AI-Powered Clinical Trial Software Configuration Automation

clario · Healthcare · 2025 · source Agentic: no · promotional bias: high · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Clinical-trial software configuration

Clario needed to reduce manual extraction, transcription errors, version-control problems, and fragmented information flow when configuring clinical-trial software from PDFs, study databases, protocols, and configuration parameters.

Constraints:

Architecture

Clario Genie parses PDF transmittal forms with Claude on Bedrock, centralizes study data, presents an interactive validation dashboard, and generates both Software Configuration Specification documents and XML configuration for medical imaging software with audit trails.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  pdf-transmittal-forms["PDF transmittal forms"]
  study-data-sources["Study data sources"]
  custom-data-parser["Custom data parser"]
  centralized-study-data-layer["Centralized study data layer"]
  interactive-review-dashboard["Interactive review dashboard"]
  scs-generator["SCS generator"]
  xml-configuration-generator["XML configuration generator"]
  audit-trail["Audit trail"]
  medical-imaging-software["Medical imaging software"]
  pdf-transmittal-forms --> custom-data-parser
  study-data-sources --> centralized-study-data-layer
  custom-data-parser --> centralized-study-data-layer
  centralized-study-data-layer --> interactive-review-dashboard
  interactive-review-dashboard --> scs-generator
  interactive-review-dashboard --> xml-configuration-generator
  scs-generator --> medical-imaging-software
  xml-configuration-generator --> medical-imaging-software
  audit-trail --> interactive-review-dashboard
Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component Role Interacts with
PDF transmittal forms Provide study-specific configuration information in document form. Custom data parser
Study data sources Include study databases, standard exam protocols, and configuration parameters. Centralized study data layer
Custom data parser Uses Bedrock and Claude to extract PDF data into validated tables. Centralized study data layer
Centralized study data layer Combines extracted forms and existing study details for review. Interactive review dashboard
Interactive review dashboard Lets stakeholders verify AI-extracted data and correct it before finalization. SCS generator, XML configuration generator
SCS generator Produces Software Configuration Specification documents. Medical imaging software
XML configuration generator Produces system configuration XML for Clario imaging software. Medical imaging software
Audit trail Records reproducible changes and compliance evidence. Interactive review dashboard
Medical imaging software Receives generated SCS and XML configuration outputs for Clario clinical-trial imaging workflows. SCS generator, XML configuration generator

Data flow

The service structures PDF transmittal forms into validated tables, combines them with study databases and protocols, then routes outputs through a dashboard into SCS and XML configuration artifacts.

“Clario’s approach uses a custom data parser using Amazon Bedrock to automatically structure information from PDF transmittal forms into validated tables.”article, stated
“An interactive review dashboard helps stakeholders verify AI-extracted information and make necessary corrections before finalizing the validated configuration.”article, stated

Why this architecture

The architecture needs detailed prompts, human validation, and auditability because clinical-trial configuration data is fragmented and errors are consequential.

3 supporting quotes
“Data existed in disconnected silos, including PDFs, study detail database records, and other standalone documents.”article, stated
“Few-shot prompting with domain knowledge is essential. The team discovered that providing detailed examples and explicit business rules in the prompts was necessary for success.”article, stated
“Importantly, the solution maintains strong compliance and auditability through complete audit trails and reproducible processes.”article, stated

Models used: Anthropic Claude 3.7 Sonnet

Alternatives considered (roads not taken)

Document-centric manual configuration transfer

Rejected because: Clario centralizes extracted data in a dashboard so teams validate once and generate downstream configuration artifacts consistently.

“The solution enhanced data extraction quality while providing teams with a streamlined dashboard that accelerates the validation process.”article, stated

Big-bang rollout to all clinical teams

Rejected because: The solution rolled out in stages with pilot teams serving as internal advocates.

“Clario rolled out the solution in stages, beginning with pilot teams who could validate functionality and serve as internal advocates”article, stated

Simple prompting without domain business rules

Rejected because: Few-shot prompting with detailed examples and explicit clinical-trial business rules was necessary.

“Few-shot prompting with domain knowledge is essential. The team discovered that providing detailed examples and explicit business rules in the prompts was necessary for success.”article, stated

AI extraction without a structured validation dashboard

Rejected because: Human review remains necessary, so stakeholders verify and correct extracted information before finalizing configuration.

