Abstract
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.
Highlights
Health survey data collection is becoming more complex as researchers demand baseline survey items and other elements like adaptive sampling, continuous physiologic monitoring via wearable technologies, continuous passive digital phenotyping, flash surveys/audio recordings, and genomic data collection
5 Methods In addition to the previously defined schemas, creating a data-driven architecture utilizing such diverse data streams required pending and final disposition codes at each hand-off point to drive the valid, acceptable, and logical data step. This required a mixture of experience, anticipated outcomes, and establishing an agile, flexible system to adopt to real-world evidence
6 Results After balancing scientific trade-offs for biobehavioral, physiologic, and genomic outcomes versus big data acceptance and compliance, what do the interim results show? First, Table 5 shows the interim demographics of the trauma screened and/or population enrolled from September 2017 through March 2020
Summary
Health survey data collection is becoming more complex as researchers demand baseline survey items and other elements like adaptive sampling, continuous physiologic monitoring via wearable technologies, continuous passive digital phenotyping, flash surveys/audio recordings, and genomic data collection. Response rates are declining while concerns about privacy are growing [1,2,3] These forces are all at play in the collection of data from emergency department (ED) trauma patients. Operationalizing this complex and extensive data collection effort requires an integrated and tailored data-driven web-based Information Management System (IMS). In addition to connecting big data (2021) 10:9 sources with baseline health data, the IMS must control the data flow, allow and enable system interconnections and linkages, enrich compliance prospectively, and adhere to safety, security, and human subject regulations while being user-friendly for participants and clinical and laboratory-based researchers. We outline interim eligibility, compliance, and response rates by age and sex in this targeted population when incorporating these state-of-the-art assessments into biomedical research
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