February 3, 2025
Research Fellow:
- Stephan Mabry
Title: Taxonomy and Measurement Criteria for Automated Testing Frameworks in Healthcare Interoperability
Research Abstract
The digitization of healthcare has resulted in exponential data growth and integration of diverse systems, such as EHRs, imaging platforms, and IoT devices, but challenges like fragmented data, inconsistent standards, and frequent system updates hinder interoperability and threaten patient safety and operational efficiency. Automated testing has become essential for ensuring system reliability, yet finite resources and varying API criticality demand a strategic approach. This paper proposes a framework comprising a taxonomy of automated testing strategies, healthcare- specific evaluation criteria, and a risk assessment model to prioritize high-impact API regions. By focusing on critical areas such as clinical workflows, regulatory compliance, and data sensitivity, the framework provides actionable tools for maximizing the results of deploying automated testing methodologies. The result demonstrates the e effectiveness of functional validation for safety-critical workflows and highlight the inefficacy of methods like chaos testing in healthcare. The findings provide a structured approach to evaluating automated testing methodologies, offering insights into how different strategies align with the unique demands of healthcare interoperability, such as ensuring system resilience, regulatory compliance, and data accuracy. The framework's applicability is supported by examples of prioritizing high-risk API regions and is positioned for future validation through real-world case studies and standardization efforts to advance healthcare interoperability.
Link To Research Paper
Link To Research Presentation
Research Summary
The rapid digitization of healthcare has led to a surge in data exchange across systems like electronic health records (EHRs), imaging platforms, and IoT devices. However, interoperability challenges—including data fragmentation, inconsistent standards, and frequent system updates—hinder seamless integration and create risks for patient safety and operational efficiency. To address these issues, automated testing has become essential for validating system reliability and ensuring compliance with regulatory frameworks such as HL7, FHIR, and HIPAA. This paper introduces a taxonomy of automated testing strategies, healthcare-specific evaluation criteria, and a risk assessment model designed to help organizations prioritize high-impact API testing efforts.
Mabry’s framework categorizes automated testing methods into six key areas, including functional validation, performance testing, security assessment, and compliance verification. By evaluating testing strategies based on their alignment with clinical workflows, data integrity, and regulatory requirements, the research highlights which approaches are most effective in detecting vulnerabilities and ensuring system resilience. The findings reveal that some commonly used techniques, such as chaos testing, are ineffective in healthcare due to their disruptive nature, while others—like functional and interoperability-specific testing—play a critical role in maintaining data accuracy and patient safety.
The paper also introduces an API risk assessment model that assigns risk scores to different API regions based on factors like patient safety impact, workflow criticality, and regulatory compliance requirements. By prioritizing automated testing in high-risk areas, healthcare organizations can allocate resources more efficiently and reduce the likelihood of system failures that could impact patient care. The research ultimately provides a structured, actionable approach for healthcare IT leaders to enhance the reliability, security, and efficiency of their digital ecosystems while meeting the increasing demands of interoperability.
About Research Fellow Stephan Mabry
Stephan Mabry is a distinguished Solutions Architect and Research Fellow at MACROPRAXIS, specializing in healthcare IT and digital transformation. With expertise in full-stack development, cybersecurity, application architecture, and AI in healthcare, Stephan has designed impactful solutions that improve patient care, reduce physician workload, and optimize healthcare operations. His experience spans over 1,000 hours of clinical observation across 70+ health systems, collaborating closely with medical and executive teams to implement targeted improvements. Stephan holds a BS in Mathematics and Biology, a business management certificate, multiple Epic certifications, and is currently pursuing a PhD focused on AI in healthcare. With two patents and numerous awards for his innovative healthcare technologies, Stephan consistently bridges technical and clinical domains, driving measurable outcomes in healthcare IT.