Book Review: Data-Driven Healthcare & Analytics in a Big Data World

the healthcare industry is generating large amounts of data, driven by a wide range of medical and healthcare functions, including clinical records, medical images, genomic data, health behaviors, clinical decision support, disease surveillance, and public health management [1]. Reports say that worldwide digital healthcare data was estimated to be equal to 500 petabytes in 2012 and is expected to reach 25,000 petabytes in 2020 [2]. However, the challenges in handling this big data are not just about volume. The challenges include capturing, storing, searching, sharing, analyzing, and then finding insights from complex, noisy, heterogeneous, longitudinal, and voluminous data. The collection of large and complex data sets is difficult (or impossible) to process using common database management tools or traditional data processing applications. Therefore, these challenges are a reality for every business, but only a few firms are taking advantage of the new world of data and information. To take advantage of the massive amounts of data in healthcare fields and provide the right intervention to the right patient at the right time, personalized care to the patient, and potentially benefit all the components of a healthcare system, such as provider, payer, patient, and management, big data analytics needs to bridge data mining and healthcare informatics communities. In particular, data mining researchers need to be introduced to the sources available and the possible challenges and techniques associated with using big data in the healthcare domain; at the same time, healthcare analysts and practitioners need to be exposed to the advances in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data. Data-Driven Healthcare & Analytics in a Big Data World