Complexity stage model of the medical device development based on economic evaluation-MedDee

The development of a new product is essential for the progress and success of any company. The medical device market is very specific, which is challenging. Therefore, this paper assesses an economic model for medical device evaluation using the economic, health, technology regulatory, and present market knowledge to enable the cost-time conception for any applicant. The purpose of this study is to propose a comprehensive stage model of the medical device development to subsequently describe the financial expenditure of the entire development process. The identification of critical steps was based on the literature review, and analysis, and a comparison of the available medical device development stages and directives. Furthermore, a preliminary assessment of the medical device development steps and procedures on the basis of the interviews was performed. Six interviews were conducted with an average duration of one hour, focusing on areas: relevance and level of detail of the medical device development stages, involvement of economic methods, and applicability of the proposed model. Subsequently, the improvement and modification of the medical device investment process, based on respondents' responses, were conducted. The authors have proposed the complexity model MedDee-Medical Devices Development by Economic Evaluation. This model is comprised of six phases: initiation, concept, design, production, final verification, and market disposition in which the economic methods are incorporated.Web of Science125art. no. 175

From Verified Models to Verified Code for Safe Medical Devices

Medical devices play an essential role in the care of patients around the world, and can have a life-saving effect. An emerging category of autonomous medical devices like implantable pacemakers and implantable cardioverter defibrillators (ICD) diagnose conditions of the patient and autonomously deliver therapies. Without trained professionals in the loop, the software component of autonomous medical devices is responsible for making critical therapeutic decisions, which pose a new set of challenges to guarantee patient safety. As regulation effort to guarantee patient safety, device manufacturers are required to submit evidence for the safety and efficacy of the medical devices before they can be released to the market. Due to the closed-loop interaction between the device and the patient, the safety and efficacy of autonomous medical devices must ultimately be evaluated within their physiological context. Currently the primary closed-loop validation of medical devices is in form of clinical trials, in which the devices are evaluated on real patients. Clinical trials are expensive and expose the patients to risks associated with untested devices. Clinical trials are also conducted after device development, therefore issues found during clinical trials are expensive to fix. There is urgent need for closed-loop validation of autonomous medical devices before the devices are used in clinical trials. In this thesis, I used implantable cardiac devices to demonstrate the applications of model-based approaches during and after device development to provide confidence towards the safety and efficacy of the devices. A heart model structure is developed to mimic the electrical behaviors of the heart in various heart conditions. The heart models created with the model structure are capable of interacting with implantable cardiac devices in closed-loop and can provide physiological interpretations for a large variety of heart conditions. With the heart models, I demonstrated that closed-loop model checking is capable of identifying known and unknown safety violations within the pacemaker design. More importantly, I developed a framework to choose the most appropriate heart models to cover physiological conditions that the pacemaker may encounter, and provide physiological context to counter-examples returned by the model checker. A model translation tool UPP2SF is then developed to translate the pacemaker design in UPPAAL to Stateflow, and automatically generated to C code. The automated and rigorous translation ensures that the properties verified during model checking still hold in the implementation, which justifies the model checking effort. Finally, the devices are evaluated with a virtual patient cohort consists of a large number of heart models before evaluated in clinical trials. These in-silico pre-clinical trials provide useful insights which can be used to increase the success rate of a clinical trial. The work in this dissertation demonstrated the importance and challenges to represent physiological behaviors during closed-loop validation of autonomous medical devices, and demonstrated the capability of model-based approaches to provide safety and efficacy evidence during and after device development

From Requirements to Code: Model Based Development of a Medical Cyber Physical System

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques. In this paper we present an end-to-end model-based approach to medical device software development along with the artifacts created in the process. While outlining the approach, we also describe our experiences, challenges, and lessons learned in the process of formulating and analyzing the requirements, modeling the system, formally verifying the models, generating code, and executing the generated code in the hardware for generic patient controlled analgesic infusion pump (GPCA). We believe that the development artifacts and techniques presented in this paper could serve as a generic reference to be used by researchers, practitioners, and authorities while developing and evaluating cyber physical medical devices

