The medical device products at the European Union market must be safe and effective. To ensure this, medical device manufacturers must comply to the new regulatory requirements brought by the Medical Device Regulation (MDR) and the In Vitro Diagnostic Medical Device Regulation (IVDR). In general, the new regulations increase regulatory requirements and oversight, especially for medical software, and this is also true for requirements related to cybersecurity, which are now explicitly addressed in the legislation. The significant legislation changes currently underway, combined with increased cybersecurity requirements, create unique challenges for manufacturers to comply with the regulatory framework. In this paper, we review the new cybersecurity requirements in the light of currently available guidance documents, and pinpoint four core concepts around which cybersecurity compliance can be built. We argue that these core concepts form a foundations for cybersecurity compliance in the European Union regulatory framework.
Data and software for "A Framework for Enabling Safe and Resilient Food Factories for the Public Feeding Programs"
Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. This approach is based on discrete-time Markov chains enriched with event occurrence probabilities. We use the PRISM and Storm frameworks and their model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach on two serious games used by clinicians to monitor Alzheimer patients. This paper focuses on the suitability of such a formal approach to model patients’ behavior, to check behavioral properties of medical interest, and on the respective advantages of the PRISM and Storm frameworks. Our goal is to provide a new tool for doctors to evaluate patients.
This case study paper describes the development and evaluation of a prototype, Golden Jubilants, a smartphone app. Golden Jubilants was built within a software engineering in healthcare project, ReDEAP, that aimed to identify a set of recommendations for the design of smartphone apps for older adults aged over 50. Prototype development and evaluation is a recognized way to elicit meaningful feedback from any user group. This tangible artifact was interactive and provided fruitful engagement for us as researchers, and for the older adults who participated. This short paper presents the prototype evaluation process, and techniques used to ensure older adult involvement. To conclude, we discuss four key recommendations for consideration by software engineering in healthcare researchers who are using prototypes in their research – develop the research project through public and patient involvement, harness the potential of established evaluation and testing standards, develop a needed and tangible prototype, and involve an external group to evaluate findings.
Data quality is a critical requirement for data-driven clinical decision making in modern healthcare. It is a key prerequisite to many clinical analytics applications, yet much of the research to date focuses on assessing data quality of electronic health records for secondary use. This paper proposes a trust model and provenance-based assessment method for considering data quality during clinical decision making at the point of care. The method uses fuzzy logic to infer data quality trust from a data user’s trust preferences with respect to data producers, data production methods, verification of data items, and certification of data production methods. Implementation with an existing “SMART on FHIR” app in primary care demonstrates the feasibility of model and method. An extension to FHIR resources for data quality trust allows for platform interoperability across system contexts. We consider dual process theories in designing a user interface that supports data quality trust for clinical decisions in heuristic and systematic cognitive processing modes. Model and method are adaptable to other application domains that rely on data quality for decision making.
