Evidence-based development approach for safe, sustainable and secure mobile medical app

According to industry surveys, by 2018, more than 1.7 billion smartphone and tablet users will have downloaded at least one mobile medical app (MMA) [40]. Such widespread adoption of smartphone based medical apps is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive health data, thus it is necessary to have evidences of their trustworthiness before actual marketing. The key challenges in ensuring trustworthiness of MMAs are maintaining privacy of health data, long term operation of wearable sensors and ensuring no physical harm to the user. Traditionally, clinical studies are used to generate evidences of trustworthiness of medical systems. However, they can take a long time and could potentially harm the user during studies. Thus it is essential to establish trustworthiness of MMAs before their actual use. One way to generate such evidences can be using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging. Thus, it is required to analyze and develop MMA software while taking into account its interactions with human physiology to assure trustworthiness. This chapter focuses emerging app developmentmethodologies,which support automatic evaluation of trustworthiness of MMAs by supporting automatic generation of evidences. This methodology involves, a) evidence generation to assure trustworthiness i.e. safety, security and sustainability of MMAs and b) requirement assured code generation for vulnerable components of the MMA without hindering the app development process. These methods are intended to expedite the design to marketing process of MMAs. In this regard, this chapter discusses example models, tools and theory for evidence generation with the following themes: • Software design configuration estimation of MMAs: Using an optimization framework which can generate sustainable and safe sensor configurations while considering interactions of the MMA with the environment. • Requirements verification of the MMA design: Using models and tools to verify safety properties of the MMA design which can ensure the verified design will not cause any harm to the human physiology. • Automatic code generation for MMAs: Investigating methods for automatically generating safety, sustainability and security assured software for vulnerable components of a MMA. • Performance analysis of MMA software developed using evidence-based approach: Evaluating quality and response time of MMA software developed using evidence-based approach.