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Solutions · Biostatisticians & Statistical Programmers

Spend your time on the analysis, not the boilerplate.

You trained for the statistical thinking — not for hand-cranking the 100th TLF shell or chasing a Define.xml finding at the last minute. Health1st AI generates CDISC SDTM and ADaM datasets, Define.xml, and TLFs that validate clean in Pinnacle 21, so your effort lands where your judgment actually matters.

What you own

Your goals

  • Produce SDTM/ADaM datasets that pass Pinnacle 21 with no errors
  • Automate standard TLF outputs and free time for custom analyses
  • Maintain full source-to-submission traceability
  • Shorten the path from database lock to submission package

We know how this feels

The pains we remove

You spend your best hours on repetitive programming that any conformance check will grade pass/fail — while the analysis you're actually good at waits its turn.

  • Dataset creation and Define.xml are repetitive, unforgiving work where one slip means rework
  • The sheer volume of standard TLF programming leaves no room for the analyses you'd rather be doing
  • Conformance findings surface late — after you thought you were done — and force you back through it
  • Keeping traceability documentation current is tedious, and it's always the thing that's behind

And the part no one says out loud:

  • A Pinnacle 21 finding — or worse, a wrong number in a TLF — surfacing after database lock, with the submission clock already running
  • A derivation you can't cleanly trace to source when a reviewer asks, and the scramble to reconstruct it under deadline

Where the agents fit

Every pain, matched to the agent that removes it

The grind you just read isn't abstract — each piece of it already has an agent assigned. Here's which one picks up which fight, so you can see exactly where the help lands before you commit to anything.

Your agentic workforce

The agents that help

These feature agents map most directly to biostatisticians & statistical programmers. Enable the ones you need, per study and per deliverable, and expand over time.

Outcomes

What good looks like

Quantified targets we build toward with each engagement.

P21-clean
Datasets pass with no errors
100+
Standard TLF shells automated
Source→sub
Full traceability maintained

Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes.

Every output is grounded, reviewed, and inspection-ready

The standard datasets and shells come out P21-clean, so the finding that used to bite us after lock just isn't there. I finally spend my week on the study-specific analysis instead of the boilerplate everyone assumes is easy.
Lead Statistical Programmer, CRO

Illustrative — representative of the workflows we build toward during our pre-launch pilot.

No pressure, straight answers

What you're probably wondering

The doubts that usually surface before a team like yours says yes — answered plainly, including the places where the honest answer is a limit rather than a promise.

Let's see it on your study

See it run on one of your own studies — no pressure, no obligation, and honest answers, including on the limits. We will walk biostatisticians & statistical programmers through exactly what stays under your control.

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