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Published on: September 20, 2018
What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis
Summary
Clinicians frequently edit ambient artificial intelligence (AI) drafts, focusing on clinical facts like orders, symptoms, and diagnoses. Future AI development should prioritize specialty customization and personalized documentation.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Documentation
Background:
- Ambient artificial intelligence (AI) is increasingly used for clinical note generation from patient-provider conversations.
- Understanding how clinicians revise AI-generated drafts is crucial for optimizing AI design and implementation.
Purpose of the Study:
- To characterize real-world edits made by clinicians to ambient AI-generated clinical note drafts.
- To identify areas for improvement in AI design and the clinical implementation process.
Main Methods:
- Qualitative content analysis of clinical documentation from 200 patient encounters.
- Development of an inductive coding framework with 11 codes across clinical content, terminology, and language style.
- Thematic analysis to synthesize patterns in clinician edits.
Main Results:
- Most frequent edits involved clinical facts: orders (40.0%), symptoms (30.3%), medications (27.3%), and diagnoses (25.9%).
- Edits related to terminology (11.6%) and language style (7.2%) were less common.
- Key edit types included revising factual discrepancies, adding specialty-specific details, clarifying diagnostic certainty, converting patient language to medical terms, and content reorganization.
Conclusions:
- Clinicians routinely revise ambient AI drafts to ensure factual accuracy and clinical specificity.
- Future AI development should focus on specialty customization and supporting personalized documentation.
- Clinician education is vital for promoting thorough review and maintaining documentation quality.
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