Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Automated clinical transcription for behavioral health clinicians(Montana State University - Bozeman, College of Engineering, 2022) Kazi, Nazmul Hasan; Chairperson, Graduate Committee: Brendan Mumey; This is a manuscript style paper that includes co-authored chapters.Mental health disorder is one of the most common but expensive healthcare conditions in the world. Yet, more than half of all patients go untreated due to various reasons such as lack of access to resources and clinicians. On the other hand, providers rely on Electronic Health Records (EHRs) to compile and share clinical notes, which is a key component of clinical practice, but time-consuming data entry is considered one of the primary downsides of EHRs. Many practitioners are spending more time in EHR documentation than direct patient care, which adds to patient dissatisfaction and clinician burnout. In this work, we explore the feasibility of developing an end-to-end clinical transcription tool that fully automates the documentation process for behavioral health clinicians. We divide the task into several sub-tasks and primarily focus on the following: 1) extraction and classification of important information from patient-provider conversations, and 2) generation of clinical notes from extracted information. We develop a dataset of 65 transcripts from simulated provider-patient conversations. Then, we fine-tune a transformer language model that shows promising results on personalized data extraction (F1=0.94) and scope for improvement in classification (F1=0.18) of extracted information to EHR categories. Furthermore, we develop a rule-based natural language generation module that formalizes all types of extracted information and synthesizes them into clinical notes. The overall pipeline shows the potential of automatically generating draft clinical notes and reducing the documentation time for behavioral health clinicians by 70-80%. The findings of this work have implications for health behavioral care providers as well as machine learning and natural language processing application developers.