Building AI for Nurses: Practical Lessons for Startups

At KB Kinetics, we have been working with a promising company developing AI-enabled clinical workflows. We've connected their team with subject matter experts in nursing, IT, and hospital operations to pressure-test use cases, refine integration points, and ensure their solution truly supports frontline staff. In one recent session, we brought the founder together with a senior nursing executive to examine how the technology could reduce workload for overextended nurses, a perspective often missed when tools are designed without early clinical input.

The discussion reinforced themes we enounter consistently: healthcare leaders want solutions that solve very practical problems. Nurses are already stretched thin. Technology that adds more steps, increases documentation, or slows them down will not be adopted. This founder’s openness to listen, refine, and validate with experts was encouraging and reflects the type of approach that works in the U.S. market.

Key Takeaways

The themes from this session connect directly to the lessons I outline in The Startup Guide to U.S. Healthcare, especially in the chapter on innovation and AI.

1. Focus on solving daily problems

The best AI tools in healthcare are not necessarily those with the most advanced algorithms. They are the ones that streamline work for staff, reduce time spent on repetitive tasks, or improve patient follow-up. Begin with the basics: does this help the care team or the patient in a clear, practical way?

2. Validate with real workflows

Ideas need to be tested in the environment where they will be used. Collect feedback directly from clinicians, administrators, and care coordinators. Observe how teams currently work and identify where your solution removes unnecessary steps or streamlines work.

3. Design with integration in mind

Hospitals already use complex electronic health record systems and other digital tools. A new solution must connect to those systems without requiring extra steps. Integration and ease of use are as important as the functionality itself. One of the first questions we hear most often: “Does your solution integrate with Epic?”

4. Build trust and credibility

Healthcare leaders are cautious about AI for good reason. They have seen tools that make bold promises but fail in practice. In fact, some are cynical about AI claims so be transparent about what your product can and cannot do. Show results, respect clinical boundaries, and explain how your system keeps data private and secure.

5. Plan for the decision-making process

Hospital executives evaluate technology through multiple lenses. A Chief Nursing Officer will want to know how it impacts documentation and staffing. A Chief Financial Officer will focus on costs and return on investment. A Chief Information Officer will care about system security and uptime. Preparing for each perspective avoids delays later.

6. Avoid ethical and clinical missteps

AI should support licensed clinicians, not replace them. If your product automates treatment decisions or creates medical recommendations without oversight, you risk regulatory barriers. Be explicit about where human review occurs in your process.

The founder we worked with this week is on the right path because she and her team are listening, refining, and aligning their solution with what nurses and hospital leaders actually need. Many startups skip this step and struggle as a result.

For founders entering the U.S. market, remember: success comes from usability, credibility, and alignment with real-world priorities. Build tools that reduce burden, validate your solution with operational data, and design for integration from the start.

Startup Action Checklist: Validating AI in U.S. Healthcare

Use this to evaluate whether your AI solution is practical and positioned for adoption:

☐ My product addresses a recurring problem in daily healthcare operations.

☐ I have validated the problem through direct feedback, workflow observation, or data.

☐ The solution reduces work for frontline staff instead of adding to it.

☐ I can clearly explain the value in healthcare terms, such as cost savings or better outcomes.

☐ My product integrates with existing systems and does not require disruptive changes.

☐ Ethical and legal considerations are addressed, with licensed clinicians maintaining final responsibility for care decisions.

☐ The tool is explainable and transparent, so clinicians can understand and trust its outputs.

☐ I have considered risks such as hallucinations, bias, or misleading automation and planned for them.

☐ Adoption can occur with minimal disruption and measurable efficiency gains.

☐ I can show that health systems or payers are willing to fund this type of solution today.

At KB Kinetics, we work with founders to prepare them for the realities of U.S. healthcare—helping refine solutions, connect with the right stakeholders, and anticipate the questions they will face in the boardroom or at the bedside. If your company is developing a healthcare solution and would like support navigating this environment, let us know how we can help.

For a deeper dive into these lessons, explore The Startup Guide to U.S. Healthcare, an Amazon #1 New Release in Medicaid & Medicare. The book is grounded in real-world executive experience and offers practical guidance for international founders and companies looking to succeed in the U.S. market. Check it out here.