Healthcare organizations have long relied on scribes for dictation, but today the landscape is rapidly changing with the advent of ambient AI solutions. Healthcare leaders are turning to ambient AI as a gateway to broader AI adoption in a crowded market where vendors are increasingly showcasing their AI offerings. This shift represents a pivotal moment as organizations explore how these advanced tools can increase efficiency, improve patient care, and streamline operations.

Epic Systems launched its user group conference on Monday, August 19, 2024, at its Verona campus. The conference attracted over 7,000 attendees, and the central theme and use case presentations all covered the organization’s use of ambient AI technology.

Crowded room

Epic Showroom is a curated app store with a growing list of ambient AI-powered tools from multiple vendors. These vendors have been carefully vetted to ensure seamless integration with Epic’s platforms. Some offer advanced features that can be embedded directly into Epic’s Haiku mobile app and Hyperspace workstation app.

According to Epic’s showroom, DAX Copilot has been the most popular with live customers and has a competitive advantage due to its embedded integration with Haiku and Hyperspace. Abridge is also gaining traction in this space, creating structured clinical notes from patient-doctor conversations in real time.

New ambient AI solutions are expanding their capabilities to include multilingual support and specializations and integrating Clinical Documentation Enhancement (CDI) wizards. These tools analyze conversations in real time, provide ICD-10 and CPT codes, and generate complete audit trails. This comprehensive functionality improves reimbursement for healthcare organizations by capturing correct coding based on the patient visit.

Other vendors in this space include Sunoh.ai, which works with many EMR vendors. Saurabh Singh, CTO and VP of Sunoh.ai, said, “Sunoh.ai is the first EHR-agnostic AI medical scribe that creates multimodal notes, including annotations of dental charts, homunculus models, and vision tests in tabular form, in addition to traditional SOAP notes.”

What’s next for Epic and AI?

Below are the four main areas where Epic is improving its system natively with AI.

1. Clinical documentation and summary: Epic’s AI initiatives focus on improving clinical documentation and patient care through advanced summarization tools. These initiatives include AI-driven note summarization based on body position, generating patient instructions from notes, and summarizing key information in emergency departments and inpatient settings. In addition, specialized summarization projects target specific areas, such as obstetric and radiology patient summaries, oncology specialty summaries, and transplant episode summaries for committee reviews.
2. AI integration for patient care: Several projects focus on integrating AI into patient care workflows, such as AI-driven procedure logging, service level suggestions, and real-time Clinical Documentation Improvement (CDI) coding. Similar to point solutions on the market, the goal is to improve the accuracy of clinical documentation and improve decision-making at the point of care.
3. Revenue cycle management: These initiatives include AI-driven tools for pre- and post-visit CDI coding, improving risk adjustment accuracy. They also provide point-of-care CDI assistants and automated generation of ICD-10 and CPT codes, improving billing accuracy. In addition, professional billing and hospital coding assistants and AI-suggested CDI queries enable more efficient billing workflows. The AI ​​tools also support denial appeal and clinical summary writing and automated prior authorization clinical documentation capture, ensuring healthcare organizations maintain accurate and compliant revenue cycle processes.
4. Clinical decision support and quality improvement: These efforts include AI-driven tools that provide service level suggestions, identify missed diagnoses, and detect sensitive content in clinical notes. Initiatives include summarizing clinical updates, automating curation of rare diagnoses, and generating case summaries for GI and pulmonary specialties. In addition, AI tools automatically assess medical necessity criteria, extract phenotypes as ontology codes for human phenotypes from notes, and perform deficit validation, all with the goal of improving diagnostic accuracy and maintaining high standards of patient care.

In summary, the ambient listening tools market is becoming increasingly crowded with numerous vendors offering similar functionality. However, Epic’s development of equivalent functionality directly in its EMR is changing the vendor landscape. It will be interesting to see how the future of these point solutions unfolds given Epic’s robust and integrated offerings.