Healthcare AI Failure Mode Research Volunteer-Clinical Workflows, Patient Safety & Payer Systems (Remote)

About the Initiative

The BRITE Institute is expanding a structured AI Safety and Risk Framework initiative focused on identifying, analyzing, and documenting AI failure modes in healthcare and other high-risk environments.

As artificial intelligence becomes more integrated into clinical workflows, diagnostics, patient monitoring, EMR systems, claims review, utilization management, care coordination, population health, and operational decision-making, there is an urgent need for contributors who understand how healthcare actually works in the real world.

This initiative is building a scalable AI safety research and intelligence framework designed to:

• Improve patient safety
• Strengthen AI deployment practices
• Identify clinical and operational risks
• Reduce preventable AI-related harm
• Support responsible adoption of AI in healthcare settings
• Help ensure AI tools reflect real-world patient care, payer systems, and healthcare operations

This is a unique opportunity to contribute to meaningful work at the intersection of:

• Healthcare
• Patient safety
• Clinical operations
• Health insurance and payer systems
• AI/ML
• Care delivery
• Risk management
• Emerging technology

The project operates through a standardized research workflow and collaborative system.

Volunteer Roles Available

We are seeking contributors from a wide range of medical, clinical, healthcare, and health insurance backgrounds, including:

• EMTs
• Paramedics
• Pre-med students
• Medical students
• Nursing students
• Nurses
• Nurse practitioners
• Physician assistants
• Early-career doctors
• Physicians
• Specialists
• Dentists
• Pharmacists
• Allied health professionals
• Clinical researchers
• Public health professionals
• Healthcare administrators
• Healthcare operations professionals
• Care coordinators
• Case managers
• Utilization management professionals
• Prior authorization specialists
• Claims reviewers
• Health insurance professionals
• Quality improvement professionals
• Patient safety professionals
• Population health professionals
• Revenue cycle or billing professionals
• Clinical documentation specialists
• EMR / EHR workflow users
• Healthcare compliance professionals

This is a fully remote and unpaid volunteer opportunity.

What You Will Do

Contributors may assist with:

• Identifying healthcare AI failure modes
• Evaluating how AI failures may impact patient care, clinical decision-making, claims, coverage, and healthcare operations
• Reviewing real-world healthcare use cases and deployment scenarios
• Assessing how AI tools may disrupt clinical workflows or payer processes
• Identifying unsafe deployment conditions and edge cases
• Analyzing risks related to clinician reliance, patient trust, and institutional adoption
• Evaluating how AI may affect care coordination, access, authorization, claims review, documentation, or follow-up
• Identifying risks in EMR/EHR integration and healthcare data workflows
• Supporting structured research documentation
• Contributing to mitigation strategy development
• Supporting publication-oriented research outputs
• Entering finalized findings into standardized project systems

Ideal Candidate Profile

We are looking for contributors who are:

• Detail-oriented
• Highly organized
• Reliable and responsive
• Able to follow directions and guidance
• Comfortable following structured workflows
• Able to work independently
• Adaptable to evolving research processes
• Comfortable reviewing healthcare-related scenarios
• Able to think critically about patient safety and operational risk
• Capable of producing high-quality work within standardized systems

Strong Candidates Are Comfortable With

• Healthcare operations
• Patient-care environments
• Clinical workflows
• Health insurance or payer workflows
• Claims, authorization, coverage, or utilization processes
• Research-intensive work
• Documentation accuracy
• Collaborative systems
• Iterative feedback
• Structured templates
• Shared research systems
• Maintaining consistency across standardized workflows

Prior AI experience is helpful but not required.

The strongest candidates will be able to apply their healthcare, clinical, operational, or payer experience to identify where AI systems may fail in real-world settings.

Why Medical, Clinical, Healthcare & Insurance Experience Matters

Healthcare AI does not fail only because of technical problems. It can also fail because it does not account for how care is actually delivered, documented, reviewed, approved, denied, escalated, or coordinated.

Contributors with clinical, healthcare operations, and health insurance experience can help identify risks that purely technical reviewers may miss, including:

• What happens during real patient encounters
• How clinicians interpret recommendations under pressure
• How documentation affects care and reimbursement
• How payer decisions affect patient access
• How delays, denials, or workflow breakdowns may create safety risks
• How AI tools may behave differently across hospitals, clinics, specialties, or patient populations
• How trust, usability, and institutional adoption influence real-world outcomes

This role is especially valuable for people who understand the practical realities of healthcare systems.

Important Notes

This initiative is fast-moving, systems-oriented, and research intensive.

Contributors should be comfortable with:

• Learning new workflows quickly
• Operating within standardized systems
• Receiving structured feedback
• Contributing consistently within collaborative research environments
• Meeting deadlines
• Using collaborative digital tools and remote workflows
• Maintaining professionalism and accuracy

Remote collaboration may include:

• Slack
• Zoom
• Google Meet
• Google Docs
• Shared spreadsheets
• Research templates
• Structured data entry systems

Because this work may contribute to future publications, policy frameworks, and advanced AI safety initiatives, the following are extremely important:

• Professionalism
• Reliability
• Operational awareness
• Confidentiality
• Attention to detail
• Clear communication
• Consistent follow-through

What You’ll Gain

Volunteers may gain:

• Exposure to emerging AI safety research
• Interdisciplinary healthcare and AI experience
• Publication-oriented collaboration
• Operational and systems-thinking experience
• Experience analyzing clinical, payer, and healthcare workflow risks
• Experience working alongside technical AI contributors and researchers
• Hands-on exposure to the rapidly growing AI/ML ecosystem impacting healthcare globally
• Opportunities to help shape safer and more clinically grounded AI systems

This is an opportunity to help bridge the gap between real-world healthcare experience and the future of AI-driven healthcare systems.

Additional Information

• Estimated commitment: approximately 8–15 hours per week
• Work may include structured research, documentation, and standardized data entry
• This is a merit-based and experience-based volunteer opportunity
• Applicants should be prepared to submit relevant work samples, research examples, writing samples, healthcare experience, clinical experience, payer experience, or related professional background
• Selected candidates may also be asked to complete a short skills-based assessment aligned with the responsibilities outlined in this position description

Remote • Volunteer • Unpaid • Flexible Hours