Background: Medical errors occur frequently, yet they are often under-reported and strategies to increase the reporting of medical errors are lacking. In this work, we detail how a level 1 trauma center used a secure messaging application to track medical errors and enhance its quality improvement initiatives.
Methods: We describe the formulation, implementation, evolution, and evaluation of a chatroom integrated into a secure texting system to identify performance improvement and patient safety (PIPS) concerns. For evaluation, we used descriptive statistics to examine PIPS reporting by the reporting method over time, the incidence of mortality and unplanned ICU readmissions tracked in the hospital trauma registry over the same, and time-to-loop closure over the study period to quantify the impact of the processes instituted by the PIPS team. We also categorized themes of reported events.
Results: With the implementation of a PIPS chatroom, the number of events reported each month increased and texting became the predominant way for users to report trauma PIPS events. This increase in PIPS reporting did not appear to be accompanied by an increase in mortality and unplanned ICU readmissions. The PIPS team also improved the tracking and timely resolution of PIPS events and observed a decrease in time-to-loop closure with the implementation of the PIPS chatroom.
Conclusions: The adoption of clinical texting as a way to report PIPS events was associated with increased reporting of such events and more timely resolution of concerns regarding patient safety and healthcare quality.
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http://dx.doi.org/10.1177/25160435231190196 | DOI Listing |
J Am Soc Mass Spectrom
January 2025
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32611, United States.
Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, a reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) metabolomics data processing, validated with publicly available data from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS data processing to minimize human errors from manual data handling.
View Article and Find Full Text PDFSci Rep
January 2025
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove.
View Article and Find Full Text PDFBMJ Open
January 2025
Department of Emergency Medicine, St Michael's Hospital, Toronto, Ontario, Canada.
Introduction: Traumatic injuries are a significant public health concern globally, resulting in substantial mortality, hospitalisation and healthcare burden. Despite the establishment of specialised trauma centres, there remains considerable variability in trauma-care practices and outcomes, particularly in the initial phase of trauma resuscitation in the trauma bay. This stage is prone to preventable errors leading to adverse events (AEs) that can impact patient outcomes.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFFood Chem
December 2024
National Key Laboratory of Veterinary Public Health Security, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, and Beijing Laboratory for Food Quality and Safety, Beijing 100193, China; Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China. Electronic address:
Ovalbumin (OVA) is a high-risk allergen with complex tertiary structure in food samples. Here, we developed an accurate UPLC-MS/MS-based assay to improve OVA quantitative performance in processed foods. Full-length isotope-labeled OVA proteins (OVA-I) were synthesized using stable isotope labeling by amino acids in cell culture (SILAC) technique and employed as functional internal standards to ensure similar cleavage sites between internal standards and analytes.
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