Challenge: Despite advances in natural language processing, converting a clinic visit conversation into a clinical note is a largely unexplored area of research.
Approach: They propose an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences.
Outcome: The proposed method is content- and technique-agnostic while associating note sentences to sets of dialogue sentences.

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Challenge: Existing datasets for summarization of medical conversations are limited to conversation-summary pairs . a novel annotation framework is proposed to capture the summarizing process via an annotation task .
Approach: They propose an incremental note generation framework that captures the human summarization process via an annotation task by instructing annotators to first incrementally create a draft note and polish it into a reference note.
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Extracting relevant information from physician-patient dialogues for automated clinical note taking (D19-62)

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Challenge: a system that extracts pertinent medical information from dialogues between clinicians and patients is proposed . entering data into EMRs is currently slow and error-prone, and clinicians spend up to 50% of their time on data entry.
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An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)

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Challenge: Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters.
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The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)

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Challenge: Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking.
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MIMICause: Representation and automatic extraction of causal relation types from clinical notes (2022.findings-acl)

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Challenge: Extracted causal information from clinical notes can be combined with structured EHR data such as demographics, diagnoses, and medications.
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A French Medical Conversations Corpus Annotated for a Virtual Patient Dialogue System (2020.lrec-1)

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Challenge: Existing methods for creating virtual patient dialogue systems require large data specific to the language, domain and clinical cases studied.
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Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding (2022.lrec-1)

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Challenge: Existing corpus and annotations focus on textual features and relation prediction, but there are no structured corpus models for clinical diagnostic thinking.
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User-Driven Research of Medical Note Generation Software (2022.naacl-main)

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Challenge: Existing studies on how NLP systems could be used in clinical practice focus on technical difficulties and usability challenges involved in implementing them.
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Extracting Symptoms and their Status from Clinical Conversations (P19-1)

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Challenge: Existing models for extracting symptoms from clinical conversations are inherently difficult.
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Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)

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Challenge: Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools.
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