Study Objective: Improve the efficiency of an inpatient clinical decision support tool (CDS) for patients with adult congenital heart disease (ACHD).
Design: The efficiency of a CDS was evaluated across two time periods and compared.
Setting: An academic, tertiary care center.
Participants: ACHD patients roomed in an inpatient setting.
Intervention: Plan-Do-Study-Act (PDSA) methods were applied starting in 2021 and included refinement of diagnostic codes and the addition of department encounter codes.
Main Outcome Measures: True positive and false positive CDS alerts.
Results: Baseline data from 2017 had a median (IQR) of 38 (17) and 2019 baseline data had 65 (19) total alerts per month. Combining both baseline data years, the median true positive CDS alerts was 47.3 %. There were 71 (6) total alerts per month for the 2021-2022 time period and with ongoing PDSA cycles and optimization in the CDS the true positive alerts improved substantially resulting in a shifting of the median to 78.9 % within 9 months.
Conclusion: CDS can efficiently notify providers when an ACHD patient is encountered. The use of ICD 10 codes alone to identify ACHD patients has limited accuracy with a high proportion of false positives. Ongoing revision of the CDS system methods is important to improving efficiency and minimizing provider alert fatigue.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10945959 | PMC |
| http://dx.doi.org/10.1016/j.ahjo.2023.100303 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
DisCo P-ad: Distance-Correlation-Based -Value Adjustment Enhances Multiple Testing Corrections for Metabolomics.
Metabolites
January 2025
Department of Biostatistics & Informatics, Colorado School of Public Heath, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Background: Due to scientific advancements in high-throughput data production technologies, omics studies, such as genomics and metabolomics, often give rise to numerous measurements per sample/subject containing several noisy variables that potentially cloud the true signals relevant to the desired study outcome(s). Therefore, correcting for multiple testing is critical while performing any statistical test of significance to minimize the chances of false or missed discoveries. Such correction practice is commonplace in genome-wide association studies (GWAS) but is also becoming increasingly relevant to metabolome-wide association studies (MWAS).
View Article and Find Full Text PDFExploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.
J Imaging
December 2024
Technology Department, CERN, 1211 Geneva, Switzerland.
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).
View Article and Find Full Text PDFRole of Virtual iMRI in Glioblastoma Surgery: Advantages, Limitations, and Correlation with iCT and Brain Shift.
Brain Sci
December 2024
Department of Medical and Surgical Sciences and Advanced Technologies "G.F. Ingrassia", Neurological Surgery, Policlinico "G. Rodolico-San Marco" University Hospital, University of Catania, 95124 Catania, Italy.
: Elastic image fusion (EIF) using an intraoperative CT (iCT) scan may enhance neuronavigation accuracy and compensate for brain shift. : To evaluate the safety and reliability of the EIF algorithm (Virtual iMRI Cranial 4.5, Brainlab AG, Munich Germany, for the identification of residual tumour in glioblastoma surgery.
View Article and Find Full Text PDFBiomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification.
Bioengineering (Basel)
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.
As the leading cause of dementia worldwide, Alzheimer's Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18).
View Article and Find Full Text PDFEnhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.
Bioengineering (Basel)
January 2025
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Objective: This study evaluated the applicability of the classical method, height loss ratio (HLR), for identifying major acute compression fractures in clinical practice and compared its performance with deep learning (DL)-based VCF detection methods. Additionally, it examined whether combining the HLR with DL approaches could enhance performance, exploring the potential integration of classical and DL methodologies.
Methods: End-to-End VCF Detection (EEVD), Two-Stage VCF Detection with Segmentation and Detection (TSVD_SD), and Two-Stage VCF Detection with Detection and Classification (TSVD_DC).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!