Comprehensive improvements in the emergency laboratory test process based on information technology

Posted on May 7, 2024 By LabLynx Journal articles

Can the thoughtful application and optimization of laboratory informatics solutions lead to do a variety of improvements in a hospital-based emergency medicine laboratory? Zhang et al. sought to answer this question in a a contemporary tertiary second-class general hospital in China. After analyzing their emergency laboratory workflows, identifying problems withing those workflows, and identifying other areas of optimization, the authors applied a laboratory information management system (LIMS) to their operations, upgraded instrumentation, and finessed other aspects of their IT infrastructure. After discussing their results, the authors concluded that "the automation and standardization of most stages in emergency laboratory testing can be realized by IT, which reduces the workload of employees and improves emergency laboratory test quality," further noting that "emergency laboratory test report time can be shortened, emergency laboratory test quality can be enhanced, and employee and patient satisfaction can also be improved."

Restricted data management: The current practice and the future

Posted on April 29, 2024 By LabLynx Journal articles

In this brief article in Journal of Privacy and Confidentiality, Jang et al. of Inter-university Consortium for Political and Social Research (ICPSR) at University of Michigan review the current approach of researchers towards restricted data management and how it may need to evolve given certain challenges. They offer this review in the scope of increasing demands for FAIR (findable, accessible, interoperable, and reusable) data in research and journal communities. After a brief introduction, the authors discuss three main elements of managing restricted date: data use agreements, disclose review, and training. While the Five Safes framework is effective under this scope, the authors concludes that such "safeguards could generate unintended challenges to certain groups of individuals (e.g., institutional approval that could exclude researchers without institutional affiliation) or in different areas (e.g., rigorous output checking that requires extensive insights from experts)." They add that organizations may need develop more thoughtful and standardized data management policies that still remain flexible under the scope of FAIR data.