking

Medium confidence

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.

Solves for

Monitor how well our patients are doing on recommended treatmentsUnderstand which trial recommendations lead to successful outcomesUse 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

Requires

Longitudinal patient follow-up data in EHR

Outcome measurement infrastructure

Data linkage to trial registries or external outcome databases

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

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓Oncologists and cancer center staff
✓Clinical research coordinators
✓Cancer centers with high patient volume
✓Large cancer centers with high volume of unstructured clinical notes
✓Healthcare systems with multiple EHR platforms
✓Research organizations processing many patient records
✓Oncologists seeking treatment options beyond standard protocols
✓Patients with rare cancer subtypes or complex presentations

Known 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
⚠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

Requirements

Structured patient medical records in EHR systemIntegration with hospital EHR infrastructureAccess to comprehensive clinical trial protocol databaseAccess to patient medical recordsEHR system integrationSufficient clinical documentation in patient chartsComprehensive patient clinical profileBiomarker and genetic testing results

Input / Output

Accepts: EHR patient records (structured and unstructured text), Clinical trial protocol documents, Unstructured clinical notes (text), Lab results (structured and unstructured), Pathology reports, Imaging reports, Patient medical records, Genomic/biomarker test results, Cancer diagnosis and staging information, Prior treatment history, EHR API connections, HL7 messages, Patient identifiers and data requests, Clinical trial registry data, Trial protocol documents, Enrollment status updates, Trial eligibility criteria, Enrollment status and requirements, Genomic test reports, Pathology reports with molecular findings, Lab results with biomarker values, Clinical trial protocol documents (PDF, text), Eligibility criteria sections from trial registries, Patient population data from EHR, Demographic and clinical filters, Patient follow-up clinical data, Trial outcome reports, Survival and progression data

Produces: Ranked list of matching trials, Eligibility match scores, Specific criteria met/unmet for each trial, Structured patient data (JSON/database format), Extracted biomarkers and mutations, Standardized clinical variables, Ranked list of treatment recommendations, Clinical trial suggestions with match scores, Evidence summaries for each recommendation, Links to relevant clinical trials, Patient data from EHR, Trial matching results back to EHR, Clinical decision support alerts, Searchable trial database, Trial protocol summaries, Eligibility criteria in structured format, Trial status and enrollment information, Pre-screened eligible patient lists, Enrollment readiness assessments, Patient notification/outreach lists, Structured biomarker data, Identified mutations with clinical significance, Biomarker-matched trial recommendations, Structured eligibility criteria (JSON/database format), Inclusion/exclusion rules in machine-readable format, Flagged ambiguous or subjective criteria requiring manual review, Patient cohort lists matching trial criteria, Cohort size and demographic summaries, Trial enrollment potential assessments, Treatment outcome summaries, Effectiveness metrics by treatment type, Feedback for recommendation algorithm improvement

UnfragileRank

Adoption15%(30% weight)

Quality48%(25% weight)

Ecosystem15%(15% weight)

Match Graph10%(25% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Product

10 capabilities

Visit Triomics→

About

AI-driven oncology platform streamlines trial matching, improves care

Unfragile Review

Triomics leverages AI to match cancer patients with appropriate clinical trials and personalized treatment options, addressing a critical pain point in oncology where trial eligibility screening remains manually intensive and time-consuming. The platform's ability to parse complex medical records and match them against trial protocols could significantly reduce patient enrollment timelines and improve treatment outcomes, though its impact remains dependent on healthcare system adoption and data quality.

Pros

+Automates tedious trial matching process that typically requires manual review of eligibility criteria against patient records, saving oncologists substantial administrative time
+Helps surface clinical trial opportunities patients might otherwise miss, potentially expanding access to cutting-edge treatments beyond standard care options
+Integrates with existing EHR systems, reducing friction for hospital and cancer center implementation compared to standalone platforms

Cons

-Effectiveness heavily dependent on quality and completeness of patient data in hospital systems; garbage-in-garbage-out limitations apply to AI matching algorithms
-Paid model may limit adoption in resource-constrained healthcare settings, potentially creating disparities in trial access between well-funded and underfunded institutions

