In this 2019 paper published in BMC Medical Informatics and Decision Making, Wang et al. of China Medical University demonstrate the results of an attempt to add autoverification mechanisms for coagulation assays into their laboratory information systems (LIS) to better improve both operations and patient care. After providing background on coagulation assays and autoverification guidelines, the researchers describe their methodology for programmatically developing autoverification decisions rules and implementing them into their laboratory workflow. Additionally, they discuss how best assess validation of the new system and its results. The authors conclude that not only has the new system improved turnaround time in the lab, but it also has improved the level of medical safety in its diagnoses in the affiliated hospitals.
This is a University of Adelaide course that is released on the edX platform. The ten-week course is designed to provide greater "understanding of the various applications of big data methods in industry and research." The course is free to take, with a Verified Certificate of completion available for $150. This course is also part of Adelaide's Big Data MicroMasters program. The course requires on average eight to ten hours a week of effort. Access to the class begins September 29, 2019.