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Background In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using four federated Common Data Models. N3C Data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source CDM conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for data quality improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multi-site data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Among patients whose physicians reviewed recommendations of the decision support tool discordant therapy decreased significantly over 1 year. However, in nonstratified analyses, the intervention did not result in significant improvements in discordant antithrombotic therapy.

As unmanned extraction vehicles become a reality in the military theater, opportunities to augment medical operations with telesurgical robotics become more plausible. This project demonstrated an experimental surgical robot using an unmanned airborne vehicle (UAV) as a network topology. Because battlefield operations are dynamic and geographically challenging, the installation of wireless networks is not a feasible option at this point. However, to utilize telesurgical robotics to assist in the urgent medical care of wounded soldiers, a robust, high bandwidth, low latency network is requisite. For the first time, a mobile surgical robotic system was deployed to an austere environment and surgeons were able to remotely operate the systems wirelessly using a UAV. Two University of Cincinnati surgeons were able to remotely drive the University of Washington's RAVEN robot's end effectors. The network topology demonstrated a highly portable, quickly deployable, bandwidth-sufficient and low latency wireless network required for battlefield use.