Chief Clinical Informatics Officer
Support clinical research data requests
What You Do Today
Extract de-identified datasets for clinical researchers, ensuring HIPAA compliance, proper IRB documentation, and data quality. Translate research questions into queryable data structures.
AI That Applies
AI auto-identifies and de-identifies PHI with high accuracy, suggests relevant data elements researchers may not have considered, and validates cohort definitions against clinical ontologies.
Technologies
How It Works
The system ingests clinical data — patient records, lab results, vitals, and care history from the EHR. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
De-identification and cohort building accelerate dramatically. Researchers get data faster and with fewer iterations.
What Stays
Ensuring data extracts actually answer the research question — and that the researcher understands the limitations — requires your clinical and informatics expertise.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for support clinical research data requests, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long support clinical research data requests takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What data do we already have that could improve how we handle support clinical research data requests?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with support clinical research data requests, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for support clinical research data requests, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.