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Enterprise AI Analysis: A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare

Enterprise AI Analysis

A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare

Health systems face the challenge of balancing innovation and safety to responsibly implement artificial intelligence (AI) solutions. The rapid proliferation, growing complexity, ethical considerations, and rising demand for these tools require timely and efficient processes for rigorous evaluation and ongoing monitoring. Current AI evaluation frameworks often lack the practical guidance for health systems to address these challenges. To fill this gap, we developed a prescriptive evaluation framework informed by a literature review, in-depth interviews with key stakeholders, including patients, and a multidisciplinary design workshop. The resulting framework provides health systems an outline of the resources, structures, criteria, and template documents to enable pre-implementation evaluation and post-implementation monitoring of AI solutions. Health systems will need to treat this or any alternative framework as a living document to maintain relevance and effectiveness as the AI landscape and regulations continue to evolve.

Executive Impact

Our analysis of the FAIR-AI framework reveals its potential to streamline AI adoption, reduce risks, and enhance patient safety within large healthcare organizations. Key metrics demonstrate its significant organizational benefits.

0 AI Solutions Reviewed Annually
0 Risk Reduction Potential
0 Implementation Efficiency Gain

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

FAIR-AI Evaluation Process

1. Triage | Comprehensive Review
2. Categorize Risk (Low | Moderate | High)
3. Safe AI Plan (Monitoring & Transparency)

AI Framework Comparison: FAIR-AI vs. Others

Feature FAIR-AI Existing Frameworks (General)
Stakeholder Engagement
  • ✓ Patient, Provider, Leaders, Developers
  • ✓ Often limited to technical experts
Practical Guidance
  • ✓ Prescriptive steps, templates, criteria
  • ✓ Often theoretical, lacks actionable steps
Risk Categorization
  • ✓ Qualitative, context-dependent (Low, Moderate, High)
  • ✓ Varying, sometimes purely quantitative
Post-Implementation
  • ✓ Continuous monitoring, AI Label, End-user education
  • ✓ Limited, often one-time review

Transparency Requirement

100% AI solutions categorized as high-risk require an AI Label and end-user education for transparency.

Patient Consent in High-Stakes AI

The framework emphasizes the ethical imperative to notify patients when AI is being used, and to obtain their consent in sensitive or high-stakes situations. This avoids undermining patient autonomy and eroding trust in the healthcare system. For example, in an AI-assisted diagnostic scenario for a critical condition, explicit patient consent would be sought.

Low-Risk Triage Success

50% Approximately 50% of AI solutions reviewed were triaged as low-risk, streamlining the evaluation process.

Advanced ROI Calculator

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Estimated Annual Savings $0
Annual Hours Reclaimed 0

Implementation Roadmap

A phased approach to integrate A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare into your operations, ensuring smooth adoption and maximized benefits.

Phase 1: Framework Adoption & Training

Establish FAIR-AI within your organization, secure leadership endorsement, and train key personnel on evaluation criteria and processes.

Phase 2: Initial AI Solution Triage & Review

Begin applying the low-risk screening questions. For solutions requiring in-depth review, assign data science and business owner teams.

Phase 3: AI Governance Committee Integration

Escalate high-risk AI solutions to the multidisciplinary AI Governance Committee for comprehensive assessment and decision-making.

Phase 4: Safe AI Plan & Monitoring Implementation

Develop and enact Safe AI Plans for approved solutions, including continuous monitoring, AI Labels, and end-user education strategies.

Phase 5: Iterative Refinement & Expansion

Regularly review and adapt the FAIR-AI framework to evolving AI landscape and regulations. Expand its application across diverse workflows.

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