Study shows potential for using artificial intelligence tools to detect healthcare-associated infections
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Using artificial intelligence to monitor healthcare-associated infections
emphasize
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background
surveillance healthcare associated infections (IAAS) is critical to the safety of healthcare environments. Helping identify risk factors for infection can improve patient safety and quality of care. However, nosocomial infection surveillance is complex and requires specialized knowledge and resources.This study investigated the use of AI (artificial intelligence), especially Large language generation modelimprove HAI monitoring.
method
We evaluated 2 AI agents, chatGPT plus (GPT-4) from OpenAI and a local model based on Mixtral 8×7b, to see their recognition capabilities Central venous catheter-associated bloodstream infection (CLABSI) and Catheter-related urinary tract infection (CAUTI) 6 training scenarios for the National Health Insurance Network. The complexity of these scenarios was analyzed, and responses were compared with expert opinion.
result
Given clear prompts, both AI models were able to accurately identify CLABSI and CAUTI in all scenarios. When challenges arise, the prompts are unclear and include Arabic numerals for dates, abbreviations, and special characters, leading to occasional inaccuracies on repeated tests.
discuss
This study demonstrates the potential of artificial intelligence to accurately identify HAIs such as CLABSI and CAUTI. Clear and specific instructions are critical for reliable AI responses, highlighting the need for human supervision in AI-assisted hospital infection surveillance.
in conclusion
Artificial intelligence shows promise in improving hospital infection surveillance, potentially streamlining tasks and freeing up health care workers to engage in patient-centered activities. Effective use of AI requires user education and continuous improvement of AI models.
Comment
Tools like ChatGPT can improve infection surveillance and keep patients safer in healthcare settings
a new one Research proof of concept Published on American Journal of Infection Control (AJIC) Report says artificial intelligence (AI) technology can accurately identify cases healthcare associated infections (HAI) even in complex clinical scenarios. The study highlights the need for clear and consistent language when using AI tools for this purpose, illustrating the potential for incorporating AI technology into a cost-effective component of routine infection surveillance programmes.
According to the latest HAI hospital prevalence survey conducted by the Centers for Disease Control and Prevention, there were approximately 687,000 healthcare-associated infections (HAIs) in U.S. acute care hospitals in 2015 and 72,000 related deaths among hospitalized patients. Approximately 3% of hospitalized patients have at least one healthcare-associated infection (HAI) at any given time. Implementation of infection surveillance programs and other infection prevention protocols has reduced the incidence of HAIs, but they remain a risk, especially for critically ill hospitalized patients who have devices such as central lines, catheters, or breathing tubes inserted.
Many hospitals and other healthcare facilities have healthcare-associated infection (HAI) surveillance programs to detect increased risk of infection, but their maintenance requires significant resources, training, and experience. In resource-limited settings, cost-effective alternatives can help improve surveillance programs and better protect high-risk patients.