Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients

• URL: http://arxiv.org/abs/2404.05144v1
• Date: Mon, 8 Apr 2024 01:55:28 GMT
• Title: Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients
• Authors: HyoJe Jung, Yunha Kim, Heejung Choi, Hyeram Seo, Minkyoung Kim, JiYe Han, Gaeun Kee, Seohyun Park, Soyoung Ko, Byeolhee Kim, Suyeon Kim, Tae Joon Jun, Young-Hak Kim,
• Abstract summary: This study addresses inefficiencies and inaccuracies in creating discharge notes manually, particularly for cardiac patients. Our research evaluates the capability of large language model (LLM) to enhance the documentation process. Among the various models assessed, Mistral-7B distinguished itself by accurately generating discharge notes.
• Score: 1.379398224469229
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical documentation, including discharge notes, is crucial for ensuring patient care quality, continuity, and effective medical communication. However, the manual creation of these documents is not only time-consuming but also prone to inconsistencies and potential errors. The automation of this documentation process using artificial intelligence (AI) represents a promising area of innovation in healthcare. This study directly addresses the inefficiencies and inaccuracies in creating discharge notes manually, particularly for cardiac patients, by employing AI techniques, specifically large language model (LLM). Utilizing a substantial dataset from a cardiology center, encompassing wide-ranging medical records and physician assessments, our research evaluates the capability of LLM to enhance the documentation process. Among the various models assessed, Mistral-7B distinguished itself by accurately generating discharge notes that significantly improve both documentation efficiency and the continuity of care for patients. These notes underwent rigorous qualitative evaluation by medical expert, receiving high marks for their clinical relevance, completeness, readability, and contribution to informed decision-making and care planning. Coupled with quantitative analyses, these results confirm Mistral-7B's efficacy in distilling complex medical information into concise, coherent summaries. Overall, our findings illuminate the considerable promise of specialized LLM, such as Mistral-7B, in refining healthcare documentation workflows and advancing patient care. This study lays the groundwork for further integrating advanced AI technologies in healthcare, demonstrating their potential to revolutionize patient documentation and support better care outcomes.

Related papers

• Improving Clinical Documentation with AI: A Comparative Study of Sporo AI Scribe and GPT-4o mini [0.0]
  Sporo Health's AI scribe was evaluated against OpenAI's GPT-4o Mini. Results show that Sporo AI consistently outperformed GPT-4o Mini, achieving higher recall, precision, and overall F1 scores.
  arXiv  Detail & Related papers  (2024-10-20T22:48:40Z)
• A GEN AI Framework for Medical Note Generation [3.7444770630637167]
  MediNotes is an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio.
  arXiv  Detail & Related papers  (2024-09-27T23:05:02Z)
• Improving Clinical Note Generation from Complex Doctor-Patient Conversation [20.2157016701399]
  We present three key contributions to the field of clinical note generation using large language models (LLMs) First, we introduce CliniKnote, a dataset consisting of 1,200 complex doctor-patient conversations paired with their full clinical notes. Second, we propose K-SOAP, which enhances traditional SOAPcitepodder20soap (Subjective, Objective, Assessment, and Plan) notes by adding a keyword section at the top, allowing for quick identification of essential information. Third, we develop an automatic pipeline to generate K-SOAP notes from doctor-patient conversations and benchmark various modern LLMs using various
  arXiv  Detail & Related papers  (2024-08-26T18:39:31Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation [0.0]
  This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process. We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions. The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care.
  arXiv  Detail & Related papers  (2024-05-28T16:43:41Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization [50.01382938451978]
  We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
  arXiv  Detail & Related papers  (2023-03-23T04:47:46Z)
• Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes [0.0]
  We propose an end-to-end machine learning solution based on large language models (LLMs) The entities used in the KG construction process are diseases, factors, treatments, as well as manifestations that coexist with the patient while experiencing the disease. The application of the proposed methodology is demonstrated on age-related macular degeneration.
  arXiv  Detail & Related papers  (2023-01-29T15:52:33Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.