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[Retracted] Quality Control in the Clinical Medical Laboratory Based on Mobile Medical Edge Computing

2022·1 Zitationen·Contrast Media & Molecular ImagingOpen Access
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1

Zitationen

3

Autoren

2022

Jahr

Abstract

Today, the IT departments of many healthcare organizations are suffering from data storage and network speed. Doctors diagnose patients to help them out, and this treatment process needs to be well managed. Clinical trial data are the main basis for judging the patient's condition in the medical process. With the rapid development of clinical laboratory technology in China, important achievements have been made in medicine. Clinical hospitals use a large number of different types of testing reagents and equipment, and the accuracy of testing data has become a key issue for testing results. The accuracy of testing data comes from the zero error of clinical testing, derived from the rigor and operational rigor of the testers in clinical testing. This article is dedicated to the quality control analysis and research based on mobile medical edge computing in clinical trials. This article introduces the relevant theories of mobile medical edge computing technology and quality control methods, with reference to the study on the general medical students' clinical competency in problem-solving, communication skills, procedure, history, and physical examination and critical and non-critical indicators in objective structured clinical examination (OSCE). In addition, a routine testing group and a quality control group for strengthening important aspects of medical testing were designed to conduct comparative experiments. Experiments have proved that the quality control group for strengthening the important links of medical testing has a higher index than the routine testing group in all aspects. Among them, the detection accuracy rate can reach up to 99.48%, which is of great significance for the improvement of the detection link of the patient's condition and the follow-up diagnosis and treatment link.

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Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareTechnology and Human Factors in Education and Health
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