Background: Studies of the effects of electronic health records (EHRs) have had mixed findings, which may be attributable to unmeasured confounders such as individual variability in use of EHR features.
Objective: To capture physician-level variations in use of EHR features, associations with other predictors, and usage intensity over time.
Methods: Retrospective cohort study of primary care providers eligible for meaningful use at a network of federally qualified health centers, using commercial EHR data from January 2010 through June 2013, a period during which the organization was preparing for and in the early stages of meaningful use.
Results: Data were analyzed for 112 physicians and nurse practitioners, consisting of 430,803 encounters with 99,649 patients. EHR usage metrics were developed to capture how providers accessed and added to patient data (eg, problem list updates), used clinical decision support (eg, responses to alerts), communicated (eg, printing after-visit summaries), and used panel management options (eg, viewed panel reports). Provider-level variability was high: for example, the annual average proportion of encounters with problem lists updated ranged from 5% to 60% per provider. Some metrics were associated with provider, patient, or encounter characteristics. For example, problem list updates were more likely for new patients than established ones, and alert acceptance was negatively correlated with alert frequency.
Conclusions: Providers using the same EHR developed personalized patterns of use of EHR features. We conclude that physician-level usage of EHR features may be a valuable additional predictor in research on the effects of EHRs on healthcare quality and costs.
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http://dx.doi.org/10.1136/amiajnl-2013-002627 | DOI Listing |
Background: People with Alzheimer's disease (AD) exhibit varying clinical trajectories. There is a need to predict future AD-related outcomes such as morbidity and mortality using clinical profile at the point of care.
Objective: To stratify AD patients based on baseline clinical profiles (up to two years prior to AD diagnosis) and update the model after AD diagnosis to prognosticate future AD-related outcomes.
BMC Health Serv Res
January 2025
School of Pharmacy and Biomolecular Sciences (PBS), Royal College of Surgeons in Ireland (RCSI), 1st Floor Ardilaun House Block B, 111 St Stephen's Green, Dublin 2, Ireland.
Background: The advantages of electronic health records (EHRs) are well-documented regarding the process of care, enhanced data accessibility and cost savings. However, EHR design can also contribute to usability challenges, with poorly designed EHRs being implicated in user errors including patient overdoses. Our study seeks to evaluate how EHR design influences both usability and medication safety.
View Article and Find Full Text PDFJ Drugs Dermatol
January 2025
Background: The prevalence of burnout among United States (US) dermatologists has surged, reaching 49% in 2023, with a growing volume of bureaucratic tasks (eg, charting, paperwork) the leading factor behind professional fatigue. We seek to explore the competitive landscape and efficacy of AI-powered patient documentation to alleviate burnout among dermatologists by optimizing documentation practices while maintaining accuracy.
Methods: We conducted a review of eighteen AI-powered automated documentation products available in the current healthcare landscape, focusing on their integration with electronic health record (EHR) systems, HIPAA compliance, language support, mobile accessibility, and consumer type.
Diabetes Technol Ther
January 2025
Children's Mercy Kansas City, Endocrinology, Kansas City, Missouri, USA.
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.
View Article and Find Full Text PDFFront Pharmacol
December 2024
Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.
Introduction: The Precision Medicine Program (PMP) at the University of Florida (UF) focuses on advancing pharmacogenomics (PGx) to improve patient care.
Methods: The UF PMP, in collaboration with the UF Health Pathology Laboratory (UFHPL), utilized Health Level Seven (HL7) standards to integrate PGx data into Epic's Genomic Module to enhance the management and utilization of PGx data in clinical practice.
Results: A key feature of the Genomic Module is the introduction of genomic indicators-innovative tools that flag actionable genetic information directly within the electronic health record (EHR).
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