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Role of Communication in the Nursing Process III: Evaluation and Documentation01:08

Role of Communication in the Nursing Process III: Evaluation and Documentation

A successful patient outcome depends mainly on the evaluation stage of the nursing process. Evaluation determines effectiveness by reviewing what was done previously after the completion of nursing interventions. Every time a healthcare professional steps in or administers treatment, they must reassess or evaluate the action to ensure the intended result. During the evaluation phase, there are three probable patient outcomes:
Formats for Nursing Documentation01:28

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Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...

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Related Experiment Video

Updated: Jun 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Voice-Based Structured Nursing Documentation Using Automatic Speech Recognition and Large Language Models:

Meng-Han Su1, Wei-Chun Wang1, Yi-Min Hsu2

  • 1Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, China Medical University, No. 2, Yude Rd, North Dist, Taichung, 404327, Taiwan, 886 0422052121 ext 12584.

JMIR Nursing
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an integrated automatic speech recognition (ASR) and large language model (LLM) system to streamline nursing documentation. The system significantly reduces manual data entry, improving efficiency and record completeness for clinical nurses.

Keywords:
automatic speech recognitioncode-switchingdocumentationlarge language modelnursingnursing records

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Healthcare Technology

Background:

  • Manual nursing documentation in Hospital Information Systems (HISs) is time-consuming and error-prone.
  • Previous speech recognition faced challenges with code-switching, medical terms, and noisy environments.
  • Advances in customized ASR and LLMs enable feasible speech-based, structured nursing documentation.

Purpose of the Study:

  • To develop and evaluate an integrated ASR and LLM system for transforming spoken nursing input into structured DART notes.
  • To assess the system's accuracy, usability, and clinical feasibility within HIS workflows.

Main Methods:

  • Fine-tuned Whisper ASR model using a code-switching nursing speech corpus.
  • Employed LLMs for schema-constrained DART record generation from ASR transcripts.
  • Evaluated ASR accuracy (mixed error rate), DART classification (F1-scores), hallucination rates, and nurse feedback.

Main Results:

  • Reduced ASR mixed error rate from 44.79% to 6.67%.
  • Achieved a macroaveraged F1-score of 0.82 for DART generation, meeting noninferiority to human transcripts.
  • Observed a 2.51% hallucination rate and a significant increase in documented notes; 75.8% of nurses reported reduced workload and improved completeness.

Conclusions:

  • The integrated ASR and LLM system is feasible and performs well, demonstrating strong acceptance among clinical nurses.
  • The system effectively reduces the manual documentation burden and enhances record completeness.
  • Supports the value of ASR and LLM-assisted workflows for optimizing nursing documentation.