Healthcare / Delivered project

Healthcare Research Assistant AI System

A source-controlled research environment for gathering, comparing, and synthesizing clinical, regulatory, and market evidence.

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Healthcare professional reviewing evidence in a secure AI-assisted environment — Healthcare · Data → Intelligence → ActionPhoto by National Cancer Institute · Unsplash License ↗

01 / Problem statementDelivered project · Healthcare

The operating problem

Research teams navigate large volumes of publications, trial records, guidance, and internal evidence while maintaining traceability and review quality.

Conditions the system must survive03 operating constraints

Scientific, regulatory, trial, and internal evidence have different authority

Search terminology and evidence quality vary by clinical question

Generated synthesis requires expert review and complete citations

02 / How we approached it

From operating uncertainty to testable evidence.

The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.

01Frame

Connect approved scientific and internal sources

Evidence produced

Question, source-authority, and inclusion policy

02Evaluate

Assign retrieval and comparison tasks to narrow agents

Evidence produced

Retrieval benchmark across scientific and regulatory tasks

03Engineer

Preserve citations and evidence boundaries

Evidence produced

Citation and synthesis review with domain experts

04Operationalize

Route synthesis through expert review

Evidence produced

Private research workspace with evidence and approval states

03 / Deployment record

Deployed around the workflow—not beside it.

The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.

Implementation statusDelivered project

Topology

Private cloud research workspace with permission-aware retrieval, narrow agents, and an evidence graph.

Integration

Approved literature, trial, regulatory, and internal repositories contribute source-controlled evidence.

Operation

Agents perform observable retrieval and comparison tasks; experts approve synthesis and source freshness is monitored.

04 / Evaluation metrics

What must be measured before the system earns trust.

Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.

01Evaluation gate

Retrieval precision and recall

How it is measured

Relevant included evidence across representative research questions.

What it decides

Sets search and reranking policy.

02Evaluation gate

Citation correctness

How it is measured

Claims accurately supported by source passages and metadata.

What it decides

Determines whether synthesis is review-ready.

03Evaluation gate

Evidence coverage

How it is measured

Required source types, populations, outcomes, and viewpoints represented.

What it decides

Reveals gaps before expert conclusion.

04Evaluation gate

Expert correction effort

How it is measured

Edits, rejected claims, and time to approved synthesis.

What it decides

Measures whether the system accelerates rigorous research.

05 / Customer perspective

Value has to appear in the customer’s operating day.

What matters in practice

The assistant should expose inclusion logic, source passages, conflicts, and evidence gaps—not present a polished answer without its basis.

01Observable value signalFaster evidence collection

02Observable value signalClearer provenance for summaries

03Observable value signalRepeatable research workflows

04Observable value signalExpert ownership of conclusions

06 / Technology context

Tools follow the system—not the other way around.

Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.

Multi-agent systemsRAGBiomedical searchKnowledge graphsPrivate cloud

Test the operating assumption

Define the evidence required to move from possibility to production.

Discuss this use case ↗

Healthcare Research Assistant AI System

Research teams navigate large volumes of publications, trial records, guidance, and internal evidence while maintaining traceability and review quality.

From operating uncertainty to testable evidence.

Connect approved scientific and internal sources

Assign retrieval and comparison tasks to narrow agents

Preserve citations and evidence boundaries

Route synthesis through expert review

Deployed around the workflow—not beside it.

What must be measured before the system earns trust.

Retrieval precision and recall

Citation correctness

Evidence coverage

Expert correction effort

Value has to appear in the customer’s operating day.

Tools follow the system—not the other way around.

Related operational systems.

Private document intelligence for clinical operations

Voice AI for Healthcare Hiring

Define the evidence required to move from possibility to production.