J Med Syst
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Healthcare organisations and governments have invested heavily in electronic health records in anticipation that they will deliver improved health outcomes for consumers and efficiencies across emergency departments. Despite such investment, electronic health records designed to support emergency care have been poorly evaluated. Given the accelerated development and adoption of information technology across healthcare, it is timely that a systematic review of this evidence base is updated in order to drive improvements to design, interoperability and overall clinical utility of electronic health record systems implemented in emergency departments. ⋯ The most frequently reported findings were efficiencies, including reductions in diagnostic tests, imaging and costs. This review is the first to report moderate to significant increases in admission rates are associated with electronic health record use in the emergency department, contrasting the findings of previous reviews. Diversity in the methodology employed across the included studies emphasises the need for further research to examine the impact of electronic health record implementation and system design on the findings reported, in order to ensure return on investment for stakeholders and optimised consumer care.
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Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.
The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. ⋯ This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/ ).