An evidence-based risk-oriented V-model methodology to develop ambient intelligent medical software

Abstract

The Ambient Intelligence (AmI) paradigm has increasingly been adopted to build what we name Ambient Intelligent Medical Software (AmI-MS) to face the complexity of designing software-based systems for monitoring medical conditions and supporting physician in making decisions. When this kind of systems are directly related to the safety of people, they are subject to pass an approval process as Medical Devices to ascertain their quality regarding safety and effectiveness. Currently, building an AmI-MS follows a conventional approach which employs two processes: the first for developing the software and the second for identifying, assessing, and managing the related risks. This method hides its complexity within the interaction between the processes mentioned above, which is not standardised and left to manufacturer quality management. For AmI-MS, it is even worse due to the multitude of environmental conditions to be analysed from both a design and a risk perspective which leads to inappropriate, or insufficient evidence, and undiscovered, or not adequately controlled, risk scenarios. In this work, we propose a novel risk-driven, evidence-oriented V-model methodology which addresses the previous issues by defining a seamless and unified development process. The novelty of our approach consists of interleaving risk management with software development activities and employing assurance cases for driving and controlling quality concerns. A concrete application on a case study is presented to show the strengths and weaknesses of this approach.