Resources from NCURA FRA and PRA Webinar | June 10, 2025
Thank you for joining our session on AI & Automation in Research Contracting. This resource page provides access to all materials referenced in our presentation, along with additional resources to help you implement AI and automation in your research contracting workflows.
The field of AI is moving quickly, and we believe research administrators should not be left behind. This is an opportunity to learn, enrich, and improve processes for yourself and others to accelerate research and ultimately bring cures to patients faster.
We hope you enjoy this collection of resources and find them valuable in your journey toward implementing AI and automation in your organization.
Co-founder and CEO, The Contract Network
Jim Wagner is co-founder and CEO of The Contract Network, an AI platform designed to accelerate contracting timelines for clinical trials and research-related agreements. Previously, Jim has built multiple successful AI-driven businesses, most recently serving as head of AI and agreement cloud strategy for DocuSign, which he joined when they acquired Seal Software (where Jim served as President) in May of 2020. Jim is co-inventor for multiple patents related to the field of legal analytics and automation and is a member of the advisory board of the Duke Law Tech Lab.
Administrator – Research Administration, Mayo Clinic; Instructor in Health Care Administration, Mayo Clinic College of Medicine
Tara Rabe is a seasoned healthcare administrator and innovation leader at Mayo Clinic, where she serves as Administrator for Legal Contract Administration within the Research Shield. With over two decades of experience in contract negotiation and administration, Tara has consistently driven transformation through cutting-edge technology and operational strategies. As co-lead of automation efforts within the Research Shield, she has been instrumental in implementing AI-powered platforms, including a transformative contract negotiation tool that has enhanced efficiency and service delivery across her business unit. She is also an early adopter of Robotic Process Automation, spearheading initiatives that have delivered measurable cost savings and streamlined complex workflows. A frequent speaker at regional and national conferences, Tara brings a unique perspective at the intersection of healthcare, law, and technology.
Understand the role of AI in modernizing clinical trial contract management.
Identify challenges and ethical considerations in implementing AI solutions.
Examine case studies of AI applications in contract negotiations and compliance.
Learn strategies for integrating AI tools to enhance contract workflow efficiency.
A Guide for Research Agreement Professionals
A prompt is the input you provide to an AI system to get a specific outcome.
Think of it as giving instructions to a detail-oriented assistant who takes your words literally.
The quality of your prompt directly determines the usefulness of the AI's response.
Accelerates review of complex agreement language.
Helps identify problematic clauses and suggests alternatives.
Streamlines comparison between template and proposed terms.
Supports consistent application of institutional policies.
"Review this CTA."
"Review the indemnification and subject injury sections in this CTA from [Sponsor]. Compare them to our institutional template and identify key differences. Flag any terms that conflict with our policy of requiring full sponsor indemnification for protocol-driven injuries."
"Fix the confidentiality clause."
"Our institution requires a 5-year confidentiality term limit, but this CDA proposes perpetual confidentiality. Draft alternative language for Section 4.2 that: 1) Limits confidentiality to 5 years post-disclosure; 2) Preserves the exceptions for public domain information; 3) Maintains our right to publish research results after sponsor review."
"Which of these agreements is better?"
"Compare these three Data Use Agreements from different pharmaceutical sponsors focusing on: 1) Data security requirements and technical standards; 2) Publication rights and review periods; 3) Restrictions on data combination or integration; 4) Term and termination provisions. Create a comparison table highlighting key differences and note which agreement terms are most favorable for our research team's needs to combine this data with existing datasets."
"What's wrong with the IP section?"
"Analyze the intellectual property section (clauses 8.1-8.5) in this research collaboration agreement. Our university policy requires: 1) Ownership of inventions made solely by university personnel; 2) Joint ownership of jointly-created inventions; 3) No automatic licenses to background IP. Identify any terms that conflict with these requirements and suggest alternative language that might be acceptable to both parties."
Break down agreement analysis into specific clause reviews.
Ask for a complete agreement analysis in a single step.
"First, analyze the publication rights in this CTA. Then, review the subject injury provisions. Finally, examine the intellectual property terms. For each section, compare against our standard language and flag significant deviations."
"This is a multi-site clinical trial agreement where we are one of 20 participating institutions. The sponsor has already finalized agreements with 15 other sites."
"Identify all sections in this CTA that differ substantially from our template, focusing on subject injury, IP, and confidentiality."
"For each divergent section, explain the potential implications for our institution and whether it conflicts with our policies."
"Draft a response email highlighting the three most critical issues that need to be addressed, with suggested alternative language for each."
Clause Analysis: Identifying problematic terms and suggesting alternatives.
Redline Comparison: Summarizing key changes between versions.
Template Checking: Ensuring proposed agreements match institutional templates.
Language Simplification: Translating complex legal language into plain English.
Negotiation Preparation: Summarizing key issues and institutional positions.
Be Specific: Focus on particular clauses or issues.
Provide Context: Include institutional policies and priorities.
Request Alternatives: Ask for suggested language, not just problem identification.
Maintain Oversight: Always review AI suggestions before implementation.
Iterate as Needed: Refine prompts based on initial responses.
Real-world metrics and best practices
Staff reporting 1-3 hours of time savings per agreement within 4 months of adoption
New average days to signature (down from 90+ days industry average)
Subawards processed through automated workflows, repurposing 1 FTE to higher-level work
Clinical trial agreement negotiation is often the most cited cause for slowing study start-up. With an industry average of 90+ days to negotiate agreements, this creates a significant bottleneck that delays research and ultimately affects patient care.
