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AI-Assisted Clinical Trial Site Selection
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AI
Healthcare & Life Sciences

AI-Assisted Clinical Trial Site Selection

Using analytics to help clinical teams shortlist trial countries and sites more quickly from available data.

Representative scenario based on the kinds of projects AWZ handles. Client names and proof are private. Targets are agreed and measured per project — published figures are not audited outcomes.

Representative client profile

Life-sciences client

The Challenge

Understanding the Problem

A pharmaceutical organization found manual site selection slow. Historical trial data and country-specific requirements were difficult to compare in spreadsheets.

Our Solution

How We Solved It

We built analytics and data pipelines that consolidated historical trial data, requirements, and demographic indicators so teams could rank candidate sites for review.

Evaluation Framework

What We Measure

These are the measures we would define and validate with the client. They are not published client results.

Project target

Site selection time

Analytics can shorten the review step; actual timelines depend on data quality and completeness.

Evaluation required

Site activation cost

Cost reduction is case-specific and validated against the client's own data; not a fixed percentage.

Depends on data quality

Site qualification

Scoring improves when input data is clean and validated with subject-matter experts.

Client-owned

Regulatory context

Regulatory compliance is the client's responsibility; our role covers data tooling, not approval.

Tech Stack

Technologies Used

Data analytics
Python
Data pipelines
Regression / scoring models

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