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Background: The Joint Commission lists improving staff communication (handoffs) as part of several National Safety Goals. In this study, we developed an electronic web-based charting system for clinical pathology handoffs, which primarily consist of transfusion medicine calls, and evaluated the advantages over a paper-based handwritten call log.
Materials And Methods: A secure online web browser application using Research Electronic Data Capture (REDCap) was designed to document on-call pathology resident consults. A year after implementation, an online survey was administered to our pathology residents in order to evaluate and compare the usability of the electronic application (e-consults) to the previous handwritten call log, which was a notebook where trainees hand wrote different components of the consult.
Results: The REDCap web-based application includes discrete fields for patients' information, requesting physician contact, type of consult, action items for follow-up and faculty responses, as well as other information. These components have eventually progressed to be an online consult call catalog. With approximately 1079 consults per year, transfusion medicine-related calls account for ~90% of the encounters, while clinical chemistry, microbiology and immunology calls constitute the remainder. The overall response rate of the survey was 96% (29 of 30 participants). Of the 16 respondents who experienced both call log systems, 100% responded that REDCap was an improvement over the handwritten call log (P < 0·0001).
Conclusion: E-consult documentation entered into a web-based application was a user-friendly, secure clinical information access and effective handoff system as compared to a paper-based handwritten call log.
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http://dx.doi.org/10.1111/vox.12913 | DOI Listing |
Forensic Sci Int
August 2025
Department of Telecommunications, Brno University of Technology, Brno, Technická 3082/12, 61600, Czech Republic.
Many tasks in forensic examination of handwritten documents require classification of writing instruments that have ink of similar properties as the ink found on a questioned document. In this paper, we propose a new methodology for non-destructive identification of inks based on optical properties and reflectance spectra of the ink, measured from handwriting strokes. Building on this methodology, we developed an interactive database that we call the "Pen Ink Library", which lists 718 various writing instruments and enables systematic comparison and semi-automatic search of writing instruments, using the measured characteristics of their ink.
View Article and Find Full Text PDFEpidemiol Health
May 2023
Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea.
Objectives: In Korea, contact tracing for coronavirus disease 2019 is conducted using information from credit card records, handwritten visitor logs, KI-Pass (QR code), and the Safe Call system after an interview. We investigated the usefulness of these tools for contact tracing.
Methods: An anonymous survey was conducted for 2 months (July to September 2021) among contact tracers throughout Korea.
J Imaging
May 2022
Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva 8410501, Israel.
Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified.
View Article and Find Full Text PDFmSphere
September 2020
MRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching.
View Article and Find Full Text PDFVox Sang
July 2020
Department of Pathology & Laboratory Medicine, Emory University Hospital, Emory University School of Medicine, Atlanta, GA, USA.