“Nutrition Facts Labels” for Artificial Intelligence/Machine Learning-Based Medical Devices—The Urgent Need for Labeling Standards

Artificial Intelligence (“AI”), particularly its subset Machine Learning (“ML”), is quickly entering medical practice. The U.S. Food and Drug Administration (“FDA”) has already cleared or approved more than 520 AI/ ML-based medical devices, and many more devices are in the research and development pipeline. AI/ML-based medical devices are not only used in clinics by health care providers but are also increasingly offered directly to consumers for use, such as apps and wearables. Despite their tremendous potential for improving health care, AI/ML-based medical devices also raise many regulatory issues. This Article focuses on one issue that has not received sustained attention in the legal or policy debate: labeling for AI/ML-based medical devices. Labeling is crucial to prevent harm to patients and consumers (e.g., by reducing the risk of bias) and ensure that users know how to properly use the device and assess its benefits, potential risks, and limitations. It can also support transparency to users and thus promote public trust in new digital health technologies. This Article is the first to identify and thoroughly analyze the unique challenges of labeling for AI/ML-based medical devices and provide solutions to address them. It establishes that there are currently no standards of labeling for AI/ML-based medical devices. This is of particular concern as some of these devices are prone to biases, are opaque (“black boxes”), and have the ability to continuously learn. This Article argues that labeling standards for AI/ML-based medical devices are urgently needed, as the current labeling requirements for medical devices and the FDA’s case-by-case approach for a few AI/ML-based medical devices are insufficient. In particular, it proposes what such standards could look like, including eleven key types of information that should be included on the label, ranging from indications for use and details on the data sets to model limitations, warnings and precautions, and privacy and security. In addition, this Article argues that “nutrition facts labels,” known from food products, are a promising label design for AI/MLbased medical devices. Such labels should also be “dynamic” (rather than static) for adaptive algorithms that can continuously learn. Although this Article focuses on AI/ML-based medical devices, it also has implications for AI/ ML-based products that are not subject to FDA regulation

Design Optimization for an Electro-Thermally Actuated Polymeric Microgripper

Thermal micro-actuators are a promising solution to the need for large-displacement, gentle handling force, low-power MEMS actuators. Potential applications of these devices are micro-relays, assembling and miniature medical instrumentation. In this paper the development of thermal microactuators based on SU-8 polymer is described. The paper presents the development of a new microgripper which can realize a movement of the gripping arms with possibility for positioning and manipulating of the gripped object. Two models of polymeric microgripper electrothermo- mechanical actuated, using low actuation voltages, designed for SU-8 polymer fabrication were presented. The electro-thermal microgrippers were designed and optimized using finite element simulations. Electro-thermo-mechanical simulations based on finite element method were performed for each of the model in order to compare the results. Preliminary experimental tests were carried out.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838

Prototyping Closed Loop Physiologic Control With the Medical Device Coordination Framework

Medical devices historically have been monolithic units – developed, validated, and approved by regulatory authorities as standalone entities. Despite the fact that modern medical devices increasingly incorporate connectivity mechanisms that enable device data to be streamed to electronic health records and displays that aggregate data from multiple devices, connectivity is not being leveraged to allow an integrated collection of devices to work together as a single system to automate clinical work flows. This is due, in part, to current regulatory policies which prohibit such interactions due to safety concerns. In previous work, we proposed an open source middleware framework and an accompanying model-based development environment that could be used to quickly implement medical device coordination applications – enabling a “systems of systems” paradigm for medical devices. Such a paradigm shows great promise for supporting many applications that increase both the safety and effectiveness of medical care as well as the efficiency of clinical workflows. In this paper, we report on our experience using our Medical Device Coordination Framework (MDCF) to carry out a rapid prototyping of one such application – a multi-device medical system that uses closed loop physiologic control to a affect better patient outcomes for Patient Controlled Anelgesic (PCA) pumps

