Background: Electronic health records (EHRs) may be used to assess quality of care.
Objective: To evaluate the accuracy of automated review of EHR data to measure quality of care for outpatients with heart failure.
Design: Observational study of quality of care for heart failure comparing automated review of EHR data with automated review followed by manual review of electronic notes for patients with apparent quality deficits (hybrid review).
Setting: An academic general internal medicine clinic with several years' experience using a commercial EHR.
Patients: 517 adults with a qualifying International Classification of Diseases, Ninth Revision, diagnosis of heart failure in their EHR data and 2 or more clinic visits over the past 18 months.
Measurements: Left ventricular ejection fraction (LVEF), prescription of a beta-blocker and an angiotensin-converting enzyme (ACE) inhibitor or angiotensin-receptor blocker (ARB) for patients with left ventricular systolic dysfunction (LVEF <0.40) and prescription of warfarin for patients with comorbid atrial fibrillation.
Results: Performance based on automated review of EHR data was similar to that based on hybrid review for assessing LVEF measurement (94.6% vs. 97.3%), prescription of beta-blockers (90.9% vs. 92.8%), and prescription of ACE inhibitors or ARBs (93.9% vs. 98.7%). However, performance based on automated review was lower than that based on hybrid review for prescription of warfarin for atrial fibrillation (70.4% vs. 93.6%), primarily because automated review did not detect documentation of accepted reasons for not prescribing warfarin.
Limitations: The findings may not be applicable to other practices and other EHRs. The authors used EHR data to identify eligible patients, so the study may have excluded some patients with heart failure. Patient charts were manually reviewed only if a provider appeared to fail a quality measure on automated review and did not determine the sensitivity and specificity of automated review according to standard definitions.
Conclusions: Automated review of EHR data to measure the quality of care of outpatients with heart failure missed many exclusion criteria for medications documented only in providers' notes. As a result, it sometimes underestimated performance on medication-based quality measures.
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http://dx.doi.org/10.7326/0003-4819-146-4-200702200-00006 | DOI Listing |
Langenbecks Arch Surg
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Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2-5-1 Shikata-Cho, Kita-Ku, Okayama City, Okayama, 700-8558, Japan.
Purpose: Gastric cancer (GC) remains a major malignancy. Robotic gastrectomy (RG) has gained popularity due to various advantages. Despite those advantages, many hospitals lack the necessary equipment for RG and are still performing laparoscopic gastrectomy (LG) due to its established minimal invasiveness and safety.
View Article and Find Full Text PDFSkelet Muscle
December 2024
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada.
Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
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J Robot Surg
December 2024
Department of Thyroid Surgery, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, P. R. China.
Since its introduction, robotic surgery has experienced rapid development and has been extensively implemented across various medical disciplines. It is crucial to comprehend the advancements in research and the evolutionary trajectory of its thematic priorities. This research conducted a bibliometric analysis on the literature pertaining to robotic surgery, spanning the period from 2014 to 2023, sourced from the Web of Science database.
View Article and Find Full Text PDFJ Clin Monit Comput
December 2024
Department of Anesthesia and Intensive Care, "Policlinico San Marco" University Hospital, Catania, Italy.
Echocardiography is crucial for evaluating patients at risk of clinical deterioration. Left ventricular ejection fraction (LVEF) and velocity time integral (VTI) aid in diagnosing shock, but bedside calculations can be time-consuming and prone to variability. Artificial intelligence technology shows promise in providing assistance to clinicians performing point-of-care echocardiography.
View Article and Find Full Text PDFNeurosurg Rev
December 2024
Neurological Surgery, Thomas Jefferson University, Philadelphia, PA, US.
Objective: In recent years, the application of robotic assistance in diagnostic and therapeutic endovascular neurointerventional procedures has gained notable attention. In this systematic review and meta-analysis, we aim to evaluate the feasibility, safety, and current indications of robotic-assisted neurointerventions and to assess the degree of robotic assistance and reasons for unplanned manual conversion from robotic assistance.
Methods: We searched Medline, Scopus, Web of Science, and Cochrane Library databases following PRISMA guidelines and included studies with ≥ 4 patients reporting on robotic-assisted neurointerventions.
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