Application and integration

Case Study – Big Data Applications (Bhardwaj, Wodajo, Spano, Neal, & Coustasse, 2018; Wang & Hu, 2018)

As previously explained the ability to make timely and truly evidence-based informed decisions to provide more effective and personalized treatment while reducing the costs has been empowered by the introduction of big data analytics in healthcare. In fact, the ability to obtain and analyze big data can aid the identification of high-risk individuals, inform more effective treatments, and select cost reduction areas across the health care system. The application of big data analytics is vast within health care and goes beyond management of chronic diseases, management of resources to management of acute public health situations (e.g., as foreseen with COVID-19). Many of these applications are listed in the following article. (Bhardwaj et al., 2018)

Nevertheless, acquiring such novel information is not free of limitations. In fact, it requires the integration of multiple kinds of datasets as well as information from many sources (questionnaire interviews, standard clinical tests, and modern sources such as electronic medical records, mobile apps, and wearable devices). As a case study from clinical perspective, the following paper provides an overview while highlighting some of these limitations, on how combined multiple data sources integration and applied big-data analytics may potentially inform personalized nutrition interventions prevention and management of type 2 diabetes.(Wang & Hu, 2018)

Figure 7 – Design for the case study 1: multiple sources of information. Available at (Wang & Hu, 2018)