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The current rapidly changing and highly competitive market has put companies under a great pressure towards adopting sustainable practices, in terms of keeping a healthy balance among economic, environmental and social performances. In this context, the lean-green manufacturing approach, which combines lean practices focused on customers’ demand, and green practices focused on reducing the business’ environmental impact, has gained popularity. Nevertheless, the lean-green manufacturing is still a relatively new practice, lacking a clear and structured research definition, and of significant evidence of successful cases in the practice. In this paper, a literature review is conducted to identify the actual possibility of combining lean and green practices, the current trends for implementing such combination and the potential sustainability improvements such implementation can lead. It is the authors’ intention that the findings analysed in this paper can contribute to the state-of-the-art of lean-green manufacturing and provide practitioners with a useful tool towards developing effective strategies for its deployment.

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With the widespread use of healthcare information systems commonly known as electronic health records (EHRs), there is significant scope for improving the way healthcare is delivered by resorting to the power of big data. This has made data mining and predictive analytics an important tool for healthcare decision making. The literature has reported attempts for knowledge discovery from the big data to improve the delivery of healthcare services, however, there appears no attempt for assessing and synthesizing the available information on how the big data phenomenon has contributed to better outcomes in the delivery of healthcare services. This paper aims to achieve this by systematically reviewing the existing body of knowledge to categorize and evaluate the reported studies on healthcare operations/ and data mining frameworks. The outcome of this study is useful as a reference for the practitioners and as a research platform for the academia.