Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation
- URL: http://arxiv.org/abs/2405.18346v1
- Date: Tue, 28 May 2024 16:43:41 GMT
- Title: Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation
- Authors: Anjanava Biswas, Wrick Talukdar,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Comprehensive clinical documentation is crucial for effective healthcare delivery, yet it poses a significant burden on healthcare professionals, leading to burnout, increased medical errors, and compromised patient safety. This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process, specifically focusing on generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes. We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions, coupled with advanced prompting techniques to generate draft clinical notes using large language models (LLMs). The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care. Additionally, we discuss ethical considerations, such as maintaining patient confidentiality and addressing model biases, underscoring the need for responsible deployment of generative AI in healthcare settings. The findings suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care.
Related papers
- Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions.
VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information.
We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
arXiv Detail & Related papers (2024-05-29T23:19:28Z) - Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients [1.379398224469229]
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.
arXiv Detail & Related papers (2024-04-08T01:55:28Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - An Introduction to Natural Language Processing Techniques and Framework
for Clinical Implementation in Radiation Oncology [1.2714439146420664]
We present state-of-the-art NLP applications that employ large language models (LLMs) in radiation oncology research.
LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation.
Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.
arXiv Detail & Related papers (2023-11-03T19:32:35Z) - Conceptualizing Machine Learning for Dynamic Information Retrieval of
Electronic Health Record Notes [6.1656026560972]
This work conceptualizes the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context.
We show that our methods can achieve an AUC of 0.963 for predicting which notes will be read in an individual note writing session.
arXiv Detail & Related papers (2023-08-09T21:04:19Z) - Large Language Models for Healthcare Data Augmentation: An Example on
Patient-Trial Matching [49.78442796596806]
We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM)
Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
arXiv Detail & Related papers (2023-03-24T03:14:00Z) - 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) - Human Evaluation and Correlation with Automatic Metrics in Consultation
Note Generation [56.25869366777579]
In recent years, machine learning models have rapidly become better at generating clinical consultation notes.
We present an extensive human evaluation study where 5 clinicians listen to 57 mock consultations, write their own notes, post-edit a number of automatically generated notes, and extract all the errors.
We find that a simple, character-based Levenshtein distance metric performs on par if not better than common model-based metrics like BertScore.
arXiv Detail & Related papers (2022-04-01T14:04:16Z) - An NLP Solution to Foster the Use of Information in Electronic Health
Records for Efficiency in Decision-Making in Hospital Care [0.26340862968426904]
The project aimed to define the rules and develop a technological solution to automatically identify attributes within free-text clinical records written in Portuguese.
The project's goal was achieved by a multidisciplinary team that included clinicians, epidemiologists, computational linguists, machine learning researchers and software engineers.
arXiv Detail & Related papers (2022-02-24T15:52:59Z) - Towards more patient friendly clinical notes through language models and
ontologies [57.51898902864543]
We present a novel approach to automated medical text based on word simplification and language modelling.
We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians.
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
arXiv Detail & Related papers (2021-12-23T16:11:19Z) - Benchmarking Automated Clinical Language Simplification: Dataset,
Algorithm, and Evaluation [48.87254340298189]
We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches.
We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
arXiv Detail & Related papers (2020-12-04T06:09:02Z)
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.