Choosing an AI vendor means choosing an innovation partner

The best healthcare AI features are developed with practices, not just for them

AI adoption can be one of the more challenging technology decisions you'll face. It's about more than evaluating new capabilities. Every decision carries real implications for clinical workflows, staff adoption, and patient care. You might find yourself asking: “Will this solve the problems we’re targeting? How do we know which tools are worth adopting?”

Nevertheless, adoption is happening. But even as adoption increases, AI capabilities are rapidly evolving, making the choice as complex as ever. The questions around AI adoption and ROI are thus pivoting to whether medical practices feel their partners are equipped to handle this evolution and consistently fine-tune AI capabilities to meet practice needs.

One way to navigate this challenge is to become more than an end user. Rather than waiting for new AI capabilities to arrive and deciding whether they'll fit your practice, clinicians and healthcare leaders can play an active role in shaping how those tools are developed.

Through AI pilot programs in healthcare, usability testing, ongoing feedback, and conversations about product roadmaps, practices can help ensure AI is informed by real-world clinical experience from the start. Clinicians and practice leaders who can participate in innovative processes throughout development can contribute to a result that is more likely to fit seamlessly into the workflows they rely on every day.

Why AI requires a different kind of vendor relationship

Healthcare technology decisions have traditionally followed a familiar path — evaluate the options, select a solution, implement it, and move forward. Some technologies have consistent release updates to introduce new capabilities or make updates to existing features. Introducing AI into these workflows changes that dynamic because medical practices need tools that fit seamlessly and can evolve with practice needs.

That means choosing an AI-native EHR is about more than evaluating what's available today. It's also about understanding how those capabilities will improve over time and whether your organization has an opportunity to help shape that evolution.

Effective EHR vendor collaboration continues through ongoing dialogue, real-world feedback, and a shared commitment to solving the challenges clinicians face every day. Whether through alpha/beta programs, clinical AI user testing, or regular conversations about product direction, practices can play an important role in ensuring AI reflects the realities of patient care, not just technical possibilities.

Organizations aren’t just selecting an AI platform or AI tools. In essence, they are choosing an innovation process. The strongest vendor relationships recognize that better AI emerges through continuous learning, with clinicians helping refine capabilities as healthcare evolves.

As AI becomes a more integral part of clinical workflows, the question is shifting from "Which AI solution should we choose?" to "How can we help ensure the AI we're using continues to meet the needs of our clinicians and patients?"

What meaningful collaboration looks like in practice

Beyond submitting a feature request or responding to an occasional survey, meaningful collaboration is an ongoing partnership between healthcare organizations and the teams designing, developing, and refining AI tools. As AI evolves, that continuous exchange of ideas helps ensure new capabilities reflect how care is delivered.

Just as importantly, meaningful collaboration benefits from diverse perspectives. Physicians, clinical staff, and practice leaders all experience technology differently and bring valuable insights into how AI fits into everyday workflows. Bringing those voices together helps create solutions that are more intuitive, practical, and likely to earn clinicians' trust.

The value extends beyond improving individual features. Continuous collaboration helps organizations surface workflow challenges earlier, validate ideas before broad release, and ensure innovation remains grounded in everyday clinical practice rather than assumptions about how care is delivered.

The goal is to build AI that users trust because it reflects the realities of care delivery. This level of confidence can grow over time through an ongoing cycle of listening, learning, refining, and improving, with clinicians and practice leaders helping shape each step along the way.

How athenahealth is putting partnership into action

athenahealth is championing the idea that clinicians and practices should help shape AI capabilities throughout their development, rather than simply after their release. That’s why we're creating opportunities for customers to collaborate directly with the teams designing the next generation of AI-powered EHR capabilities.

athenahealth's EHR AI CoLab brings together clinicians and decision-makers to work alongside product and design teams to provide early feedback on emerging AI concepts, workflows, and user experiences. Beyond user testing, the goal of CoLab is to accelerate early-stage innovation by bringing real-world clinical perspectives into product development before capabilities reach broad release.  

By bringing together diverse specialties, practice sizes, and clinical roles, these ongoing conversations help ensure ambulatory AI implementation is grounded in real-world clinical practice. According to the athenahealth Success Community, EHR AI CoLab features over 70 user groups and regular engagement with hundreds of users to share feedback.

Indeed, clinician feedback gathered through athenahealth's user groups and EHR AI CoLab has directly influenced enhancements such as the AI-native encounter, Chart Assistant with Sage™, Patient Summaries, and Problem-based summaries. Unlike third-party AI solutions plugged into the EHR, the native architecture in athenaOne® means those enhancements are deployed seamlessly across the network through the Advanced Intelligence Layer, resulting in real-time updates to existing workflows, at scale.

As Michelle Gilreath of Mindwell LLC puts it, "For me, the most valuable thing is being heard. Literally, saying, 'Hey, we want this,' and then seeing some of it come to light."*  

Meanwhile, Dr. Stanton Stebbins of Pediatric Physicians PC said, "If you're able to help shape it, you're able to make athenaOne work better for your practice. And when it works better for your practice, you can provide better care for your patients." He added, "If you're able to help shape it, you're able to make athenaOne work better for your practice. And when it works better for your practice, you can provide better care for your patients." *

Through true collaboration, builders, clinicians, and decision-makers fine-tune AI capabilities so these tools meet practices where they are — tackling everyday challenges, blending into existing workflows, and continuously improving based on real-world experience.  

That's the promise of collaborative innovation: when healthcare organizations have a voice in how AI evolves, everyone benefits — from the teams building the technology to the clinicians and patients it ultimately serves.

Five questions to guide your AI vendor conversations

Evaluating today's feature set is only part of the decision. Asking questions of your existing EHR vendor or for those considering switching EHR systems can help reveal whether a prospective vendor views customers as partners in innovation:

• How do you gather clinician feedback throughout AI development? Look for evidence that customer input informs product decisions — not just after launch, but throughout development.
• Can customers participate in pilot programs, early testing, or advisory groups? Opportunities to engage early can help ensure new capabilities reflect day-to-day care delivery before they're broadly released.
• How do you approach AI governance in healthcare, including performance evaluation and patient safety? A thoughtful approach to oversight should be just as important as innovation.
• How do you ensure AI roadmap transparency and communicate product development plans to customers? AI evolves quickly, and practices should know how new capabilities are introduced and how feedback influences future improvements.
• How do you measure success after implementation? The most meaningful metrics go beyond feature adoption to include workflow improvements, clinician experience, and real-world outcomes.

The answers should help you understand whether your organization will simply receive new AI capabilities or have opportunities to influence how they evolve.

The future of healthcare AI starts with partnership

The future of AI in healthcare won't be shaped by technology alone. It will be shaped by the clinicians, staff, and practice leaders who help ensure those technologies solve real-world challenges.

The strongest healthcare technology partnerships don't end at implementation — they grow through shared learning, ongoing feedback, and a commitment to continuous improvement. When clinicians have a voice in how AI evolves, the result is technology that's more intuitive, more trusted, and more likely to improve both clinician experience and patient care.  

For many organizations, the practices that realize the greatest long-term value from AI may be the ones that help shape how it evolves.

athenahealth's EHR AI CoLab is working to bring the medical community together to help shape the future of AI in ambulatory care. Reach out to your athenahealth representative to learn more about how to get involved.