Boniface Keith S, Ogle Kat, Aalam Ahmad, LeSaux Maxine, Pyle Matt, Mandoorah Sohaib, Shokoohi Hamid
Department of Emergency Medicine The George Washington University Washington DC.
Present address: Department of Emergency Medicine Central Michigan University Saginaw MI.
AEM Educ Train. 2019 Feb 19;3(2):172-178. doi: 10.1002/aet2.10324. eCollection 2019 Apr.
Competency assessment is a key component of point-of-care ultrasound (POCUS) training. The purpose of this study was to design a smartphone-based standardized direct observation tool (SDOT) and to compare a faculty-observed competency assessment at the bedside with a blinded reference standard assessment in the quality assurance (QA) review of ultrasound images.
In this prospective, observational study, an SDOT was created using SurveyMonkey containing specific scoring and evaluation items based on the Council of Emergency Medicine Residency-Academy of Emergency Ultrasound: Consensus Document for the Emergency Ultrasound Milestone Project. Ultrasound faculty used the mobile phone-based data collection tool as an SDOT at the bedside when students, residents, and fellows were performing one of eight core POCUS examinations. Data recorded included demographic data, examination-specific data, and overall quality measures (on a scale of 1-5, with 3 and above being defined as adequate for clinical decision making), as well as interpretation and clinical knowledge. The POCUS examination itself was recorded and uploaded to QPath, a HIPAA-compliant ultrasound archive. Each examination was later reviewed by another faculty blinded to the result of the bedside evaluation. The agreement of examinations scored adequate (3 and above) in the two evaluation methods was the primary outcome.
A total of 163 direct observation evaluations were collected from 23 EM residents (93 SDOTs [57%]), 14 students (51 SDOTs [31%]), and four fellows (19 SDOTs [12%]). The trainees were evaluated on completing cardiac (54 [33%]), focused assessment with sonography for trauma (34 [21%]), biliary (25 [15%]), aorta (18 [11%]), renal (12 [7%]), pelvis (eight [5%]), deep vein thrombosis (seven [4%]), and lung scan (5 [3%]). Overall, the number of observed agreements between bedside and QA assessments was 81 (87.1% of the observations) for evaluating the quality of images (scores 1 and 2 vs. scores 3, 4, and 5). The strength of agreement is considered to be "fair" (κ = 0.251 and 95% confidence interval [CI] = 0.02-0.48). Further agreement assessment demonstrated a fair agreement for images taken by residents and students and a "perfect" agreement in images taken by fellows. Overall, a "moderate" inter-rater agreement was found in 79.1% for the accuracy of interpretation of POCUS scan (e.g., true positive, false negative) during QA and bedside evaluation (κ = 0.48, 95% CI = 0.34-0.63). Faculty at the bedside and QA assessment reached a moderate agreement on interpretations noted by residents and students and a "good" agreement on fellows' scans.
Using a bedside SDOT through a mobile SurveyMonkey platform facilitates assessment of competency in emergency ultrasound learners and correlates well with traditional competency evaluation by asynchronous weekly image review QA.
能力评估是床旁超声(POCUS)培训的关键组成部分。本研究的目的是设计一种基于智能手机的标准化直接观察工具(SDOT),并在超声图像质量保证(QA)审查中,将床边教师观察到的能力评估与盲法参考标准评估进行比较。
在这项前瞻性观察研究中,使用SurveyMonkey创建了一个SDOT,其中包含基于急诊医学住院医师协会 - 急诊超声学会的《急诊超声里程碑项目共识文件》的特定评分和评估项目。当学生、住院医师和研究员进行八项核心POCUS检查之一时,超声教员在床边使用基于手机的数据收集工具作为SDOT。记录的数据包括人口统计学数据、特定检查数据和整体质量指标(范围为1 - 5,3及以上被定义为足以用于临床决策)以及解读和临床知识。POCUS检查本身被记录并上传到符合健康保险流通与责任法案(HIPAA)的超声存档QPath中。随后,另一位对床边评估结果不知情的教员对每次检查进行审查。两种评估方法中评定为合格(3及以上)的检查的一致性是主要结果。
共收集了来自23名急诊医学住院医师(93次SDOT评估[57%])、14名学生(51次SDOT评估[31%])和4名研究员(19次SDOT评估[12%])的163次直接观察评估。对学员进行的检查包括心脏检查(54次[33%])、创伤超声重点评估(34次[21%])、胆道检查(25次[15%])、主动脉检查(18次[11%])、肾脏检查(12次[7%])、骨盆检查(8次[5%])、深静脉血栓检查(7次[4%])和肺部扫描(5次[3%])。总体而言,在评估图像质量(评分1和2与评分3、4和5)时,床边评估和QA评估之间观察到的一致次数为81次(占观察次数的87.1%)。一致性强度被认为是“中等”(κ = 0.251,95%置信区间[CI] = 0.02 - 0.48)。进一步的一致性评估表明,住院医师和学生所拍摄图像的一致性为中等,而研究员所拍摄图像的一致性为“完美”。总体而言,在QA和床边评估期间,POCUS扫描解读准确性(如真阳性、假阴性)的评分者间一致性为79.1%,为“中等”(κ = 0.48,95% CI = 0.34 - 0.63)。床边教员和QA评估在住院医师和学生的解读方面达成了中等一致性,在研究员的扫描方面达成了“良好”一致性。
通过移动SurveyMonkey平台使用床边SDOT有助于评估急诊超声学习者的能力,并且与通过每周异步图像审查QA进行的传统能力评估具有良好的相关性。