Sites are under pressure to do more with fewer resources, making it challenging to keep up with the volume of agreements requiring review.
Manual reviews and repetitive redlines waste valuable time that could be spent on more complex negotiation issues.
Sites often work in silos, hindering collaboration and efficiency across different departments (IRB, budgets, contracts).
Begin with small-scale AI integrations focused on specific agreement types or clauses.
Provide comprehensive training for contract managers on AI-assisted workflows.
Consistently review AI outputs for accuracy, consistency, and alignment with institutional policies.
Create clear guidelines on appropriate AI usage in contracting processes.
Evaluate AI tool security and compliance with institutional IT policies.
Learn more: Key Considerations Before Negotiating Healthcare AI Vendor Contracts
"[We] sit down with men and women with this awful cancer every day, often after others have told them that it's incurable and is going to kill them. I have seen this disease rob people I love of decades of life. The point of this trial—and, really, everything [We] do—is to offer cures to people who would otherwise not get that chance. Because of you and your hard work in getting this trial open at "warp speed" (that's the term used by our patient I just talked to on the phone who is joining the trial tomorrow), these patients have a shot at living longer and better lives. From the bottom of our hearts, thank you again for giving our patients this chance to live. Thank you for putting the needs of our patients first. You may never get to meet them, but our patients love and thank you."
— Anonymous grateful PI and Research Subject at Mayo Clinic
Robotic Process Automation (RPA) and AI working together
Automation is applying technology, programs, robotics or processes appropriate to a task to achieve outcomes with minimal human intervention. It includes process automation, robotics and artificial intelligence.
Process automation is largely process-driven and focused on using technology to complete manual role tasks with varying levels of human intervention.
Example: EHR campaigns that identify eligible patient populations and send portal messages automatically.
Software that automates repetitive, rules-driven tasks, collects data from multiple sources, and can integrate with multiple systems.
Example: "Bot" transferring facilities request tickets for work order management.
Artificial Intelligence is largely data-driven and focused on analyzing and utilizing large data sets and providing insights and/or answers to replace human intervention or improve quality.
Example: Automating provider tasks like calculating total kidney volume.
RPA excels at rules-based, repetitive tasks while AI provides intelligent analysis and decision support. Together they create a powerful solution for contract management.
RPA can handle document intake and routing while AI performs content analysis, creating a seamless workflow from receipt to execution.
The combined approach allows for handling higher volumes of agreements without proportional increases in staff time.
AI can learn from past negotiations while RPA consistently executes processes, creating a cycle of ongoing optimization.
Document current workflows and identify repetitive, rules-based tasks suitable for automation.
Evaluate and select appropriate RPA and AI tools based on institutional needs and IT environment.
Start with a small-scale project to demonstrate value and identify improvement opportunities.
Ensure team members understand how to work alongside automated processes.
Expand successful automation to additional processes and continuously refine based on results.
Hands-on activities and practical AI applications
These practical exercises demonstrate how AI can instantly transform your research contracting workflows. Each exercise includes step-by-step instructions, sample prompts, and real examples you can try immediately.
Create a specialized AI agent for comprehensive NDA analysis. Build a GPT with personality that consistently analyzes agreements and provides structured recommendations.
Start Exercise →Transform messy policies into structured references. Convert dense, text-heavy guidelines into clearly organized, scannable tables that improve comprehension.
Start Exercise →Convert guidelines into engaging quizzes. Transform dry policies into interactive learning experiences that improve knowledge retention and training effectiveness.
Start Exercise →One-click resource generation with Google Docs Notebook. Transform complex protocols into timelines, mind maps, FAQs, and study guides automatically.
Start Exercise →CTA Moneyball is an approach that applies data analytics to clinical trial agreement negotiations, similar to how the Oakland Athletics revolutionized baseball recruitment using statistics. This approach identifies which contract terms are most likely to cause delays and which are most commonly accepted, allowing for more efficient negotiations.
The CTA Moneyball Study analyzes negotiation patterns across thousands of clinical trial agreements to identify market standards and negotiation efficiency opportunities.
Test your clinical trial agreement negotiation skills in this interactive game based on real negotiation data.
Experience the negotiation process from a site perspective and learn how data-driven approaches can improve outcomes.
Explore how different negotiation strategies impact time-to-signature and overall trial activation timelines.
These educational games were created using AI tools to help research administrators and contract professionals learn complex concepts in an engaging way.
Compare your diagnostic abilities against AI and medical professionals in this interactive game that demonstrates how AI and humans can work together effectively.
Test your clinical trial agreement negotiation skills in this data-driven simulation based on real negotiation patterns.
Take on the role of a site contract negotiator and navigate complex sponsor demands while maintaining institutional priorities.
Want to create similar educational experiences for your team? Here are some resources to get started:
Recent studies have examined how AI affects knowledge worker productivity and performance across various domains, including legal, medical, and administrative professions.
This landmark study compared diagnostic accuracy between physicians working alone, AI systems working alone, and physicians collaborating with AI.
The Contract Network is an AI-enabled platform focused on accelerating the negotiation of clinical trial and research agreements for the life sciences and research community. Built in collaboration with Mayo Clinic, TCN combines legal expertise with cutting-edge AI technology to streamline the contract negotiation process.
Our mission is to Change Research Contracting for Good.
Advanced AI tools compare agreements to institutional standards and identify deviations that require attention.
Learn from past negotiations to understand which terms are market standard and which are likely to cause delays.
Seamless collaboration between legal teams, research administrators, and sponsors to accelerate agreement finalization.
