Among the diverse AI and machine learning tools being integrated in medical group practices, the most common are those that enable physicians and other clinicians to dedicate more time to patient care during visits.
An Oct. 29, 2024, MGMA Stat poll found that nearly six medical group leaders in 10 (59%) cited scribing/documentation tools as their organizations’ top priority among AI tools, ahead of revenue cycle AI (19%), patient communication AI (10%) and “other” (3%). An additional 9% of respondents selected “not applicable,” indicating they currently have no plans for AI. The poll had 302 responses.
A closer look
Practice leaders who shared their organization’s top AI tool priorities gave further insight into where they are on the path to AI implementation:
- Among practice leaders who responded “scribing/documentation,” about 80% said they already have an AI tool (38%) or plan to add one in the next year (42%).
- Practices where patient communication tools were the top priority were more likely to already have an AI tool (54%) compared to those looking to add one in the coming year (38%) or where unsure (8%).
- Revenue cycle AI tool respondents were the least likely to report they currently have a tool implemented (21%), while more than one in three (35%) plan to add one in the next year, while most (44%) were unsure.
These results echo similar responses from MGMA polling earlier in October 2024 that found more than four practices in 10 (43%) had added or expanded use of AI tools this year, jumping from only 21% in a September 2023 poll.
Looking ahead
An upcoming research report from MGMA surveyed healthcare leaders throughout summer 2024 about their perceptions of AI and implementation of solutions in their organizations, specifically:
- The motivations for and barriers to AI adoption
- Governance of AI use, including policies and policy review
- Important factors for organizational buy-in.
Among the main findings of the forthcoming report:
- The most used AI applications are ambient AI/transcription/scribing and clinical diagnostic tools.
- Process efficiency motivates adoption, while costs and lack of training are barriers.
- Motivations for AI adoption: Improving process efficiency, reducing the burden on providers/staff and enhancing patient outcomes, reflecting the pressing need to improve operational efficiency and alleviate healthcare professional workloads.
- Barriers to AI adoption: Lack of education/training, high costs and limited trust or buy-in. Organizations that have already adopted AI also cite regulatory hurdles and a shortage of skilled personnel as significant challenges.
- Formal AI governance structures and regular policy reviews remain limited, with most organizations reporting that they update their internal AI policies on an as-needed basis rather than at set intervals.
Watch MGMA Insights newsletter in November for the release of the full report, as well as the January issue of MGMA Connection magazine for more about the key motivations and long-term investments and strategies being taken around AI in healthcare.
Join MGMA Stat
Our ability at MGMA to provide great resources, education and advocacy depends on a strong feedback loop with healthcare leaders. To be part of this effort, sign up for MGMA Stat and make your voice heard in our weekly polls. Sign up by texting “STAT” to 33550 or visit mgma.com/stat. Polls will be sent to your phone via text message.
Do you have any best practices or success stories to share on this topic? Please let us know by emailing us at connection@mgma.com.
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