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How WellSpan Health Built a Diagnostic AI Layer to Provide a Better Experience for Patients and Providers

aiOS, C-Suite, Care Coordination, Workflow
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The use of AI in healthcare is often framed as a trade-off: it makes things easier for clinicians, but patient experience may decline. Or, the patient experience improves, but frontline care teams take on more work, leading to stress and burnout.

A recent Scottsdale Institute webinar made the case for a different approach. In a conversation between Mark Kandrysawtz, Senior Vice President and Chief Innovation Officer at WellSpan Health, and Dr. Jesse Ehrenfeld, Global Chief Medical Officer at Aidoc, the discussion focused on how AI can improve both clinician workflows and patient outcomes when it’s implemented intentionally.

Clinical AI is helping health systems like WellSpan scale and accelerate critical functions in radiology and diagnostic imaging, including how quickly scans are reviewed, how urgent cases are flagged and how patients are routed to follow-up care.. Today, WellSpan is on track to have about 1.5 million medical imaging scans analyzed by AI, helping more than 6,000 patients benefit from earlier case prioritization. Kandrysawtz also shared that average imaging turnaround time has dropped from 25 hours to 2.3 hours, making same-day imaging results the norm for many patients.

That stands out even more in the context of what is happening nationally. A Harvey L. Neiman Health Policy Institute study found that imaging interpretation turnaround time for office and hospital outpatient imaging increased 113% between 2014 and 2023, with most of that increase occurring in 2022 and 2023. While delays have been rising across the industry, WellSpan has successfully moved in the opposite direction.

Workflow design is what makes AI useful.

Kandrysawtz and Dr. Ehrenfeld both emphasized that AI algorithm accuracy alone is not enough. AI has to work inside real care delivery workflows, with clear ownership and follow-through.

That is especially important for AI-flagged incidental and even non-urgent findings. Dr. Ehrenfeld shared that often, the challenge is not detection, rather it’s making sure documented findings reliably translate into follow-up care. But now, AI helps surface the radiologist’s confirmed findings, prioritizes them when necessary and ensures that the patient is routed for appropriate follow-up. For patients, this means faster answers, clearer next steps and better care.

Simply stated, care moves more effectively when health systems pair interoperability with orchestration.

Trust comes from the experience.

Kandrysawtz shared one of the more practical lessons from WellSpan’s AI journey: trust isn’t built around technology alone, but around the experience of using it.

That’s why human-centered design has been central to WellSpan’s approach. Frontline teams are involved in shaping how AI tools fit into daily work rather than being instructed to use AI solutions that wouldn’t work for their needs.

The bottom line, according to Dr. Ehrenfeld: the test for clinical AI is whether it removes cognitive load for clinicians or adds to it.

In addition, WellSpan’s AI strategy connects directly to long-term business and clinical priorities, including workforce shortages, rising patient complexity and the need to redesign care delivery for the future. 

Dr. Ehrenfeld made a similar point when addressing implementation of AI solutions: organizations move faster and better when they integrate AI into a clinical program, rather than treat it as a siloed IT project.

Patient experience is now part of the AI conversation, too.

The patient experience perspective is just as important. When imaging results come back the same day instead of a week later, patients spend less time waiting. When follow-up recommendations are proactively tracked instead of left to manual processes, clinicians are better able to ensure patients can access the care they need.  When AI tools help care teams identify actionable findings sooner, patients have a better chance of getting the right intervention at the right time.

The Aidoc-WellSpan partnership shows how clinical AI can improve the patient experience by making the system more responsive. Faster prioritization, expedited time-to-treatment and more reliable follow-up all change what the patient actually experiences.

What it looks like when AI is done right

The health systems that get the most from AI are the ones applying it to real problems, embedding it into clinical workflows and designing it around the clinicians who use it. At WellSpan, this has led to improved capacity, reduced burden and a better experience for both clinicians and patients at the same time.

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