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Challenges of developing a digital scribe to reduce clinical documentation burden.
Juan C Quiroz1, Liliana Laranjo1, Ahmet Baki Kocaballi1
1Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
NPJ Digital Medicine
|December 5, 2019
Summary
Automated clinical documentation using artificial intelligence (AI) and machine learning (ML) digital scribes faces challenges. Key issues include audio recording, speech recognition, and data extraction for AI development.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Documentation Improvement
Background:
- Clinicians dedicate significant time to patient encounter documentation, negatively affecting care quality and contributing to physician burnout.
- Artificial intelligence (AI) and machine learning (ML) offer potential solutions for automating clinical documentation through digital scribes.
- Developing effective digital scribes is complex due to the intricate nature of clinical environments and conversations.
Purpose of the Study:
- To identify and discuss the primary challenges in developing automated speech-based clinical documentation systems.
- To explore the technical hurdles in creating AI-powered digital scribes for healthcare settings.
Main Methods:
- Literature review and conceptual analysis of AI/ML applications in clinical documentation.
- Identification of key stages in automated speech-based documentation: audio recording, speech recognition, topic structuring, concept extraction, summarization, and data acquisition.
- Discussion of the complexities inherent in each stage within clinical contexts.
Main Results:
- Significant challenges exist in obtaining high-quality audio recordings in clinical settings.
- Speech recognition accuracy is a major hurdle for converting spoken language to accurate clinical text.
- Extracting medical concepts and generating meaningful summaries from conversations requires sophisticated natural language processing (NLP) techniques.
- Acquiring sufficient and appropriate clinical data for training AI/ML algorithms presents a substantial obstacle.
Conclusions:
- Overcoming challenges in audio quality, speech recognition, and NLP is crucial for successful AI-driven clinical documentation.
- Further research and development are needed to address the identified obstacles for practical implementation of digital scribes.
- Addressing these challenges will be key to reducing clinician burden and improving the efficiency of healthcare documentation.
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