Suki and MedStar Health's National Center for Human Factors in Healthcare have launched a research collaboration to study how ambient AI affects clinicians, patients, and care delivery. The partnership moves beyond tracking adoption numbers, applying human factors science to understand what actually drives safe and effective use in complex clinical environments.
The work will unfold across MedStar Health clinical sites as Suki's ambient clinical intelligence platform rolls out. Researchers plan to generate real-world evidence that health systems can use when scaling ambient AI beyond pilot programs.
Three research priorities
The collaboration targets three areas that determine whether ambient AI succeeds or fails in practice.
Designing for adoption. Adoption rates vary widely across clinicians and health systems. MedStar Health's human factors experts will design and test interventions to create a repeatable blueprint for scaling adoption, rather than leaving uptake to chance.
The human experience. Researchers will examine how ambient AI affects clinicians, trainees, and patients. The scope includes administrative burden, clinical reasoning, and the patient-provider relationship across diverse care settings and populations.
Patient safety and care quality. The team will evaluate how ambient AI performs inside clinical workflows, with attention to safety, care quality, and the experience of both clinicians and patients.
Why human factors science matters now
"Realizing the transformational potential of ambient AI requires thoughtful, rigorous evaluation to ensure it's implemented safely and effectively at all health systems," said Joshua Biro, PhD, principal investigator for the partnership and research scientist at the MedStar Health National Center for Human Factors in Healthcare.
Raj Ratwani, PhD, director of the center and vice president of Scientific Affairs for MedStar Health Research Institute, framed the stakes directly: "Ambient AI has the potential to fundamentally reshape the patient-provider experience, but only if it is designed and deployed with people at the center." He added that the work with Suki focuses on reducing burden without compromising clinical thinking, enhancing patient engagement, and delivering benefits across all patient populations.
Sudha Jayaraman, MD, MSc, FACS, who leads the effort at Suki, said the AI for Science & Research collaborative brings together "clinicians, informaticists, and implementation scientists to ensure that ambient AI is developed, tested, implemented, and evaluated thoughtfully and responsibly."
Methods and expected output
The research will use mixed methods, combining quantitative and qualitative approaches to study how ambient AI is adopted and performs in real clinical environments. Findings will be submitted for scientific publication and shared with the broader healthcare community.
The collaboration aims to raise the industry's evidence standards. By pairing Suki's ambient clinical intelligence expertise with MedStar Health's human factors and safety research capabilities, the partners want to define stronger evaluation frameworks for how ambient AI gets integrated into clinical care.
Why this matters for healthcare professionals
For clinicians and health system leaders, this research addresses a practical gap. Many organizations have adopted ambient AI, but few have studied what makes it work safely across different specialties, patient populations, and care settings. The interventions and frameworks that emerge from this partnership could give healthcare teams evidence-based guidance on implementation - not just vendor claims or anecdotal success stories. For professionals evaluating or already using these tools, the forthcoming publications may offer the first rigorous look at how ambient AI affects clinical reasoning, patient interaction, and workflow safety. Those looking to build deeper expertise in applying AI within clinical contexts may also find value in structured AI for Healthcare Courses & Certifications.
Your membership also unlocks:
