RESUMO
BACKGROUND: Precleaning is a key step in endoscopic reprocessing. AIM: To develop an effective and economic endoscope cleaning method by using a disposable endoscope bedside precleaning kit. METHODS: Altogether, 228 used gastrointestinal endoscopes were selected from five high-volume endoscopy units and precleaned by a traditional precleaning bucket (group T) or a disposable endoscope bedside precleaning kit (group D). Each group was further subdivided based on the replacement frequency of the cleaning solution, which was replaced every time in subgroups T1 and D1 and every several times in subgroups Ts and Ds. The adenosine triphosphate (ATP) level and residual proteins were measured three times: Before and after precleaning and after manual cleaning. RESULTS: After precleaning, the precleaning kit significantly reduced the ATP levels (P = 0.034) and has a more stable ATP clearance rate than the traditional precleaning bucket. The precleaning kit also saved a quarter of the cost of enzymatic detergent used during the precleaning process. After manual cleaning, the ATP levels were also significantly lower in the precleaning kit group than in the traditional precleaning bucket group (P < 0.05). Meanwhile, the number of uses of the cleaning solution (up to four times) has no significant impact on the cleaning effect (P > 0.05). CONCLUSION: Considering its economic cost and cleaning effect, the use of a disposable endoscope bedside precleaning kit can be an optimal option in the precleaning stage with the cleaning solution being replaced several times in the manual cleaning stage.
RESUMO
The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.