Interactive Assessment Tools

Healthcare AI Evaluation Framework

Evidence-based assessment tools for healthcare professionals, researchers, and AI developers. All tools are grounded in peer-reviewed methodologies and international standards (FDA, WHO, ISO, AHRQ).

View Assessment Tools← Back to Hubs
⚠️

Important Disclaimer: Educational Use Only

These assessment tools are designed for educational and research purposes only. They provide general guidance and preliminary insights based on established academic frameworks and regulatory guidelines. These tools are NOT intended to replace professional medical advice, clinical judgment, formal regulatory compliance assessments, legal consultation, or official certification processes. Results should be interpreted by qualified professionals within the appropriate organizational and regulatory context. No warranty is provided regarding accuracy, completeness, or fitness for any particular purpose. Users assume full responsibility for any decisions made based on tool outputs.

Methodological Foundation

All assessment tools are built upon rigorous, peer-reviewed methodologies and internationally recognized standards. Our frameworks are continuously updated to reflect the latest regulatory guidance and academic research.

Regulatory Standards

  • FDA: Software as a Medical Device (SaMD) guidance, AI/ML Action Plan, Clinical Decision Support guidance
  • ISO: ISO 14971:2019 (Risk Management), ISO 13485 (Quality Management), IEC 62304 (Software Lifecycle)
  • WHO: Ethics and Governance of AI for Health, Global Patient Safety Action Plan 2021-2030

Privacy & Security Frameworks

  • HIPAA: Privacy Rule (45 CFR § 164.514), Safe Harbor method, Expert Determination requirements
  • GDPR: Article 89 (Research safeguards), Article 25 (Privacy by Design), Recital 26 (Anonymization)
  • Academic: k-anonymity (Sweeney, 2002), l-diversity (Machanavajjhala et al., 2007), t-closeness

AI Reporting Guidelines

  • TRIPOD-AI: Transparent Reporting of prediction models using AI
  • CONSORT-AI: Consolidated Standards of Reporting Trials for AI interventions
  • STARD-AI: Standards for Reporting Diagnostic accuracy studies using AI
  • SPIRIT-AI: Standard Protocol Items for clinical trials involving AI

Patient Safety Standards

  • AHRQ: Patient Safety Culture Survey, Patient Safety Indicators (PSIs)
  • Joint Commission: National Patient Safety Goals (NPSGs), Sentinel Event Policy
  • IHI: Framework for Safe, Reliable, and Effective Care
  • NHS: Patient Safety Incident Response Framework

Available Assessment Tools

Select a tool below to begin your assessment. Each tool provides detailed questionnaires, evidence-based scoring, and actionable recommendations.

AI Risk Assessment Tool

Comprehensive clinical AI system risk evaluation framework

Evaluate AI-based medical devices and clinical decision support systems using a multi-dimensional risk assessment framework. This tool implements FDA Software as a Medical Device (SaMD) guidelines, ISO 14971 risk management standards, Failure Mode and Effects Analysis (FMEA) methodology, and WHO AI ethics principles to provide systematic safety analysis.

Methodology

Based on FDA guidance for AI/ML-based SaMD, ISO 14971:2019 risk management framework, and FMEA scoring matrix (Severity × Occurrence × Detection). Includes clinical impact assessment, data quality evaluation, validation metrics, transparency scoring, and regulatory compliance review.

Frameworks

  • FDA SaMD Guidance
  • ISO 14971:2019
  • FMEA Methodology
  • WHO AI Ethics

Assessment Outputs

  • Risk Priority Number (RPN)
  • Multi-dimensional Risk Score
  • Category-specific Analysis
  • Actionable Recommendations

Target Users

Healthcare AI DevelopersClinical Safety OfficersRegulatory Affairs TeamsHealthcare Administrators
Launch Assessment Tool →

Data Anonymization Test

Healthcare data privacy protection and re-identification risk assessment

Assess the re-identification risk of anonymized healthcare datasets and evaluate compliance with HIPAA Privacy Rule and GDPR Article 89. This tool analyzes direct identifiers, quasi-identifiers, k-anonymity levels, l-diversity metrics, and external linkage risks to provide comprehensive privacy protection assessment.

Methodology

Implements HIPAA Safe Harbor method (18 identifiers), Expert Determination framework, k-anonymity privacy model (Sweeney, 2002), l-diversity principle (Machanavajjhala et al., 2007), and GDPR Article 89 safeguards. Scoring algorithm evaluates removal of direct identifiers, quasi-identifier suppression/generalization, and external data linkage risks.

Frameworks

  • HIPAA Privacy Rule
  • GDPR Article 89
  • k-anonymity Model
  • l-diversity Principle

Assessment Outputs

  • Re-identification Risk Score
  • Compliance Status
  • Privacy Vulnerability Analysis
  • Mitigation Strategies

Target Users

Data ScientistsPrivacy OfficersIRB MembersHealthcare Researchers
Launch Assessment Tool →

Patient Safety Calculator

Patient safety performance measurement and risk stratification

Measure patient safety culture and identify areas for improvement using evidence-based indicators from AHRQ, WHO, and Joint Commission. This comprehensive tool evaluates medication safety, patient identification, communication practices, infection control, fall prevention, and incident response to calculate an organizational patient safety index.