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Capabilities10 decomposed

clinical-trial-eligibility-matching

Medium confidence

Solves for

Best for

Oncologists and cancer center staff

Clinical research coordinators

Cancer centers with high patient volume

Requires

Structured patient medical records in EHR system

Integration with hospital EHR infrastructure

Access to comprehensive clinical trial protocol database

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

medical-record-parsing-and-extraction

Medium confidence

Solves for

Best for

Large cancer centers with high volume of unstructured clinical notes

Healthcare systems with multiple EHR platforms

Research organizations processing many patient records

Requires

Access to patient medical records

EHR system integration

Sufficient clinical documentation in patient charts

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

personalized-treatment-recommendation

Medium confidence

Solves for

Best for

Oncologists seeking treatment options beyond standard protocols

Patients with rare cancer subtypes or complex presentations

Cancer centers offering precision medicine programs

Requires

Comprehensive patient clinical profile

Biomarker and genetic testing results

Access to treatment protocol and trial database

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

ehr-system-integration

Medium confidence

Solves for

Access patient data directly from our EHR without manual export stepsHave trial matching results appear in our existing clinical workflowsReduce 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

Requires

Compatible EHR system (Epic, Cerner, or other major platform)

API access or HL7 integration capabilities

IT infrastructure support for integration setup

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

trial-database-management

Medium confidence

Solves for

Ensure we're matching patients against current, active clinical trialsAccess detailed trial protocol information for patient counselingKnow 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

Requires

Access to clinical trial registries (ClinicalTrials.gov, etc.)

Automated data ingestion and update mechanisms

Database infrastructure for storing and indexing trial protocols

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

patient-trial-enrollment-acceleration

Medium confidence

Solves for

Speed up the time from trial identification to patient enrollmentReduce administrative burden on research coordinatorsIncrease 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

Requires

Automated trial matching capability

Integration with enrollment workflow systems

Patient communication infrastructure

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

biomarker-and-mutation-identification

Medium confidence

Solves for

Automatically identify which biomarkers my patient has tested forFind trials that match my patient's specific genetic mutationsEnsure 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

Requires

Genomic test results in patient records

Access to biomarker/mutation databases

Standardized mutation nomenclature (HGVS, etc.)

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

trial-eligibility-criteria-parsing

Medium confidence

Solves for

Best for

Research organizations managing many trials with varying protocols

Cancer centers needing to quickly onboard new trials

Healthcare systems seeking standardized trial evaluation

Requires

Trial protocol documents in accessible format

NLP capability to parse clinical language

Database to store structured eligibility criteria

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')

patient-cohort-analysis

Medium confidence

Solves for

Best for

Large cancer centers with diverse patient populations

Research organizations managing multiple trials

Healthcare systems conducting population health analysis

Requires

Access to patient database with sufficient clinical detail

Ability to run bulk queries against EHR

Data governance and privacy compliance infrastructure

Limitations

Requires access to large patient databases

Results depend on data quality across many patient records

Privacy and security considerations for bulk patient analysis

treatment-outcome-tracking

Medium confidence

Solves for

Monitor how well our patients are doing on recommended treatmentsUnderstand which trial recommendations lead to successful outcomesUse 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

Requires

Longitudinal patient follow-up data in EHR

Outcome measurement infrastructure

Data linkage to trial registries or external outcome databases

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

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Unfragile Review

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Triomics

Capabilities10 decomposed

clinical-trial-eligibility-matching

medical-record-parsing-and-extraction

personalized-treatment-recommendation

ehr-system-integration

trial-database-management

patient-trial-enrollment-acceleration

biomarker-and-mutation-identification

trial-eligibility-criteria-parsing

patient-cohort-analysis

treatment-outcome-tracking

Related Artifactssharing capabilities

Zephyr AI

Tempus

Quanthealth

Qureight

Sully AI

JADBio

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to Triomics

Are you the builder of Triomics?

Get the weekly brief

Data Sources

Triomics

Capabilities10 decomposed

clinical-trial-eligibility-matching

medical-record-parsing-and-extraction

personalized-treatment-recommendation

ehr-system-integration

trial-database-management

patient-trial-enrollment-acceleration

biomarker-and-mutation-identification

trial-eligibility-criteria-parsing

patient-cohort-analysis

treatment-outcome-tracking

Related Artifactssharing capabilities

Zephyr AI

Tempus

Quanthealth

Qureight

Sully AI

JADBio

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to Triomics

Are you the builder of Triomics?

Get the weekly brief

Data Sources