Will Artificial Intelligence (AI) Make or Break Hospital Finances?
AI has attracted billions in investments, promising to transform healthcare operations. Yet, hospitals struggle to predict the real value these technologies will bring. A recent survey by McKinsey found that while about half of health system leaders expect a return on investment (ROI) from AI, only 17% currently measure a positive return.
With many hospitals operating on razor-thin or negative margins, risking resources on uncertain AI tools is not an option. Historically, hospitals have reacted to innovation rather than driving it, as seen with mandated electronic health record (EHR) implementations and consumer-facing digital tools. To avoid repeating this pattern, healthcare leaders must actively pursue AI opportunities that improve operational efficiency and profitability.
Hospitals’ AI ROI Challenge
New healthcare technologies have a mixed history in delivering promised benefits, leading to understandable skepticism among hospital executives. While software companies have seen financial gains, health systems—relying on these technologies for imaging, billing, and more—haven't always shared the same success.
Operating in a competitive market where median margins often fall below 1%, hospitals need AI investments that are productive and accountable. Tight budgets and financial pressures can lead to rushed AI purchases without proper ROI planning or, conversely, waiting indefinitely for an all-in-one solution. Both approaches risk hospitals’ financial health and patient care capacity.
To make AI work, leaders need a clear plan: define how to measure value, carefully select tools, and scale what proves effective.
Start with the Benefits
ROI discussions should begin with a full understanding of AI’s benefits. Many focus solely on automation and cost savings, but AI can also improve the quality and scope of work. For example, clinical documentation integrity (CDI) teams typically catch only about 40% of missed diagnosis codes. AI can help identify the remaining 60%, including less common but high-impact codes, boosting revenue without replacing human efforts.
AI is not just about doing the same work cheaper; it’s about doing better, smarter, and faster work. Hospitals should start small—pilot AI in a single department or with a limited user group. Demonstrating early wins builds the political capital needed for broader implementation.
Align on Success Metrics with Analytics Teams
Many hospitals lack internal analytics to measure AI ROI and rely on vendors. Before selecting a vendor, hospitals must align with their analytics teams on how to define and attribute value. Poorly designed metrics can hide true performance issues. For example, a vendor might report an 80% efficiency gain based on cost alone, while the actual workforce required remains unchanged.
Clearly define success metrics upfront and ensure vendors agree to be held accountable for delivering on them.
Help AI Vendors Help You
Vendors want to know the key metric they should optimize but may need guidance. Hospitals and vendors should collaborate closely, mapping out how the solution will address specific challenges. If a vendor can't clearly explain how their product creates value in your context, consider it a warning sign.
Provide vendors with the necessary data and context to succeed. Remember, ROI is a shared responsibility.
Build the Right Team for AI Success
Success depends on the people behind AI, not just the technology. A small, capable AI task force supported by strong analytics can steer smart investments. This group should work closely with technology partners and internal stakeholders to evaluate and validate AI tools.
A large committee is unnecessary. A few focused, curious, and analytically minded individuals can make a significant difference.
Learn by Experience to Plan for the Future
Confidence in AI is growing as capabilities that once seemed futuristic become commercially viable, especially with advances like large language models that understand clinical documentation.
Hospitals that delay AI adoption risk falling behind and missing out on tangible benefits. For example, AI in revenue cycle management now performs second-level patient chart reviews before billing, improving efficiency and generating an ROI of 5:1. This is proven performance, not just potential.
AI doesn’t have to be a mystery. With clear plans, well-defined metrics, and the right team, healthcare leaders can cut through hype and make choices that drive real value. In today’s tight financial environment, AI must deliver productivity—not just promise it. For many hospitals, it already does.
For healthcare professionals looking to enhance their understanding of AI applications and improve hospital operations, exploring targeted AI training courses can be a valuable step. Resources like Complete AI Training’s latest AI courses offer practical knowledge tailored for healthcare roles.
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