Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication
This work addresses the labor-intensive and error-prone task of coding medical transcripts to improve patient-centered communication in healthcare, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of manually coding physician-patient transcripts for patient-centered communication by developing an automatic coding approach using machine learning algorithms, achieving evidence that the method can distinguish codes sufficiently for training human annotators.
This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Naïve Bayes, Random Forest, and Support Vector Machine) to categorize lines in transcripts into corresponding codes. The result showed that there is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators.