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Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation

Anjanava Biswas, Wrick Talukdar

TL;DR

This work investigates using generative AI to streamline clinical documentation by automatically generating SOAP and BIRP notes from transcribed patient-clinician encounters. It implements an end-to-end pipeline with NLP/ASR transcription, speaker diarization, and advanced prompting across multiple large language models, including zero-shot, one-shot, and JSON-schema-based approaches. A case-study-based evaluation shows GPT-4 Turbo delivering the strongest note-generation performance (ROUGE-1 F1 around 0.90–0.95) with notable model variability, and it demonstrates iterative note improvement leveraging subsequent encounters and version control. The study also highlights critical ethical, privacy, and governance considerations necessary for responsible deployment in healthcare. Overall, the results suggest substantial potential to reduce documentation burden and refocus clinician effort on patient care, contingent on robust data quality, transparency, and human oversight.

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.

Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation

TL;DR

This work investigates using generative AI to streamline clinical documentation by automatically generating SOAP and BIRP notes from transcribed patient-clinician encounters. It implements an end-to-end pipeline with NLP/ASR transcription, speaker diarization, and advanced prompting across multiple large language models, including zero-shot, one-shot, and JSON-schema-based approaches. A case-study-based evaluation shows GPT-4 Turbo delivering the strongest note-generation performance (ROUGE-1 F1 around 0.90–0.95) with notable model variability, and it demonstrates iterative note improvement leveraging subsequent encounters and version control. The study also highlights critical ethical, privacy, and governance considerations necessary for responsible deployment in healthcare. Overall, the results suggest substantial potential to reduce documentation burden and refocus clinician effort on patient care, contingent on robust data quality, transparency, and human oversight.

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.
Paper Structure (22 sections, 3 equations, 7 figures, 1 table)

This paper contains 22 sections, 3 equations, 7 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. PREVIOUS WORK
  3. METHODOLOGY
  4. Data Collection
  5. Transcription
  6. Prompt Engineering
  7. Model Selection and Deployment
  8. Comparative Analysis of Models for SOAP and BIRP Notes
  9. ITERATIVE NOTE IMPROVEMENT FOR EVOLVING PATIENT CARE
  10. Iterative Note Improvement with Subsequent Patient Encounters
  11. Incorporating Data from Subsequent Encounters
  12. Incremental Note Generation
  13. Continuous Learning and Adaptation
  14. Version Control and Auditing
  15. CHALLENGES AND FURTHER RESEARCH
  16. ...and 7 more sections

Figures (7)

  • Figure 1: Audio Transcription with ASR and Transcript Diarization
  • Figure 2: Confusion Matrix of Utterance Classification using Whisper and Pyannotate
  • Figure 3: Confusion Matrix of Utterance Classification using Whisper and GPT-3.5
  • Figure 4: Basic and Advanced Prompting to Generate Structured Clinical Notes
  • Figure 5: ROUGE-1 F1 Scores for Different Models Across SOAP note Samples
  • ...and 2 more figures