With every hospital admission, a vast amount of data is collected from every patient. Big data can help in data mining and processing of this volume of data. The goal of this study is to investigate the potential of big data analyses by analyzing clinically relevant data from the immediate postoperative phase using big data mining techniques. A second aim is to understand the importance of different postoperative parameters. We analyzed all data generated during the admission of 739 women undergoing a free DIEAP flap breast reconstruction. The patients’ complete midcare nursing report, laboratory data, operative reports and drug schedule were examined (7,405,359 data points). The duration of anesthesia does not predict the need for revision. Low Red Blood cell Counts (3.53 × 106/µL versus 3.79 × 106/µL, p < 0.001) and a low MAP (MAP = 73.37 versus 76.62; p < 0.001) postoperatively are correlated with significantly more revisions. Different drugs (asthma/COPD medication, Butyrophenones) can also play a significant role in the success of the free flap. In a world that is becoming more data driven, there is a clear need for electronic medical records which are easy to use for the practitioner, nursing staff, and the researcher. Very large datasets can be used, and big data analysis allows a relatively easy and fast interpretation all this information.

Abstract-Attracted by impressive cash flows and success stories, mobile application developers are trying to penetrate the mobile application market more than ever. Despite this increasing interest, the market configuration is certainly not always clear, let alone how to formulate an optimal business and revenue model from a developer's or provider's viewpoint. Based on case study research, we identified business model characteristics and several revenue models for a mobile application provider. In addition to existing literature on business models, this paper describes two main value network configurations for offering mobile applications: 1) All-in-one mobile application provider, and 2) Outsourcing mobile application provider. Further, we focus on the business model elements of an application developer which can be subdivided in recurrent elements (e.g.: cost structure, key partners, resources, key activities, promotion channels, customer relationships and revenue streams) or unique elements (value proposition and customer segments). This actor specific research allows us to 1) develop a decision chart and 2) to extend existing guidelines in order to determine the most optimal revenue model for a mobile application. The tool and the insights can help new developers to set up their business model or existing mobile application providers to evaluate their current one.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.