From the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan (M.L.B., M.R.M., J.V., X.T., B.L., D.A.C., N.S., S.K., L.S.) Department of Anaesthesiology, University Medical Center Goettingen, Goettingen, Germany (L.S.).
Anesthesiology. 2020 Apr;132(4):738-749. doi: 10.1097/ALN.0000000000003150.
Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures.
Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard.
Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text.
Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.
准确的麻醉程序代码数据对于麻醉实践中的质量改进、研究和报销任务至关重要。先进的数据科学技术，包括机器学习和自然语言处理，为开发麻醉程序中当前程序术语代码的分类工具提供了机会。
模型是使用包括来自 16 家学术和私立医院的 1,164,343 例手术的 Train/Test 数据集创建的。创建了五个有监督的机器学习模型来对麻醉科的当前程序术语代码进行分类，准确性定义为与围手术期数据库中存在的机构分配代码匹配的首选分类。对来自与 Train/Test 不同的单一机构的 Holdout 数据集进行进一步细化和测试的两个表现最佳的模型。创建了一个可调置信参数来识别模型高度准确的病例，目标是准确率至少为 95%，高于 2018 年医疗保险和医疗补助服务中心（马里兰州巴尔的摩）服务费的报告准确率。计费专家提交的实际索赔数据被用作参考标准。
支持向量机和神经网络标签嵌入注意模型分别是表现最好的模型，总体准确率分别为 87.9%和 84.2%（单个最佳代码）和 96.8%和 94.0%（前三个）。在 Train/Test 数据集中，支持向量机的分类准确率为 96.4%，在 47.0%的病例中使用标签嵌入注意模型的分类准确率为 94.4%。在 Holdout 数据集中，各自的分类准确率分别为 93.1%，在 58.0%的病例中，95.0%在 62.0%的病例中。模型训练中最重要的特征是程序文本。
通过应用机器学习和自然语言处理技术，为麻醉科当前程序术语代码分类创建了高度准确的实时模型。这种分类方法的处理速度提高和预先设定的准确性可能为依赖麻醉程序代码的质量改进、研究和报销任务提供性能优化和成本降低。
