Steps that hospitals should take to ensure they are gaining optimal value from their electronic health record (EHR) systems include: Creating a value framework for EHR implementation a value framework or EHR implementation. Creating and build executive understanding of the framework. Quantifying each of the expected benefits of EHR implementation. Creating a cross-functional executive team to lead investments in performance management related to the EHR system. Aligning incentives through a formal physician engagement strategy.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.
BMC Anesthesiol
January 2025
Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Background: Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.
Methods: Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test).
Association of an HIV-Prediction Model with Uptake of Pre-Exposure Prophylaxis (PrEP).
Appl Clin Inform
January 2025
Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, The University of Texas Southwestern Medical Center, Dallas, United States.
Background: Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.
View Article and Find Full Text PDFApplication of an Externally Developed Algorithm to Identify Research Cases and Controls from Electronic Health Record Data: Failures and Successes.
Appl Clin Inform
January 2025
Divison of Quantitative and Clinical Sciences, Vanderbilt University Medical Center, Nashville, United States.
Background: The use of Electronic Health Records (EHRs) in research demands robust, interoperable systems. By linking biorepositories to EHR algorithms, researchers can efficiently identify cases and controls for large observational studies (e.g.
View Article and Find Full Text PDFEfficacy of a clinical decision support tool to promote guideline concordant evaluations in patients with high-risk microscopic hematuria: a cluster randomized quality improvement project.
J Urol
January 2025
Department of Population Health, NYU Grossman School of Medicine, New York, New York.
Purpose: We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record (EHR) of a multi-site academic medical center increased the proportion of patients with American Urological Association (AUA) "high risk" microscopic hematuria (MH) who receive guideline concordant evaluations.
Materials And Methods: We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with 'high-risk' MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result.
Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.
Front Med (Lausanne)
January 2025
Department of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection.
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!