Rationale and Architecture Principles for Medical Application Platforms

The concept of “system of systems” architecture is increasingly prevalent in many critical domains. Such systems allow information to be pulled from a variety of sources, analyzed to discover correlations and trends, stored to enable realtime and post-hoc assessment, mined to better inform decisionmaking, and leveraged to automate control of system units. In contrast, medical devices typically have been developed as monolithic stand-alone units. However, a vision is emerging of a notion of a medical application platform (MAP) that would provide device and health information systems (HIS) interoperability, safety critical network middleware, and an execution environment for clinical applications (“apps”) that offer numerous advantages for safety and effectiveness in health care delivery. In this paper, we present the clinical safety/effectiveness and economic motivations for MAPs, and describe key characteristics of MAPs that are guiding the search for appropriate technology, regulatory, and ecosystem solutions. We give an overview of the Integrated Clinical Environment (ICE) – one particular achitecture for MAPs, and the Medical Device Coordination Framework – a prototype implementation of the ICE architecture

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems

Evaluation of the robustness of cerebral oximetry to variations in skin pigmentation using a tissue-simulating phantom

Clinical studies have demonstrated that epidermal pigmentation level can affect cerebral oximetry measurements. To evaluate the robustness of these devices, we have developed a phantom-based test method that includes an epidermis-simulating layer with several melanin concentrations and a 3D-printed cerebrovascular module. Measurements were performed with neonatal, pediatric and adult sensors from two commercial oximeters, where neonatal probes had shorter source-detector separation distances. Referenced blood oxygenation levels ranged from 30 to 90%. Cerebral oximeter outputs exhibited a consistent decrease in saturation level with simulated melanin content; this effect was greatest at low saturation levels, producing a change of up to 15%. Dependence on pigmentation was strongest in a neonatal sensor, possibly due to its high reflectivity. Overall, our findings indicate that a modular channel-array phantom approach can provide a practical tool for assessing the impact of skin pigmentation on cerebral oximeter performance and that modifications to algorithms and/or instrumentation may be needed to mitigate pigmentation bias.Funding: FDA Office of Minority Health and Health Equity and FDA Perinatal Health Center of Excellence</p