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Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...
The trend of large health companies merging allows for greater health data accessibility. Greater health data lays the groundwork for the implementation of AI algorithms. A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As more data is collected, machine learning ...
Big data analytics has been used in healthcare in providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries.
Numerous companies are exploring the possibilities of the incorporation of big data in the healthcare industry. Many companies investigate the market opportunities through the realms of "data assessment, storage, management, and analysis technologies" which are all crucial parts of the healthcare industry. [10]
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics , Biomedical informatics , and machine learning , with the goal of understanding biological and medical data.
[16] [17] EHRs have expanded the secondary uses of health data for quality assurance, clinical research, medical research and development, public health, and big data health analytics, among other fields. [18] [19] [20] [14] Personal health records (PHRs), while less popular than EHRs, [21] have expanded the primary uses of health data.
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The first edition of the textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. [1] The second edition of the book was printed in 2023. [2]
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