Use case 14 · Healthcare

Clinical trial matching

Compare unstructured patient records with complex trial criteria and prepare clinician-validated eligibility evidence.

Executive ownerHead of Clinical Research
Business valueBroader trial access with lower screening burden
Real-world evidence

Paradigm

Published by OpenAI

Using AI to improve patient access to clinical trials

The case describes large-language-model evaluation of medical records for clinical-trial data extraction and matching against expert-curated data.

  • Paradigm reports a 90% reduction in expert-clinician time required for model validation.
  • The company reports a 10% increase in blended precision and recall compared with prior specialized models.
Read the original source Results are reported by the named organization or publisher. They are evidence of feasibility, not a forecast or guarantee for another business.

The business problem

Orchestration redesigns the whole operating loop.

Trial matching requires coordinators to interpret complex inclusion and exclusion criteria against fragmented, often unstructured patient records. Manual screening limits scale and can restrict access to patients near research centers.

AI orchestration can extract relevant facts, compare criteria, identify missing evidence, and rank cases for review. Clinical teams confirm eligibility, contact patients, obtain consent, and make every care and enrollment decision.

Infographic 01

Orchestration corridor

How the work moves from signal to accountable outcome.

Every step has a defined input, intelligence task, action, and handoff. This is what separates an operating system from an isolated prompt.

Infographic 02

Ecosystem command map

The capability lives between systems, not inside one model.

The four quadrants show the assets that must be coordinated. Select any quadrant to emphasize its role in the operating system.

Infographic 03

Value and control instrument panel

Measure the business result and the integrity of the route.

The bars are not performance claims. They show the measurement sequence: establish the current baseline, agree a pilot threshold, and verify the observed result.

90-day implementation route

Move from reconnaissance to controlled scale.

Select a phase to inspect its executive decision gate.

Phase 1

Recon

Choose one active protocol with clear criteria and an expert-reviewed benchmark set.

Executive owner
Head of Clinical Research
Decision gate
Problem and baseline confirmed

Source and governance discipline

Evidence first. Claims qualified. Accountability designed in.

This operating play is an independent synthesis by Dr. Alejandro Canonero, DBA. It translates a documented implementation into a vendor-neutral business design and applies the War of the Ecosystems perspective. It does not imply endorsement by the source organization.

Deployment evidenceUsing AI to improve patient access to clinical trials NIST AI Risk Management FrameworkA voluntary framework for governing, mapping, measuring, and managing artificial intelligence risk.ISO/IEC 42001 artificial intelligence management systemsAn international management-system standard for responsible development and use of artificial intelligence.

Adjacent operating plays

Compare how orchestration changes across the ecosystem.

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 01 · Customer Customer service resolution

Resolve routine requests across knowledge, account systems, and escalation queues while preserving a clean human handoff.

 07 · Risk Internal audit planning

Synthesize risk signals, prior findings, control evidence, and business change into a reviewable audit plan.

Executive working session

Apply this pattern to your healthcare workflow.

A focused diagnosis will map the current work, quantify the opportunity, define human command, and shape the first controlled pilot.

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