The role of Big Data in the business challenge of Covid-19: a systematic literature review in managerial studies

Using trends and outliers in managing delayed transfusions

OBJECTIVES

To investigate if time to initiate a blood transfusion after an informative laboratory test could feasibly be used by the transfusion medicine service as a metric to monitor for transfusion delays.

BACKGROUND

Delayed transfusions may result in patient morbidity and mortality, but no standards for timely transfusion have been developed. Information technology tools could be implemented to identify gaps in provision of blood and to recognise areas of improvement.

MATERIALS AND METHODS

Data obtained from a children's hospital's data science platform and time from the release of laboratory results to the initiation of transfusions were calculated and weekly medians were used for trend analyses. Outlier events were obtained using locally estimated scatterplot smoothing and generalised extreme studentized deviate test.

RESULTS

Overall, the number of outlier events on the timing of transfusions based on patients' haemoglobin level and platelet count were small (n = 1 and n = 0 for 139 weeks, respectively). Investigation of these events for adverse clinical outcomes was non-significant.

CONCLUSIONS

Herein, we propose that the trends and outlier events could be further investigated and used to make decisions and implement protocols to improve patient care.

Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms' big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms' innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models.