Solutions that fix real problems

Hospitals and health systems are looking at AI-enabled solutions that target their most urgent pain points: staffing shortages, clinician burnout, rising costs, and patient bottlenecks. These operational realities keep leadership up at night, and AI solutions  must directly address them.

For instance, hospitals and health systems are eager for AI tools that can reduce documentation burden for physicians and nurses. Natural language processing (NLP) solutions that auto-generate clinical notes or streamline coding to free up time for direct patient care are far more compelling pitches than generic efficiency gains. Similarly, predictive analytics that help optimize staffing levels or manage patient flows can directly address operational workflow and improve throughput.

Ultimately, if an AI solution doesn’t target these critical issues and deliver tangible benefits, it’s unlikely to capture serious buyer interest.

Demonstrate real-world results

AI solutions need validation in environments that mirror actual care settings. The first step toward that is to leverage high-quality, well-curated real-world data to drive reliable insights and avoid misleading results when building and refining AI models. 

Then, hospitals and health systems need evidence that the solution does what it claims to do, for instance through independent-third party validation, pilot projects, peer-reviewed publications, or documented case studies.

Mayo Clinic Platform offers a rigorous independent process where clinical, data science, and regulatory experts evaluate a solution for intended use, proposed value, and clinical and algorithmic performance, which gives innovators the credibility their solutions need to win the confidence of health-care leaders.    

Integration with existing systems

With so many demands, health-care IT leaders have little patience for standalone AI tools that create additional complexity. They want solutions that integrate seamlessly into existing systems and workflows. Compatibility with major electronic health record (EHR) platforms, robust APIs, and smooth data ingestion processes are now baseline requirements.

Custom integrations that require significant IT resources—or worse, create duplicative work—are deal breakers for many organizations already stretched thin. The less disruption an AI solution introduces, the more likely it is to gain traction. This is the reason solution developers are turning to platforms like Mayo Clinic Platform Solutions Studio, a program that provides seamless integration, single implementation, expert guidance to reduce risk, and a simplified process to accelerate solution adoption among healthcare providers. 

Explainability and transparency

The importance of trust cannot be overstated when it comes to health care, and transparency and explainability are critical to establishing trust in AI. As AI models grow more complex, health-care providers recognize that simply knowing what an algorithm predicts isn’t enough. They also need to understand how it arrived at that insight.

Health-care organizations are increasingly wary of black-box AI systems whose logic remains opaque. Instead, they’re demanding solutions that offer clear, understandable explanations clinicians can relay confidently to peers, patients, and regulators.

As McKinsey research shows, organizations that embed explainability into their AI strategy not only reduce risk but also see higher adoption, better performance outcomes, and stronger financial returns. Solution developers that can demystify their models, provide transparent performance metrics, and build trust at every level will have a significant edge in today’s health-care market.

Clear ROI and low implementation burden

Hospitals and health systems want to know precisely how quickly an AI solution will pay for itself, how much staff time it will save, and what costs it will help offset. The more specific and evidence-backed the answers, the better rate of adoption.

Solution developers that offer comprehensive training and responsive support are far more likely to win deals and keep customers satisfied over the long term.

Alignment with regulatory and compliance needs

As AI adoption grows, so does regulatory scrutiny. Health-care providers are increasingly focused on ensuring that any new solution complies with HIPAA, data privacy laws, and emerging guidelines around AI governance and bias mitigation.

Solution developers that can proactively demonstrate compliance provide significant peace of mind. Transparent data handling practices, rigorous security measures, and alignment with ethical AI principles are all becoming essential selling points as well.

A solution developer that understands health care

Finally, it’s not just about the technology. Health-care providers want partners that genuinely understand the complexities of clinical care and hospital operations. They’re looking for partners that speak the language of health care, grasp the nuances of change management, and appreciate the realities of delivering patient care under tight margins and high stakes.

Successful AI vendors recognize that even the best technology must fit into a highly human-centered and often unpredictable environment. Long-term partnerships, not short-term sales, are the goal.

Delivering true value with AI

To earn their trust and investment, AI developers must focus relentlessly on solving real problems, demonstrating proven results, integrating without friction, and maintaining transparency and compliance.

Those that deliver on these expectations will have the chance to help shape the future of health care.

This content was produced by Mayo Clinic Platform. It was not written by MIT Technology Review’s editorial staff.