Automated Detection and Mitigation of Dependability Failures in Healthcare Scenarios through Digital Twins

TL;DR

This paper introduces M-GENGAR, a novel methodology using digital twins and formal analysis to proactively detect and mitigate dependability failures in healthcare cyber-physical systems, enhancing patient safety.

Contribution

It presents a new dependability assurance framework combining modeling, data-driven analysis, and automated mitigation synthesis for medical CPSs.

Findings

Achieves 87.5% success in stabilizing patient metrics in case studies.

Synthesized strategies outperform human decision-making in maintaining vital signs.

Maintains relevant metrics 20% closer to healthy values on average.

Abstract

Medical Cyber-Physical Systems (CPSs) integrating Patients, Devices, and healthcare personnel (Physicians) form safety-critical PDP triads whose dependability is challenged by system heterogeneity and uncertainty in human and physiological behavior. While existing clinical decision support systems support clinical practice, there remains a need for proactive, reliability-oriented methodologies capable of identifying and mitigating failure scenarios before patient safety is compromised. This paper presents M-GENGAR, a methodology based on a closed-loop Digital Twin (DT) paradigm for dependability assurance of medical CPSs. The approach combines Stochastic Hybrid Automata modeling, data-driven learning of patient dynamics, and Statistical Model Checking with an offline critical scenario detection phase that integrates model-space exploration and diversity analysis to systematically identify and classify scenarios violating expert-defined dependability requirements. M-GENGAR also supports the automated synthesis of mitigation strategies, enabling runtime feedback and control within the DT loop. We evaluate M-GENGAR on a representative use case study involving a pulmonary ventilator. Results show that, in 87.5% of the evaluated scenarios, strategies synthesized through formal game-theoretic analysis stabilize patient vital metrics at least as effectively as human decision-making, while maintaining relevant metrics 20% closer to nominal healthy values on average.