Healthcare

Healthcare: Genomic Patient Matching for Clinical Trials

4.35/5.00

4.4

D

4.3

U

4.5

B

4.2

E

The Problem

Oncology clinical trials have a 3.4% success rate, the lowest of any therapeutic area, with poor patient-drug matching driving many failures. At an NCI-designated comprehensive cancer center, manually reviewing genomic profiles against trial eligibility criteria took weeks per patient cohort. With median trial timelines stretching to 13 years and costs exceeding $1.5 billion per approved drug, the center needed an ML-powered system to automate genomic patient matching while maintaining FDA-compliant explainability.

Type

Cancer Research Center

Industry

Healthcare

Size

Enterprise

Region

Ohio, United States

Users

50+

The Analysis

The solution required an ML platform capable of ingesting and processing next-generation sequencing data, including whole exome sequencing and targeted gene panels, matching patient molecular profiles against clinical trial eligibility criteria. The system needed to integrate with Epic EMR infrastructure to pull patient demographics, treatment history, diagnosis codes, and lab results alongside genomic data from the institutional biobank. Explainability was non-negotiable, with every patient-trial match requiring documented reasoning that clinical researchers and IRB reviewers could audit for FDA compliance. The platform needed human-readable explanations detailing which genomic markers, prior treatments, and clinical factors contributed to eligibility. Real-time processing was essential, enabling oncologists to identify trial-eligible patients during treatment planning with results returned in under 10 seconds across 100,000+ patient records and 40+ active trials.

Compliance:FDA regulations and Common Rule for human subjects research
Integration:Epic EMR via FHIR APIs and institutional biobank data systems
Privacy:HIPAA required for all patient data; de-identification for research datasets
Processing:Sub-10-second query response for interactive cohort exploration
Scale:100,000+ patient records with genomic profiles across 40+ active trials
Explainability:Auditable reasoning for every patient-trial match decision

The Solution

Discovery

12 weeks

Development

32 weeks

Integration

32 weeks

Deployment

20 weeks

The Results

Key Outcomes

Patient-trial matching time2 days (-85%)
Genomic records processed100,000+
Active trials supported40+
Match accuracy87%
Query response time<8 seconds
Researcher adoption50+ users

Key Learnings

01

Epic EMR integration took 40% longer than estimated. Early data audits would have identified gaps sooner.

02

Explainability shaped the ML architecture. Building audit trails as a core feature enabled smoother IRB reviews.

03

Interactive cohort exploration was the most valued feature. Prioritize dashboard usability over batch workflows.

About DUBEScore™

DDelivery

On-time, on-budget execution. Measures project management quality, milestone adherence, and resource efficiency.

UUtility

Real-world usefulness. Evaluates how well the solution solves the stated problem and meets user needs.

BBusiness Impact

Measurable ROI and value creation. Tracks revenue impact, cost savings, and strategic outcomes.

EEndurance

Long-term sustainability. Assesses maintainability, scalability, and system resilience over time.

Scale: 1.0–5.05.0 = Exceptional4.0 = Strong3.0 = Meets expectations