Using a Communication Model to Collect Measurement Data through Mobile Devices

Wireless systems and services have undergone remarkable development since the first mobile phone system was introduced in the early 1980s. The use of sensors in an Ambient Intelligence approach is a great solution in a medical environment. We define a communication architecture to facilitate the information transfer between all connected devices. This model is based in layers to allow the collection of measurement data to be used in our framework monitoring architecture. An overlay-based solution is built between network elements in order to provide an efficient and highly functional communication platform that allows the connection of a wide variety of devices and technologies, and serves also to perform additional functions such as the possibility to perform some processing in the network that may help to improve overall performance.Wireless systems and services have undergone remarkable development since the first mobile phone system was introduced in the early 1980s. The use of sensors in an Ambient Intelligence approach is a great solution in a medical environment. We define a communication architecture to facilitate the information transfer between all connected devices. This model is based in layers to allow the collection of measurement data to be used in our framework monitoring architecture. An overlay-based solution is built between network elements in order to provide an efficient and highly functional communication platform that allows the connection of a wide variety of devices and technologies, and serves also to perform additional functions such as the possibility to perform some processing in the network that may help to improve overall performance

Integrating formal methods into medical software development : the ASM approach

Medical devices are safety-critical systems since their malfunctions can seriously compromise human safety. Correct operation of a medical device depends upon the controlling software, whose development should adhere to certification standards. However, these standards provide general descriptions of common software engineering activities without any indication regarding particular methods and techniques to assure safety and reliability. This paper discusses how to integrate the use of a formal approach into the current normative for the medical software development. The rigorous process is based on the Abstract State Machine (ASM) formal method, its refinement principle, and model analysis approaches the method supports. The hemodialysis machine case study is used to show how the ASM-based design process covers most of the engineering activities required by the related standards, and provides rigorous approaches for medical software validation and verification

Using a Communication Model to Collect Measurement Data through Mobile Devices

Wireless systems and services have undergone remarkable development since the first mobile phone system was introduced in the early 1980s. The use of sensors in an Ambient Intelligence approach is a great solution in a medical environment. We define a communication architecture to facilitate the information transfer between all connected devices. This model is based in layers to allow the collection of measurement data to be used in our framework monitoring architecture. An overlay-based solution is built between network elements in order to provide an efficient and highly functional communication platform that allows the connection of a wide variety of devices and technologies, and serves also to perform additional functions such as the possibility to perform some processing in the network that may help to improve overall performance.Wireless systems and services have undergone remarkable development since the first mobile phone system was introduced in the early 1980s. The use of sensors in an Ambient Intelligence approach is a great solution in a medical environment. We define a communication architecture to facilitate the information transfer between all connected devices. This model is based in layers to allow the collection of measurement data to be used in our framework monitoring architecture. An overlay-based solution is built between network elements in order to provide an efficient and highly functional communication platform that allows the connection of a wide variety of devices and technologies, and serves also to perform additional functions such as the possibility to perform some processing in the network that may help to improve overall performance

Specification and Verification of Timing Properties in Interoperable Medical Systems

To support the dynamic composition of various devices/apps into a medical system at point-of-care, a set of communication patterns to describe the communication needs of devices has been proposed. To address timing requirements, each pattern breaks common timing properties into finer ones that can be enforced locally by the components. Common timing requirements for the underlying communication substrate are derived from these local properties. The local properties of devices are assured by the vendors at the development time. Although organizations procure devices that are compatible in terms of their local properties and middleware, they may not operate as desired. The latency of the organization network interacts with the local properties of devices. To validate the interaction among the timing properties of components and the network, we formally specify such systems in Timed Rebeca. We use model checking to verify the derived timing requirements of the communication substrate in terms of the network and device models. We provide a set of templates as a guideline to specify medical systems in terms of the formal model of patterns. A composite medical system using several devices is subject to state-space explosion. We extend the reduction technique of Timed Rebeca based on the static properties of patterns. We prove that our reduction is sound and show the applicability of our approach in reducing the state space by modeling two clinical scenarios made of several instances of patterns