A patient tracking system is a promising tool for managing patient flow and improving efficiency in the operating room. Wireless location systems, using infrared or radio frequency transmitters, can automatically timestamp key events, thereby decreasing the need for manual data input. In this study, we measured the accuracy and precision of automatically documented timestamps compared with manual recording. Each patient scheduled for urgent surgery was given an active radio frequency/infrared transmitter. The prototype software tracked the patient throughout the perioperative process, automatically documenting the timestamps. Both automatic and traditional data entry were compared with the reference data. The absolute value of median error was 64% smaller (P < 0.01), and the average quartile deviation of error was 69% smaller in automatic documentation. The average delay between an activity and the documentation was 80 seconds in automatic documentation and 735 seconds in manual documentation. Both the accuracy and the precision were better in automatic documentation and the data were immediately available. Automatic documentation with the Indoor Positioning System can help in managing patient flow and in increasing transparency with faster availability and better accuracy of data.
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http://dx.doi.org/10.1213/01.ane.0000196527.96964.72 | DOI Listing |
BMC Med Res Methodol
January 2025
Department of Gynecology-Obstetric and Reproductive Medicine, AP-HM, La Conception University teaching Hospital, 147 Boulevard Baille, Marseille, 13005, France.
Background: We aimed to develop and validate an algorithm for identifying women with polycystic ovary syndrome (PCOS) in the French national health data system.
Methods: Using data from the French national health data system, we applied the International Classification of Diseases (ICD-10) related diagnoses E28.2 for PCOS among women aged 18 to 43 years in 2021.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFGraefes Arch Clin Exp Ophthalmol
January 2025
Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany.
Purpose: Our study presents a virtual reality-based tangent screen test (VTS) to measure subjective ocular deviations including torsion in nine directions of gaze. The test was compared to the analogous Harms tangent screen test (HTS).
Methods: We used an Oculus Go controller and head-mounted-display with rotation sensors to measure patient's head orientation for the VTS.
PLoS One
January 2025
Institute of Systems Analysis and Informatics "A. Ruberti" (IASI), National Research Council of Italy, Rome, Italy.
Background And Objective: The growing availability of patient data from several clinical settings, fueled by advanced analysis systems and new diagnostics, presents a unique opportunity. These data can be used to understand disease progression and predict future outcomes. However, analysing this vast amount of data requires collaboration between physicians and experts from diverse fields like mathematics and engineering.
View Article and Find Full Text PDFBMC Genomics
December 2024
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, 02-106, Poland.
Low Complexity Regions (LCRs) are segments of proteins with a low diversity of amino acid composition. These regions play important roles in proteins. However, annotations describing these functions are dispersed across databases and scientific literature.
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