REVIEW 4 cited by
Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 4 Pith papers
-
Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
A weakly supervised, retrieval-augmented vision-language framework generates structured SOAP notes from lesion images and sparse clinical text, with evaluation against GPT-4o, Claude, and Janus Pro on a small set of cases.
-
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
DENSE synthesizes progress notes across hospital visits using retrieval over heterogeneous clinical notes, claiming temporal continuity that even exceeds gold-standard notes.
-
Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation
With temperature set to zero and ten repeated runs, all tested LLMs kept semantic consistency above 96% on clinical note generation, while Llama 70B and Mistral Small had the best combined consistency and correctness.
-
Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes
Skin-SOAP is a weakly supervised multimodal system that turns a skin lesion image and sparse clinical text into structured SOAP notes, evaluated with two new metrics.
Discussion (0). Sign in to comment.