ArticleJournal of the American Medical Informatics Association : JAMIA2019
Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic study on blockchain technology for genomic data management in healthcare: approaches, challenges, and research opportunities.Medical & biological engineering & computing · 2026Pooled it
- Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.medRxiv : the preprint server for health sciences · 2026Article
- Enhancing Detection of Message Intents in a Mobile Health Smoking-Cessation Intervention Using Large Language Model Fine-Tuning, Data Downsampling, and Error Correction: Algorithm Development and Validation.Journal of medical Internet research · 2026Article
- Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.JMIR formative research · 2025Article
- Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls.Applied clinical informatics · 2024Article
- Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A meta-narrative review of coding tools for healthcare interactions and their applicability to written communication.PEC innovation · 2023Review
- "Mm-hm," "Uh-uh": are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology?Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning in general practice: scoping review of administrative task support and automation.BMC primary care · 2023Article
- Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare.Cell reports. Medicine · 2022Review
- An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates.Computer speech & language · 2022Article
- Exploring the Discursive Emphasis on Patients and Coaches Who Participated in Technology-Assisted Diabetes Self-management Education: Clinical Implementation Study of Health360x.Journal of medical Internet research · 2022Article
- Automatic speech recognition performance for digital scribes: a performance comparison between general-purpose and specialized models tuned for patient-clinician conversations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.JMIR mental health · 2021Article
- Article
- Utilization of Time Series Tools in Life-sciences and Neuroscience.Neuroscience insights · 2020Article
- Assessing the accuracy of automatic speech recognition for psychotherapy.NPJ digital medicine · 2020Article
- Challenges of developing a digital scribe to reduce clinical documentation burden.NPJ digital medicine · 2019Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveAmid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts. MATERIALS AND
methodsWe used dialog transcripts from 279 primary care visits to predict talk-turn topic labels. Different machine learning models were trained to operate on single or multiple local talk-turns (logistic classifiers, support vector machines, gated recurrent units) as well as sequential models that integrate information across talk-turn sequences (conditional random fields, hidden Markov models, and hierarchical gated recurrent units).
resultsEvaluation was performed using cross-validation to measure 1) classification accuracy for talk-turns and 2) precision, recall, and F1 scores at the visit level. Experimental results showed that sequential models had higher classification accuracy at the talk-turn level and higher precision at the visit level. Independent models had higher recall scores at the visit level compared with sequential models.
conclusionsIncorporating sequential information across talk-turns improves the accuracy of topic prediction in patient-provider dialog by smoothing out noisy information from talk-turns. Although the results are promising, more advanced prediction techniques and larger labeled datasets will likely be required to achieve prediction performance appropriate for real-world clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.