Healthcare costs associated with acquired brain injury (ABI), injury to the brain acquired after birth and not hereditary, congenital or degenerative, are continuing to increase, driven in large part by the increasing prevalence and costs of one specific cause of ABI, traumatic brain injury (TBI). The risk factors associated with such injuries and their consequent outcomes are determined by a complex interaction of factors, including comorbid conditions, and lifestyle and environmental factors. Big data presents an opportunity to advance knowledge of the individual patient attributes important for subgrouping cohorts for valuable information about trends in public health, the circumstances of injuries, access to healthcare, response to treatment and rehabilitation. Despite its potential, big data methods to integrate and infer information obtained from administrative healthcare databases using the International Statistical Classification of Diseases and Related Health Problems, with codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases, are still evolving.
This symposium is proposed by a team with expertise in neuroscience, brain injury medicine, rehabilitation science and biostatistics, and incorporates input from the leaders of other big data programs as well as that from ethics board of the largest rehabilitation research teaching hospital in North America. We aim to expedite translation of healthcare big data from emergency, acute care, and inpatient rehabilitation results into enhanced practices for ABI patients. Individual talks will describe the process of development of novel statistical approaches addressing big data challenges in research design and methodology, and ethical considerations, using examples from TBI, central nervous system trauma, and hypoxic- ischemic brain injury cohorts to answer clinically relevant research questions.