Electronic health record (EHR) vendors are accelerating their adoption of artificial intelligence, aiming to enhance clinician workflows, improve patient care, and remain competitive in an evolving healthcare landscape.
Leaders including Epic and Oracle are integrating AI-driven capabilities into their platforms to help alleviate administrative burdens and boost productivity in an industry grappling with rising costs and clinician burnout.
The move signals a pivotal shift in the role of EHR systems, which have long been criticized by healthcare professionals for their complexity and time-consuming documentation requirements. By leveraging AI, vendors seek to modernize digital health records and make them more intuitive, efficient, and beneficial for both providers and patients.
Addressing Clinician Pain Points
Healthcare professionals often cite EHRs as a source of frustration due to their intricate interfaces and excessive data entry demands. While these systems were originally implemented to digitize and streamline medical documentation, they have frequently been viewed as more of a bureaucratic necessity than a tool designed to support clinical decision-making.
Leigh Burchell, chair of the Electronic Health Records Association, told Healthcare Dive of the need for AI to alleviate administrative strain rather than replace physicians. “Doctors are not looking for AI to act as a doctor or step into their place. They want help with administrative burdens—tasks that take time after a visit to document—so they can focus on patient care,” Burchell explains.
By Venkatgiri Vandali, president of healthcare and life sciences, Firstsource.
AI is a natural fit in multiple functions across the health plan value chain. In 2025, more plans will deploy generative AI in claims operations, the contact center, quality assurance, training and more.
These areas will become more efficient. In addition, health plans that use AI tools to solve perennial pain points, make employees more productive and deliver better member and provider experiences will start to pull away from competitors.
Why are generative AI and related tools such as machine learning able to deliver more value?
In the past, it took a long time for people to refine their requests to get the data they required. It often meant asking IT for help and could take days. Now AI makes it possible to essentially talk to machines and receive content in real time. This creates tremendous efficiency that will lead to cost reduction and ultimately more value for payers and members.
As an example, generative AI tools quickly extract relevant content from emails, claims, contracts, medical records and more. That minimizes the need for staff to look up information, expedites service processes and increases opportunities for automation. These efficiencies can net considerable time and cost savings while improving services.
What are AI copilots and AI agents and how do they help?
AI copilots work alongside humans, looking up information, flagging potential errors and suggesting next best actions. AI agents run in the background. Multiple autonomous AI agents can work together to exchange information and act based on business rules in “agentic workflows.”
An AI copilot could oversee an agentic workflow in which AI agents verify member data on a claim is accurate; check and correct edit codes; then evaluate whether the claim will adjudicate successfully. If the agentic workflow determines there’s an issue with the claim, the AI co-pilot will flag the potential issue for an expert human associate to mitigate.
What are some of the major efficiency gains payers can expect from AI?
Generative AI has the ability to manage massive amounts of data and yet also find a needle in that data haystack. This capability did not exist before generative AI and is invaluable to any operation that requires analytics and where resolution speed is critical. For example, things can go wrong in five million claims for a variety of reasons. Before AI, a payer would have to retroactively identify and correct the issue. Today, generative AI can constantly be talking to the system, monitoring claims, following up, ensuring claims are routed and processed correctly. Generative AI can also recognize when something is wrong. Then it routes the claim back to human stakeholders and alerts them to the problem.
Electronic health record (EHR) vendors are accelerating their adoption of artificial intelligence, aiming to enhance clinician workflows, improve patient care, and remain competitive in an evolving healthcare landscape.