Department of Anesthesiology, Department of Biomedical Informatics, Vanderbilt University Medical Center, United States.
Department of Biomedical Informatics, Vanderbilt University Medical Center, United States.
J Clin Anesth. 2021 Feb;68:110114. doi: 10.1016/j.jclinane.2020.110114. Epub 2020 Nov 1.
A challenge in reducing unwanted care variation is effectively managing the wide variety of performed surgical procedures. While an organization may perform thousands of types of cases, privacy and logistical constraints prevent review of previous cases to learn about prior practices. To bridge this gap, we developed a system for extracting key data from anesthesia records. Our objective was to determine whether usage of the system would improve case planning performance for anesthesia residents.
Randomized, cross-over trial.
Vanderbilt University Medical Center.
We developed a web-based, data visualization tool for reviewing de-identified anesthesia records. First year anesthesia residents were recruited and performed simulated case planning tasks (e.g., selecting an anesthetic type) across six case scenarios using a randomized, cross-over design after a baseline assessment. An algorithm scored case planning performance based on care components selected by residents occurring frequently among prior anesthetics, which was scored on a 0-4 point scale. Linear mixed effects regression quantified the tool effect on the average performance score, adjusting for potential confounders.
We analyzed 516 survey questionnaires from 19 residents. The mean performance score was 2.55 ± SD 0.32. Utilization of the tool was associated with an average score improvement of 0.120 points (95% CI 0.060 to 0.179; p < 0.001). Additionally, a 0.055 point improvement due to the "learning effect" was observed from each assessment to the next (95% CI 0.034 to 0.077; p < 0.001). Assessment score was also significantly associated with specific case scenarios (p < 0.001).
This study demonstrated the feasibility of developing of a clinical data visualization system that aggregated key anesthetic information and found that the usage of tools modestly improved residents' performance in simulated case planning.
减少不必要的护理差异的一个挑战是有效地管理执行的各种手术程序。虽然一个组织可能进行数千种类型的手术，但隐私和后勤方面的限制使他们无法审查以前的病例来了解之前的实践。为了弥补这一差距，我们开发了一种从麻醉记录中提取关键数据的系统。我们的目标是确定该系统的使用是否会提高麻醉住院医师的病例规划表现。
随机交叉试验。
范德比尔特大学医学中心。
我们开发了一个基于网络的数据可视化工具，用于查看去识别的麻醉记录。首先，我们招募了一年级麻醉住院医师，并在基线评估后使用随机交叉设计进行了六个病例场景的模拟病例规划任务（例如，选择麻醉类型）。根据算法对居民经常选择的护理组件进行评分，这些组件在 0-4 分的评分范围内进行评分。线性混合效应回归量化了工具对平均表现评分的影响，同时调整了潜在的混杂因素。
我们分析了 19 名住院医师的 516 份调查问卷。平均表现评分为 2.55 ± 0.32。使用该工具与平均得分提高 0.120 分（95%置信区间 0.060 至 0.179；p < 0.001）相关。此外，从每次评估到下一次评估，还观察到由于“学习效应”导致的 0.055 分的提高（95%置信区间 0.034 至 0.077；p < 0.001）。评估得分也与特定的病例场景显著相关（p < 0.001）。
这项研究证明了开发一种临床数据可视化系统的可行性，该系统可以汇总关键的麻醉信息，并发现工具的使用在模拟病例规划中适度提高了住院医师的表现。
