Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning
Conclusions 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 anda 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.Editor ’s PerspectiveWhat We Already Know about This TopicThe ability to process anesthesiology procedure code data in an accurate manner is important for clinical and research considerations. Advanced data science techniques present opportunities to improve coding and develop classification tools.What This Article Tells Us That Is NewThe application of machine learning and natural language processing techniques facilitate a more rapid creation of accurate real-time models for Current Procedural Terminology code classification. The potential benefits of this approach include performance optimization and cost reduction for quality improvement, research, and reimbursement tasks that rely on anesthesiology procedure codes.
Source: Anesthesiology - Category: Anesthesiology Source Type: research
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