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Data in the healthcare sector is growing beyond dealing capacity of the health care organizations and is expected to increase significantly in the coming years. Majority of the Healthcare data is often unstructured, exists in silos and resides in imaging systems, medical prescription notes, insurance claims data, EPR (Electronic Patient Records) etc. integrating these heterogeneous data and factoring it in to advance analytics is critical to improve healthcare outcomes. Either because data are isolated in disparate or incompatible formats or due to the lack in processing capability to load and query large datasets in a timely fashion the Healthcare organizations are not in a position to leverage the benefits of the vast data they have. With convergence of advanced computing and numerous Big Data technological options like commercial solutions, Open Source, Cloud etc. it is now possible to attain high performance, scalability at a relatively low cost. Big data solutions often come with set of innovative data management solutions and analytical tools, when effectively implemented can transform the healthcare outcomes.

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Virtual reality (VR) is the simulation of reality where the users would be immersed in an artificial/virtual environment that isn't there but creates an illusion as if it really exists. People using this technology get a feeling that they are performing everything in real time. This gives users a sense of satisfaction. Initially, VR technology was used for gaming purposes, but now it is used in many sectors, including healthcare. There are many situations wherein, when it is expensive or impossible to do something, in reality, a probable solution is virtual reality. An important area where it is explored is healthcare for training doctors, diagnosis, and treatment of various ailments. The main objective of this chapter is to shed light on the applications of VR in healthcare and to discuss some applications showing how the medical field has already started reaping the benefits of VR.

Abstract. [Context and motivation]Studies have emphasized the need for effective requirements elicitation owing to its significant impacts on software quality and overall project outcomes to meet system objectives. The empirical studies in literature present the relationships between the specific characteristics that affect elicitation and project performance that focus on process control and product flexibility. There is, however, no substantial research on the empirical relationship between the generalized problems in requirements elicitation and project performance. [Question/problem]The issues encountered in requirements elicitation generalized through categories of problems of scope, problems of volatility and problems of understanding. This study aims in establishing an empirical model to study the behavior between the requirements elicitation issues and project performance. This study also validates the model for its consistency with practitioner's views and earlier studies. [Principal ideas/ results]Researchers and practitioners have focused on developing tools and techniques that will enhance the requirements elicitation and analysis phases. However, the effectiveness of the tool usage is dependent on skills and behaviors of people and organization using them. The aspects of behavior are best modeled using techniques adopted in social research, viz. confirmatory factor analysis; the technique is adopted for this study.[Contribution] This study deduced a causal relationship between the requirements elicitation issues and project performance. This study also attempted to establish a priority-setting and decision-making to address elicitation issues that can control and manage residual performance risks.