“Although generative AI dramatically accelerates extraction, human review remains necessary within a structured validation workflow.”article, stated
“An interactive review dashboard helps stakeholders verify AI-extracted information and make necessary corrections before finalizing the validated configuration.”article, stated

Patterns

structured-output-contract (model-ops)

The service turns PDFs and study records into validated tables, SCS documents, and XML configuration outputs.

“Clario’s approach uses a custom data parser using Amazon Bedrock to automatically structure information from PDF transmittal forms into validated tables.”article, stated

human-in-the-loop-gate (safety)

Reviewers verify AI-extracted data, correct inconsistencies, and finalize validated configuration before generation proceeds.

“An interactive review dashboard helps stakeholders verify AI-extracted information and make necessary corrections before finalizing the validated configuration.”article, stated

prompt-versioning (model-ops)

Clario encodes examples, business logic, edge cases, and output requirements in prompts so extraction follows clinical-trial configuration rules.

“Rather than simple instructions, Clario’s prompts include comprehensive business logic, edge case handling, and exact output formatting requirements to guide the AI’s understanding of clinical trial configurations.”article, stated

canary-rollout (operations)

Clario introduced the workflow in stages with pilot teams who could validate functionality and advocate for process transition.

“Clario rolled out the solution in stages, beginning with pilot teams who could validate functionality and serve as internal advocates to help teams transition from familiar document-centric workflows to the new solution.”article, stated

safety-policy-enforcement (safety)

Audit trails, reproducible processes, and validation safeguards keep AI-generated configuration changes reviewable.

“Importantly, the solution maintains strong compliance and auditability through complete audit trails and reproducible processes.”article, stated

Tradeoffs

How to reduce configuration effort without losing expert control.

Chose AI extraction with interactive stakeholder validation over fully manual document transfer or fully automatic finalization.

Rationale: The workflow reduces manual entry and errors while preserving expert correction before validated configuration.

“The solution enhanced data extraction quality while providing teams with a streamlined dashboard that accelerates the validation process.”article, stated

How to change a familiar document-centric workflow.

Chose phased implementation with pilot teams over immediate organization-wide replacement.

Rationale: Pilot teams could validate functionality and help other teams transition to the new process.

“Clario rolled out the solution in stages, beginning with pilot teams who could validate functionality and serve as internal advocates”article, stated

Gotchas (what bit them)

Clinical-trial configuration data is fragmented and error-prone when handled manually.

Root cause: Teams pulled data from PDFs, study databases, protocols, and standalone documents into configuration artifacts.

Resolution: Centralize extraction and validation through Genie before generating SCS and XML artifacts.

“Data existed in disconnected silos, including PDFs, study detail database records, and other standalone documents.”article, stated

Simple prompts were insufficient for clinical-trial configuration extraction.

Root cause: The model had to understand domain rules, visit sequencing, demographic requirements, edge cases, and exact formats.

Resolution: Use few-shot prompts with domain knowledge, explicit business rules, and iterative refinement.

“Few-shot prompting with domain knowledge is essential. The team discovered that providing detailed examples and explicit business rules in the prompts was necessary for success.”article, stated

AI extraction still needed structured human validation.

Root cause: Regulated clinical-trial configuration cannot rely on generated data without expert review.

Resolution: Design the interface to highlight inconsistencies and let reviewers efficiently apply expertise.

“Although generative AI dramatically accelerates extraction, human review remains necessary within a structured validation workflow.”article, stated

SCS and system configuration can drift apart.

Root cause: Changes made in SCS documents must be reflected bidirectionally in the software solution.

Resolution: Continue refining two-system synchronization checks as part of the integrated workflow.

“One of the biggest challenges has been verifying that changes made in the SCS documents are reflected in the solution. This bidirectional integration is still being refined.”article, stated

Evaluation

Clario evaluated Genie through prompt iteration, testing different extraction approaches, stakeholder validation, built-in safeguards, audit trails, and observed operational outcomes.

Metrics: study configuration execution time reduced · overall quality improved · transcription errors reduced · teams can focus more on study design optimization

“Study configuration execution time has been reduced while improving overall quality * Teams can focus more on value-added activities like study design optimization.”article, stated
“By implementing consistent extraction logic and minimizing manual data entry, the solution has reduced potential transcription errors.”article, stated

Outcomes

AWS co-authored Clario post; it reports production workflow benefits but no public percentage for time reduction.

“By providing centralized review capabilities and giving cross-functional teams access to the same solution, communication has become more transparent and efficient.”article, stated
“This standardization has laid a strong foundation for expanding these capabilities to other operational areas within the organization.”article, stated