心房颤动
危险分层
电子病历
分层(种子)
医学
中心(范畴论)
病历
临床决策
急诊医学
医疗急救
重症监护医学
内科学
化学
休眠
发芽
种子休眠
生物
植物
结晶学
作者
Stephanie Lively,Christina E. DeRemer,Micaele Carroll,William Maddox,Maribeth H. Johnson,Adam E. Berman
标识
DOI:10.19102/icrm.2016.070802
摘要
Risk assessment of potential stroke in patients with atrial fibrillation (AF) is a fundamental component of AF disease management.Electronic medical record (EMR) use affords clinicians easier access to key cardiovascular risk factors when treating AF patients.The primary focus of this study was to evaluate documented risk assessment via widely accepted risk stratification schemes and subsequent appropriate anticoagulant therapy in AF patients at an academic medical center.We performed a retrospective chart review of hospitalized adult patients possessing a discharge diagnosis of AF.In the event of multiple admissions, the patient's initial hospitalization within the specified time period was evaluated.Patients identified were separated into those initiated on oral anticoagulation (OAC) during hospitalization and those who were not.Patients not started on OAC therapy were analyzed to determine the appropriateness of withholding OAC agents.Of 262 OAC-eligible patients not receiving anticoagulation, CHADS 2 was documented in the EMR for 60 patients (23%), and CHA 2 DS 2 -VASc was documented for nine patients (3%).Three patients (1%) not receiving anticoagulation had both CHADS 2 and CHA 2 DS 2 -VASc documented.Based on calculated risk stratification and the documented presence of contraindications, 32 of 262 patients (12%) were considered to have OAC therapy inappropriately omitted.Hypertension emerged as the single predictor of inappropriate omission of OAC therapy in these patients.Despite EMR implementation, a significant number of OAC-eligible AF patients do not receive appropriate anticoagulant therapy.EMR-based clinical decision support tools boosting awareness of patient OAC eligibility may improve compliance with guideline-based AF treatment strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI