This paper is a systematic review of the relevant literature surrounding the implementation and utilisation of eHealth in order to identify key challenges and opportunities to future eHealth applications. NHS Evidence, PubMed, IEEE Explorer, Cochrane Library and JMIR Publications were all searched for reviews published between the dates 1 January 2010 and 30 June 2017. 47 papers met the final inclusion criterion. The published literature focused on a wide array of challenges, categorised into five areas, facing the implementation and utilisation of eHealth and from this, four areas of opportunity to advance eHealth were identified. The five challenge areas are (C1) Stakeholders and System Users, (C2) Technology and Interoperability, (C3) Cost-Effectiveness and Start-up Costs, (C4) Legal Clarity and Legal Framework and (C5) Local Context and Regional Differences. The four opportunity areas are (O1) Participation and Contribution, (O2) Foundation and Sustainability, (O3) Improvement and Productivity and (O4) Identification and Application. The literature analysed in this systematic review identifies design and implementation priorities that can guide the development and utilisation of future eHealth initiatives.

As data projects become more conventional, increase in the use of information has surpassed the knowledge of how to support individuals/teams that undertake such projects. Leading data mining methodology, CRISP-DM has become limited in managing the requirements of working with recent technologies such as Machine Learning. Resultantly, many have either created their own methods or adopted alternative approaches such as the Design Thinking and Lean Startup innovation strategies. Consequently, this paper proposes a novel software development methodology entitled Lean Design Thinking Methodology (LDTM) to guide the development of modern data projects. LDTM combines the strengths of CRISP-DM with the more innovative Design Thinking and Lean Startup strategies to introduce an approach divided into three stages, comprising of seven steps. This paper concludes on how there is no one correct method, nor is one single approach enough, but together, elements of each approach can unite to help guide data projects forward.