AI has the potential to revolutionize healthcare, from comprehensive coding to patient outreach, but there are major barriers that prevent healthcare organizations from embracing this technology. In this article, learn how user mistrust, an overwhelmed technical team, insufficient data infrastructure, and workforce challenges keep organizations from adopting solutions that could transform outcomes. Then, learn how you can overcome these barriers with the right strategy and industry wisdom.
A recent Harris Poll survey found that 97% of healthcare organizations think AI will have an important role in the next 5 years, while only 54% of IT leaders are very confident in their ability to implement a solution successfully. They cite challenges such as a lack of talent to facilitate adoption, limited organizational experience, and ethics and privacy concerns as primary blockers to implementing AI. Furthermore, 40% of IT leaders say staff at their organization are not equipped to make the best use of data analytics.
The first step to overcoming these hurdles is to clearly identify them, so you can create an action plan to move forward. Below are four major challenges that hinder AI adoption:
A 2023 AMA survey claimed 40% of physicians are equally excited and concerned about AI applications in healthcare, while 23% of respondents to a recent McKinsey and the American Nursing Association Survey expressed discomfort regarding AI’s potential impacts on care. If healthcare professionals don’t trust AI tools, organizations are unlikely to invest in them. To drive adoption, organizations must prioritize transparency, usability, and clear communication about AI's benefits.
AI is not a plug-and-play solution — it requires ongoing oversight. If your IT team is already stretched thin, introducing AI may feel like an additional burden rather than a solution. According to this article from The Journal of General Internal Medicine, successful AI implementation requires collaboration between technical and clinical experts, project managers, and process owners. Without the right support structure, organizations may struggle to implement AI effectively.
AI models require large volumes of high-quality data. Whether it’s a data lakehouse or a more traditional data warehouse, organizations must ensure their data is interoperable and easily accessible. The more data you need to run a program or application, the more complex your storage needs. Without a clear sense of what’s required for an AI tool — or an experienced partner to handle these requirements — setting up data systems that support AI might be overwhelming.
AI adoption requires training, but many tools on the market lack comprehensive education programs. This leaves organizations with a difficult quandary. Is it worth investing in a new AI tool if implementation will lag due to a long learning curve? Is it worth creating a training program from scratch, expending time and resources for a tool that might change as fast as the technology it depends on? Also, while AI can ultimately alleviate staffing shortages, it might require an upfront investment for new technical staff. These issues prevent some organizations from taking the leap.
A saturated market and a climate of uncertainty makes the path forward for healthcare AI murky. As regulations change and fiscal pressures grow, leaders need confidence that their AI investments will deliver a high ROI, quickly.
Navigating the AI landscape can be complex, but a well-defined strategy can help organizations unlock AI’s full potential. In Your guide to building a winning healthcare AI strategy, learn from experts at major payer, clinical, and business organizations with a finger on the pulse. Download your free guide to learn how you can tap AI’s potential without facing these hurdles in three concrete steps.