Make eligibility review more traceable.
Explore trial operations assistance that organises protocol criteria and candidate information for an authorised research team. Preserve the distinction between a potential match and confirmed eligibility. Protocol interpretation, recruitment decisions and study procedures remain under the responsible team's governance.
Workflow scoping · Evaluation design · Human oversight · Integration planning
Capabilities
Review the capability scope against your workflow, data access and human oversight requirements.
Structured protocol criteria with source references
Candidate information linked to authorised records
Uncertain or missing evidence flagged for review
Reviewer decisions and protocol-version tracking
Workflows
Potential engagement scope, subject to feasibility, permissions and appropriate review.
Prepare evidence for manual eligibility screening
Organise protocol criteria for feasibility review
Track unresolved information in a screening workflow
Outcomes
Set measurable objectives with your team. Outcomes are evaluated, not assumed.
Measure screening effort without assuming recruitment outcomes
Make exclusion reasoning and missing evidence reviewable
Maintain a traceable review record as protocols change
Engineering
Use these questions to scope the evaluation, integration and operating model.
Evaluate criterion extraction, evidence matching, false exclusions and reviewer agreement on an appropriate test set.
Set a workflow-specific latency target and measure it under representative load. No performance guarantee is implied.
Approved research data sources, trial management systems and review worklists; access, consent and permitted use are assessed for the study.
Implementation decisions
Resolve the questions that change feasibility, effort and operational risk.
Describe the task, intended users, current systems and the point where a person must review the output. Share data types and constraints, not patient records or credentials. We can then define the discovery and evaluation work.
Evaluate criterion extraction, evidence matching, false exclusions and reviewer agreement on an appropriate test set.
The deliverable should include test cases, observed failure modes and a documented decision about whether to proceed.
Approved research data sources, trial management systems and review worklists; access, consent and permitted use are assessed for the study.
Confirm permissions, environment access, data ownership and failure handling before estimating the integration effort.
Agree a staged rollout, monitoring ownership, human escalation and rollback procedures. Clinical use, regulatory review and organisational approvals depend on the intended application; they are not implied by a working demonstration.
Discuss Clinical Trial Optimization AI with our team. We will work through the use case, dependencies and evidence needed for a meaningful next step.
