Commercial Products Using Generative Artificial Intelligence Include Ambient Scribes, Automated Documentation and...

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Natural Language Processing

Background:

• Artificial intelligence (AI) is increasingly integrated into clinical practice, transforming healthcare.
• Large language models (LLMs) are key AI advancements driving this transformation.
• LLMs optimize documentation, administration, and patient engagement in healthcare settings.

Purpose of the Study:

• To review the current landscape of large language models in healthcare.
• To identify emerging commercial applications and their potential benefits.
• To discuss the limitations and future considerations for LLM integration in clinical practice.

Main Methods:

• Review of emerging commercial generative artificial intelligence products, particularly ambient scribes.
• Analysis of applications in areas like automated documentation, scheduling, revenue cycle management, patient engagement, and prior authorization.
• Identification of current limitations and future ethical, clinical, and operational considerations.

Main Results:

• Approximately 90 commercial generative artificial intelligence platforms, primarily ambient scribes, are currently available.
• Emerging LLM applications show potential to improve provider efficiency and payer-provider alignment, especially in automating prior authorization.
• Key limitations include lack of regulatory oversight, existing biases, interoperability issues with electronic health records, and insufficient physician buy-in.

Conclusions:

• LLMs offer significant potential to enhance healthcare workflows and efficiency.
• Addressing limitations related to regulation, bias, interoperability, and stakeholder trust is crucial for successful adoption.
• Future advancements require careful consideration of ethical, clinical, and operational factors.