Department of Anesthesiology and Perioperative Medicine, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Int J Qual Health Care. 2021 Aug 5;33(3). doi: 10.1093/intqhc/mzab113.
While the American Society of Anesthesiologists (ASA) Physical Status (PS) is used to adjust for greater mortality risk with higher ASA PS classification, inaccurate classification can lead to an inaccurate comparison of institutions.
The purpose of this study was to assess the effect of audit and feedback with a rule-based artificial intelligence algorithm on the accuracy of ASA PS classification.
We reviewed 78 121 anesthetic records from 1 January 2017 to 19 February 2020. The first intervention entailed audit and feedback emphasizing accurately documenting ASA PS classification using body mass index (BMI), while the second intervention consisted of implementing a rule-based artificial intelligence algorithm. If a patient with a BMI ≥40 kg/m2 had a documented ASA PS classification of 1 or 2, the provider was alerted to change the ASA PS classification to 3 or above. The primary outcome was the overall proportion of patients with inaccurate ASA PS classification based on BMI per month. Secondary outcomes included the proportion of patients with a BMI ≥40 or a BMI 30-39.9 who had inaccurate ASA PS classification and the proportion of patients documented as having ASA 3-5. Data were analyzed using interrupted time-series analysis.
For the primary outcome, the slope for ASA PS classification inaccurately incorporating BMI was unchanging before the first intervention (parameter coefficient 0.002, 95% CI -0.034 to 0.038; P = 0.911). Following the first intervention, there was an immediate level change (parameter coefficient -0.821, 95% CI -1.236 to -0.0406; P < 0.001) without significant change in slope (parameter coefficient -0.048, 95% CI -0.100 to 0.004; P = 0.067). The post-intervention slope was negative (parameter coefficient -0.046, 95% CI -0.083 to -0.009; P = 0.017). Following the second intervention, there was no level change (parameter coefficient 0.203, 95% CI -0.380 to 0.463; P = 0.839) and no significant change in slope (parameter coefficient 0.013, 95% CI -0.043 to 0.043; P = 0.641). The post-intervention slope was not significant (parameter coefficient -0.034, 95% CI -0.078 to 0.010; P = 0.121). The proportion of patients whose ASA PS classification inaccurately incorporated BMI at the first and final timepoint of the study was 2.6% and 0.8%, respectively.
Our quality improvement efforts successfully modified clinician behavior to accurately incorporate BMI into the ASA PS classification. By combining audit and feedback methodology with a rule-based artificial intelligence algorithm, we created a process that resulted in immediate and sustained effects. Improving ASA PS classification accuracy is important because it affects quality metrics, research design, resource allocation and workflow processes.
美国麻醉师协会（ASA）身体状况（PS）用于调整更高的 ASA PS 分类的死亡率风险，但不准确的分类会导致机构之间的比较不准确。
本研究的目的是评估基于规则的人工智能算法的审核和反馈对 ASA PS 分类准确性的影响。
我们回顾了 2017 年 1 月 1 日至 2020 年 2 月 19 日期间的 78121 份麻醉记录。第一次干预包括审核和反馈，强调使用身体质量指数（BMI）准确记录 ASA PS 分类，而第二次干预则包括实施基于规则的人工智能算法。如果 BMI≥40kg/m2 的患者记录的 ASA PS 分类为 1 或 2，则提醒提供者将 ASA PS 分类更改为 3 或更高。主要结果是基于 BMI 的患者不准确 ASA PS 分类的总体比例，每月一次。次要结果包括 BMI≥40 或 BMI 30-39.9 的患者不准确 ASA PS 分类的比例和记录为 ASA 3-5 的患者比例。使用中断时间序列分析进行数据分析。
对于主要结果，在第一次干预之前，不准确地将 BMI 纳入 ASA PS 分类的斜率保持不变（参数系数 0.002，95%CI-0.034 至 0.038；P=0.911）。在第一次干预之后，立即发生了水平变化（参数系数-0.821，95%CI-1.236 至-0.0406；P<0.001），斜率没有明显变化（参数系数-0.048，95%CI-0.100 至 0.004；P=0.067）。干预后的斜率为负（参数系数-0.046，95%CI-0.083 至-0.009；P=0.017）。在第二次干预后，没有水平变化（参数系数 0.203，95%CI-0.380 至 0.463；P=0.839），斜率也没有明显变化（参数系数 0.013，95%CI-0.043 至 0.043；P=0.641）。干预后的斜率没有意义（参数系数-0.034，95%CI-0.078 至 0.010；P=0.121）。在研究的第一和最后时间点，不准确地将 BMI 纳入 ASA PS 分类的患者比例分别为 2.6%和 0.8%。
我们的质量改进工作成功地改变了临床医生的行为，使其能够准确地将 BMI 纳入 ASA PS 分类。通过将审核和反馈方法与基于规则的人工智能算法相结合，我们创建了一个可以立即产生持续效果的流程。提高 ASA PS 分类的准确性很重要，因为它会影响质量指标、研究设计、资源分配和工作流程。
