In part 1 of an interview with The American Journal of Managed Care® (AJMC®)),, Erin Weber, MS, Chief Policy and Research Officer at the Council for Affordable Quality Healthcare (CAQH), underlines that the adoption of artificial intelligence (IA) in health care remains limited due to concerns about privacy, bias and cost. Although current use is largely focused on administrative tasks, it stresses that the expansion of AI to areas such as eligibility verification and prior authorization will require greater collaboration between stakeholders.
Weber explores this subject further in his commentary, “has in health care: filling the income cycle lake”, published in the this month issue AJMC.
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Transcription
Despite significant progress, the adoption of AI in health care remains relatively low. What are the main obstacles that prevent suppliers from fully adopting AI? How can they be overcome?
The providers are naturally cautious with regard to AI; So me.
As our research shows, most of the adoption of AI is focused on administrative tasks, such as eligibility checks or documentation, which are truly precious departure points. These use cases can reduce errors and save time, but they have no impact directly on patient care. The enlargement of AI in clinical areas will require overcoming the probably deeper trust barriers.
To make real progress, AI tools will have to be user -friendly, solve significant problems and be built with transparency and basic security. More importantly, and this is a theme in the CAQH, success depends on collaboration. Providers, health plans and technological partners must really work together to make AI not only available but really useful.
In your comment, you highlighted the importance of collaboration between providers and health plans. How can these stakeholders work together to extend the advantages of AI beyond administrative tasks to improve the income cycle?
When providers and health plans collaborate early, and which share data or ideas, they can co-develop AI tools that improve the entire income cycle rather than simply repairing individual pain points. There are several industry -oriented initiatives that are currently working there. This type of partnership strengthens confidence and guarantees that AI solutions align with clinical workflows and payment processes.
The result is, ideally, a more fluid experience for patients, less surprises of billing, less confusion and faster access to care. As confidence and collaboration deepened, I hope that the comfort of using AI beyond these administrative tasks, which then opens the door to more impactful applications throughout the system.
Since AI is already widely used for administrative tasks, what areas of health services should then have priority for the integration of AI?
Despite all the buzz, the adoption of AI in health care remains relatively low. Data from the CAQH 2023 index revealed that only 19% of medical suppliers and 12% of dental providers said they used AI for any activity, and they are mainly administrative tasks. Many organizations are still in this exploration and evaluation phase. The adoption has increased since our initial data collection, without a doubt, but the global trend remains that, even when implemented, AI is not yet largely integrated into wider health care workflows.
I think that to move the needle on this subject, we must really focus on some of these high friction zones, such as the eligibility verification, prior authorization and correction of complaints, because they are permanent points of pain for providers and patients. If we can automate these processes using AI, we can reduce this administrative burden, save time and ideally help patients access care faster and more efficiently.
