Introduction
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The development of big data and big data analytics in healthcare holds the promise of improving healthcare resource value, management practices, and patient outcomes, while decreasing healthcare costs[Big data analytics methodology is a multistep process that typically includes a concept statement or goal for the data mining application, dataset selection, pre-analysis data processing (transformation and/or reduction), application of one or more analytic approaches, evaluation and interpretation of results, and application and translation of findings[CPI technology monitors the interface pressure between a patient’s skin in contact with a support surface and provides assistance with patient repositioning to offload high or prolonged areas of pressure[While CPI of hospitalized patients may be useful for both the prevention and management of these injuries, for each patient, every 72 h, 259,200 frames of pressure images at one-second intervals will be produced. This would create spatiotemporal data sets of approximately 6.7 GB on a single patient. As we anticipated enrolling 678 research subjects, our trial would collect in total an estimated 4.52 TB of data, creating challenges in data management and analysis not dissimilar to what other health researchers face in clinical trials of emerging health care technologies. The key pieces of information, collected in our trial, are pressure values and their duration on each body part while a patient laid on a hospital bed. The large amounts of data provided mean that it is necessary to find ways to summarize the data while avoiding loss of critical information.Our group has conducted a clinical trial evaluating the effectiveness of continuous pressure imaging (CPI) technology on the reduction of pressure injuries [