To analyse healthcare data accurately, you must first understand epidemiology and basic study design, which are covered in part one of this training series. However, you must also be able to perform descriptive and regression analysis and defend your model selection, interpretation, and presentation decisions. Part two of our Designing Big Data Healthcare Studies series delves into the logistics of planning and carrying out analysis on the analytic data set prepared in the previous course. The expert demonstrates how to conduct the analysis and interpret the final model in light of your original hypothesis. We teach best practises for code naming and arrangement, stepwise selection modelling, odd and prevalence ratios, and relative risk along the way. With the help of these tutorials, you should be able to create excellent healthcare studies that take advantage of everything big data has to offer.
‘Big data’ refers to massive amounts of information that can do amazing things. It has piqued the interest of many people over the last two decades due to the enormous potential it holds. Various public and private sector industries generate, store, and analyse large amounts of data to improve the services they provide. In the healthcare industry, various sources of big data include hospital records, patient medical records, medical examination results, and internet of things devices.
Biomedical research also generates a substantial amount of big data that is relevant to public healthcare. To derive meaningful information from this data, it must be properly managed and analysed. Otherwise, analysing big data to find a solution quickly becomes akin to looking for a needle in a haystack. There are numerous challenges associated with each step of big data handling that can only be overcome by using high-end computing solutions for big data analysis.
As a result, to provide relevant solutions for improving public health, healthcare providers must be fully equipped with the necessary infrastructure to generate and analyse big data in a systematic manner. Big data management, analysis, and interpretation that is efficient can change the game by opening up new avenues for modern healthcare. That is why various industries, including the healthcare industry, are working hard to turn this potential into better services and financial benefits. Modern healthcare organisations may be able to revolutionise medical therapies and personalised medicine with a strong integration of biomedical and healthcare data.
Various healthcare organisations now have access to massive amounts of disparate medical data. These data could be a valuable resource for gaining insights into how to improve care delivery and reduce waste. The size and complexity of these datasets’ present significant challenges in analysis and subsequent application to a clinical setting. This course introduces the characteristics and analytic challenges associated with dealing with clinical data from electronic health records. Many of these insights are derived from the medical informatics and data mining/machine learning communities. This course is divided into three sections: System, Algorithm, and Application
The core value of big data has been effectively utilised in the business sector for the identification of consumer behavioural patterns in order to develop innovative business services and solutions. In the healthcare sector, the use of big data enables predictive analytical techniques and machine learning platforms (Al-Jarrah et al., 2015) to provide long-term solutions such as treatment plan implementation and personalised medical care. Jee and Kim (2013) compared healthcare big data to business sector big data in terms of different attributes and values. They renamed the characteristics of healthcare big data Silo, Security, and Variety, rather than Volume, Velocity, and Variety. Silo is a legacy database that contains public healthcare information that is kept on the premises of stakeholders such as hospitals. The security feature denotes the extra care required in the maintenance of healthcare data. The presence of structured, unstructured, and semi-structured healthcare data is indicated by the variety feature.
From the perspective of involved stakeholders, the advent of big data analytics and its associated technologies resulted in pragmatic transformations in the healthcare domain at various stages. The impact of big data in healthcare results in the identification of new data sources such as social media platforms, telematics, wearable devices, and so on, in addition to the analysis of legacy sources such as patient medical history, diagnostic and clinical trial data, drug effectiveness indexes, and so on.
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