Model and Integrate Medical Resource Available Times and Relationships in Verifiably Correct Executable Medical Best Practice Guideline Models

Abstract

Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate is significantly reduced by computerizing medical best practice guidelines [21]. Recent data also show that some morbidity and mortality in emergency care are directly caused by delayed or interrupted treatment due to lack of medical resources [20]. However, medical guidelines usually do not provide guidance on medical resource demands and how to manage potential unexpected delays in resource availability. If medical resources are temporarily unavailable, safety properties in existing executable medical guideline models may fail which may cause increased risk to patients under care.The paper presents a separately model and jointly verify (SMJV) architecture to separately model medical resource available times and relationships and jointly verify safety properties of existing medical best practice guideline models with resource models being integrated in. The SMJV architecture allows medical staff to effectively manage medical resource demands and unexpected resource availability delays during emergency care. The separated modeling approach also allows different domain professionals to make independent model modifications, facilitates the management of frequent resource availability changes, and enables resource statechart reuse in multiple medical guideline models. A simplified stroke scenario is used as a case study to investigate the effectiveness and validity of the SMJV architecture. The case study indicates that the SMJV architecture is able to identify unsafe properties caused by unexpected resource delays.

A Systematic Literature Review on Model-driven Engineering for Cyber-Physical Systems

“…Studies implemented M2M transformations can be categorized into two: First category covers the studies using existing tools and languages. [59] used Y2U tool to transform Statechart models to UPPAAL timed automata model. [50] presented M2M transformation by transforming Simulink simulation models to AADL architectural models using Assisted Transformation of Models engine.…”

“…Studies using UPPAAL for verification include the followings: [59] used UPPAAL to formally verify the safety properties of medical guideline. A Domain-specific model checking (DSMC) for MECHATRONICUML using UPPAAL model checker was presented in [49].…”

“…Yakindu statechart tools was adopted in [59] to model and simulate a stroke statechart model. Likewise, in [58], they used Yakindu statecharts for the modeling of a simplified cardiac arrest.…”

“…Examples are Controllers (e.g. [9,8]), Development Artifacts related with transformation [99], simulation [136], System verification [162].System behavior covering timing behavior [59], System safety properties [113], and System requirements [156,65].…”

Design Verifiably Correct Model Patterns to Facilitate Modeling Medical Best Practice Guidelines With Statecharts

“…Jiang et al [9] presented a timed automata and synchronous dataflow based framework for system design. Guo et al [10], [11] proposed approaches to model and integrate medical resource availability and relationships in existing medical guideline models. To help improve clinical validation, Wu et al have developed a workflow adaptation protocol [12] to help physicians safely adapt workflows to react to patient adverse events and a treatment validation protocol [13] to enforce the correct execution sequence of performing a treatment based on preconditions validation, side effects monitoring, and expected responses checking.…”