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https://www.selleckchem.com/products/a-769662.html Urinary tract infection (UTI) is common in home care but not easily captured with standard assessment. This study aimed to examine the value of nursing notes in detecting UTI signs and symptoms in home care. The study developed a natural language processing (NLP) algorithm to automatically identify UTI-related information in nursing notes. Home care visit notes (n= 1,149,586) and care coordination notes (n=1,461,171) for 89,459 patients treated in the largest nonprofit home care agency in the United States during2014. We generated 6 categories of UTI-related information from literature and used the Unified Medical Language System (UMLS) to identify a preliminary list of terms. The NLP algorithm was tested on a gold standard set of 300 clinical notes annotated by clinical experts. We used structured Outcome and Assessment Information Set data to extract the frequency of UTI-related emergency department (ED) visits or hospitalizations and explored time-patterns in documentation of UTI-related informatioonsider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction. Information in nursing notes is often overlooked by stakeholders and not integrated into predictive modeling for decision-making support, but our findings highlight their value in early risk identification and care guidance. Health care administrators should consider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction. This study compared quality indicators across linguistic groups and sought to determine whether disparities are influenced by resident-facility language discordance in long-term care. Population-based retrospective cohort study using linked databases. Retrospective cohort of newly admitted residents of long-term care facilities in Ontario, Canada,

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