Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations

Abstract

Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and nonverbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.

A Survey on Dialogue Summarization: Recent Advances and New Frontiers

“…Name Domain Language ICSI [Janin et al, 2003] Meeting English AMI [Carletta et al, 2005] English QMSum [Zhong et al, 2021] English SAMSum [Gliwa et al, 2019] Chat English GupShup [Mehnaz et al, 2021] Code-Mix CSDS [Lin et al, 2021] Customer Service Chinese TODSum English TWEETSUMM English CRD3 [Rameshkumar and Bailey, 2020] TV Show English [Song et al, 2020] Medical Chinese SumTitles [Malykh et al, 2020] Movie English MEDIASUM [Zhu et al, 2021] Interview English DIALOGSUM Spoken English EMAILSUM [Zhang et al, 2021a] Email English ForumSum [Khalman et al, 2021] Forum English ConvoSumm [Fabbri et al, 2021] Mix English…”

“…Liu et al [2019b] specify the dialogue topics according to the symptoms, such as headache and cough, and design a topic-level attention mechanism to make the decoder focus on one symptom when generating one summary sentence. Kazi and Kahanda [2019] instead choose EHR categories to label each segment, such as family history and medical history. Specifically, Krishna et al [2021] name the medical dialogue summary SOAP note, which stands for Subjective information reported by the patient; Objective observations; Assessments made by the doctor; and a Plan for future care, including diagnostic tests and treatments.…”

Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation

“…Previous studies have shown that electronic medical record (EMR) data are difficult to use in machine learning systems due to the lack of regulation in data quality -EMR data are often incomplete and inconsistent (Weiskopf and Weng, 2013;Roth et al, 2009). Recently, there have been attempts to improve automated clinical note-taking by extracting relevant information directly from physicianpatient dialogues Kazi and Kahanda, 2019;Du et al, 2019). This can alleviate physicians of tedious data entry and ensures more consistent data quality (Collier, 2017).…”

BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

“…Recent studies have exposed the importance of biomedical NLP in the well-being of human-beings, analyzing the critical process of medical decisionmaking. However, the dialogue managing tools targeted for medical conversations (Zhang et al, 2020), (Campillos Llanos et al, 2017), (Kazi and Kahanda, 2019) between patients and healthcare providers in assisting diagnosis may generate certain insignificant perturbations (spelling errors, paraphrasing), which when fed to the classifier to determine the type of diagnosis required/detecting adverse drug effects/drug recommendation, might provide unreasonable performance. Insignificant perturbations might also creep in from the casual language expressed in the tweets (Zilio et al, 2020).…”