Background: Intensive care is confronted with an increasing complexity and large amounts of data provided by new technological tools. One way of assisting health care professionals is providing effective clinical decision support (CDS) systems.
Objectives: The aim is to develop a tailored model for the sustainable development of a clinical decision support system in intensive care.
Methods: The model consists of two parts. The first part includes the interaction of the following partners: science industry and HCP. The second part comprises a three-phase process consisting of (1) the identification of clinical needs, (2) modeling and prototyping, and (3) implementation.
Results: By July 2015, a government funded CDS development project started in Graz, Austria. After assigning a multi-professional and interdisciplinary team, a clinical need statement was formulated within the first six months. A prototype was developed by end of 2016 and verified using a clinical dataset.
Conclusion: The developed model proofed to be feasible regarding the first two phases. Additional progress needs to made to assess the performance of the model in the implementation phase.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Adjuvant radiation improves survival outcomes in adrenocortical carcinoma: A population-based study.
Medicine (Baltimore)
January 2025
Department of Urology, Shiyan People's Hospital, Jinzhou Medical University Training Base, Shiyan, China.
The aim of this study was to evaluate the clinical benefits and outcomes of adjuvant radiation therapy on adrenocortical carcinoma (ACC) patients. All patients with ACC that were reported between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results database. A forward-stepwise Cox proportional hazards regression was used to identify independent risk factors.
View Article and Find Full Text PDFDevelopment and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection.
J Neurosurg
January 2025
Departments of1Neurosurgery.
Objective: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary.
View Article and Find Full Text PDFAI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFSurgical Scheduling Errors During Manual Data Transfer.
Qual Manag Health Care
January 2025
Author Affiliations: Source Healthcare, Santa Monica, California.
Background And Objectives: Retrospective studies examining errors within a surgical scheduling setting do not fully represent the effects of human error involved in transcribing critical patient health information (PHI). These errors can negatively impact patient care and reduce workplace efficiency due to insurance claim denials and potential sentinel events. Previous reports underscore the burden physicians face with prior authorizations which may lead to serious adverse events or the abandonment of treatment due to these delays.
View Article and Find Full Text PDFDelta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFWant 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!