Methodology

Utilizes AHRQ Patient Safety Culture Survey domains, WHO Global Patient Safety Action Plan 2021-2030 framework, Joint Commission National Patient Safety Goals, and IHI Framework for Safe, Reliable, and Effective Care. Scoring incorporates weighted assessment across seven safety domains with evidence-based thresholds.

Frameworks

  • AHRQ Patient Safety Indicators
  • WHO Global Action Plan
  • Joint Commission NPSGs
  • IHI Safety Framework

Assessment Outputs

  • Patient Safety Index (0-100)
  • Domain-specific Scores
  • Risk Stratification
  • Evidence-based Interventions

Target Users

Quality Improvement OfficersPatient Safety TeamsHospital AdministratorsNursing Leadership
Launch Assessment Tool →

AI Model Evaluation

Comprehensive AI/ML model quality and regulatory readiness assessment

Evaluate healthcare AI/ML models for clinical validity, regulatory compliance, and deployment readiness. This tool assesses study design, data quality, model performance, bias and fairness, explainability, and adherence to AI reporting guidelines (TRIPOD-AI, CONSORT-AI, STARD-AI) to determine model maturity and provide actionable recommendations for improvement.

Methodology

Follows TRIPOD-AI prediction model reporting guidelines, CONSORT-AI randomized trial standards, STARD-AI diagnostic accuracy framework, and FDA SAMD Clinical Evaluation guidance. Scoring matrix evaluates 8 critical domains: study design, data quality, model architecture, performance metrics, bias assessment, explainability, clinical integration, and post-market monitoring.

Frameworks

  • TRIPOD-AI Guidelines
  • CONSORT-AI Standards
  • STARD-AI Framework
  • FDA SAMD Guidance

Assessment Outputs

  • Model Maturity Score
  • Regulatory Readiness Assessment
  • Bias and Fairness Analysis
  • Deployment Recommendations

Target Users

AI/ML DevelopersClinical ResearchersRegulatory StrategistsHealthcare Innovators
Launch Assessment Tool →

Validation, Limitations & Responsible Use

Tool Validation

All assessment tools have been developed by healthcare AI experts and reviewed against published literature and regulatory guidance. Scoring algorithms are based on established frameworks (FMEA, k-anonymity, AHRQ PSIs, TRIPOD-AI) with evidence-based thresholds. However, these tools provide preliminary assessments only and should not be considered formal certifications or regulatory submissions.

Known Limitations

  • Tools provide guidance based on general frameworks; specific organizational contexts may require additional considerations
  • Results should be validated by domain experts, legal counsel, and regulatory consultants
  • Scoring thresholds are based on literature consensus but may not reflect all international regulatory variations
  • Tools are continuously updated but may not reflect the most recent regulatory changes
  • Certain complex scenarios may require manual analysis beyond automated scoring

Responsible Use Guidelines

  • Interpret results in context: Consider organizational setting, patient population, and local regulations
  • Seek expert review: Consult qualified professionals for regulatory submissions and clinical deployment decisions
  • Document assessments: Maintain records of tool inputs, outputs, and subsequent actions for quality assurance
  • Combine with other methods: Use tools as part of comprehensive evaluation alongside other validation approaches
  • Stay informed: Monitor regulatory updates and academic literature relevant to your assessment area

When to Seek Professional Consultation

Mandatory professional review is recommended for:

  • Regulatory submissions to FDA, EMA, or other health authorities
  • Clinical deployment decisions for AI systems impacting patient care
  • Data sharing agreements involving identifiable or potentially re-identifiable data
  • High-risk or high-consequence healthcare AI applications
  • Legal compliance questions regarding HIPAA, GDPR, or other regulations
  • Incident investigations involving patient harm or safety events

Key Academic & Regulatory References

Regulatory Guidance

  1. FDA (2022). "Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: Software as a Medical Device (SaMD) Action Plan"
  2. ISO 14971:2019. "Medical devices — Application of risk management to medical devices"
  3. WHO (2021). "Ethics and governance of artificial intelligence for health: WHO guidance"
  4. 45 CFR § 164.514. "HIPAA Privacy Rule: Other requirements relating to uses and disclosures of protected health information"
  5. GDPR Article 89. "Safeguards and derogations relating to processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes"

Academic Literature

  1. Collins, G.S., et al. (2024). "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods." BMJ, 385:e078378
  2. Liu, X., et al. (2020). "Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension." Nature Medicine, 26(9):1364-1374
  3. Sweeney, L. (2002). "k-anonymity: A model for protecting privacy." International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5):557-570
  4. El Emam, K., & Arbuckle, L. (2013). Anonymizing Health Data: Case Studies and Methods to Get You Started. O'Reilly Media
  5. WHO (2021). "Global Patient Safety Action Plan 2021-2030: Towards eliminating avoidable harm in health care"
  6. AHRQ (2019). "Hospital Survey on Patient Safety Culture: User's Guide." Agency for Healthcare Research and Quality
  7. Char, D.S., Shah, N.H., & Magnus, D. (2018). "Implementing Machine Learning in Health Care — Addressing Ethical Challenges." NEJM, 378(11):981-983

Questions or Feedback?

These tools are continuously improved based on user feedback and evolving standards. If you have questions, suggestions for improvement, or need clarification on methodologies, please contact us.

For regulatory consultation, legal advice, or clinical safety assessments, please engage qualified professionals in those respective domains.