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Medical Science Liaison

Present clinical trial data to investigators

Enhances◐ 1–3 years

What You Do Today

Walk site investigators through study design, efficacy results, safety data — handle Q&A, address clinical concerns about the compound

AI That Applies

AI generates customized presentation decks from clinical data, tailored to the audience's specialty and interests; real-time data visualization

Technologies

Veeva Vault MedCommsGenerative AIPrezi AI

How It Works

The system ingests clinical data — patient records, lab results, vitals, and care history from the EHR. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output — customized presentation decks from clinical data — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Presentation preparation is faster; AI tailors data emphasis to each investigator's subspecialty and prior questions

What Stays

You deliver the presentation, handle questions with scientific depth, and build credibility through your clinical 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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for present clinical trial data to investigators, understand your current state.

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Map your current process: Document how present clinical trial data to investigators works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: You deliver the presentation, handle questions with scientific depth, and build credibility through your clinical expertise. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Veeva Vault MedComms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long present clinical trial data to investigators 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.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

“What data do we already have that could improve how we handle present clinical trial data to investigators?”

They're prioritizing which operational processes to automate

your process improvement or lean lead

“Who on our team has the deepest experience with present clinical trial data to investigators, and what tools are they already using?”

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

“If we brought in AI tools for present clinical trial data to investigators, what would we measure before and after to know it actually helped?”

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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