Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse “big data” measures using integrated data-driven cross-discipline system architecture.

将调查数据与替代数据源（例如，可穿戴技术，应用程序，生理，生态监测，基因组，神经认知评估，脑成像和心理物理数据）相结合以描绘出创伤患者的完整生物行为图景，会带来许多复杂的系统挑战和解决方案。从急诊科开始，并合并这些多样化，广泛且独立的数据流，在技术，运营和后勤方面均面临挑战，但可以使人们对创伤的长期影响有更深入的科学了解。我们的手稿描述了将这些多维大数据元素纳入并在美国急诊室进行的临床观察研究中的预期链接，目的是了解，预防，并预测每年影响超过4000万美国人的创伤后神经精神疾病后遗症（APNS）。我们概述了关键的数据驱动系统挑战和解决方案，并使用集成的数据驱动的跨学科系统体系结构，结合了这些多样化的“大数据”指标，研究了资格考虑因素，合规性和响应率结果。