Objectives: The National Academy of Medicine identified diagnostic error as a pressing public health concern and defined failure to effectively communicate the diagnosis to patients as a diagnostic error. Leveraging Patient's Experience to improve Diagnosis (LEAPED) is a new program for measuring patient-reported diagnostic error. As a first step, we sought to assess the feasibility of using LEAPED after emergency department (ED) discharge.
Methods: We deployed LEAPED using a cohort design at three EDs within one academic health system. We enrolled 59 patients after ED discharge and queried them about their health status and understanding of the explanation for their health problems at 2-weeks, 1-month, and 3-months. We measured response rates and demographic/clinical predictors of patient uptake of LEAPED.
Results: Of those enrolled (n=59), 90% (n=53) responded to the 2-week post-ED discharge questionnaire (1 and 3-month ongoing). Of the six non-responders, one died and three were hospitalized at two weeks. The average age was 50 years (SD 16) and 64% were female; 53% were white and 41% were black. Over a fifth (23%) reported they were not given an explanation of their health problem on leaving the ED, and of those, a fourth (25%) did not have an understanding of what next steps to take after leaving the ED.
Conclusions: Patient uptake of LEAPED was high, suggesting that patient-report may be a feasible method of evaluating the effectiveness of diagnostic communication to patients though further testing in a broader patient population is essential. Future research should determine if LEAPED yields important insights into the quality and safety of diagnostic care.
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http://dx.doi.org/10.1515/dx-2020-0014 | DOI Listing |
Sci Rep
January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4515 McKinley Ave., St. Louis, MO, 63110, USA.
Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
January 2025
Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain. Electronic address:
The presence of cells in urine and in particular White Blood Cells (WBCs) is often associated with Urinary Tract Infections (UTIs) and other diseases. Non-invasive screening of WBCs requires the development of cost-effective point of care diagnostic tools. Infrared (IR) spectroscopy has the potential to identify and quantify cells in urine.
View Article and Find Full Text PDFInt J Surg Case Rep
January 2025
Department of Obstetrics and Gynecology, Moriya Daiichi General Hospital, Moriya, Ibaraki, Japan.
Introduction And Importance: Fallopian tube cancer, particularly the carcinosarcoma subtype, is a rare malignancy posing diagnostic challenges.
Case Presentation: Our patient was an 83-year-old, nulligravida woman, presented to our outpatient clinic with one month of pelvic pain. On examination, a pelvic mass was detected.
Sensors (Basel)
January 2025
Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety.
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