AI in Healthcare: Clearing the Hidden Work So Clinicians Can Practice Medicine
Most people picture AI diagnosing cancer or reading scans. The quieter truth is where it's already making a difference: clearing paperwork, routing messages, and cutting repetitive clicks so clinicians can spend more time with patients.
"If we can automate, if we can accelerate that task that they are forced to do, it allows them more time to focus on their true purpose, which is diagnosing disease," said Jesse Tetreault, senior solutions architect at NVIDIA, during the AI4Health event at the University of Delaware.
Paperwork, not patients, is the bottleneck
When AI does its job in healthcare, patients may never notice it. Clinicians do. They get more minutes at the bedside and less chart-churn. As Tim Gibbs of the Delaware Health Force noted, these mechanisms can help teams stay energized and patients well served, even as demand shifts.
That's the metric that matters: time back to care. Not novelty. Not headlines. Time.
Automation that actually saves minutes
Susan Smith, a nurse researcher at ChristianaCare, shared results from a humanoid robot pilot built to handle deliveries. The lesson wasn't about robots; it was about workflow reality. Clinical work runs on urgency and timing.
Robots slowed down by elevator traffic don't help a nurse who needs supplies now. The pilot surfaced friction points and set expectations for the next iteration: tools should meaningfully save clinician time and support patient-facing work.
Burnout and retention: find the workflow signals
Turnover is expensive. Much of it is preventable. Healthcare analytics startup Atalan uses machine learning on passively collected signals-time spent charting, messaging volume, after-hours work-to spot patterns managers miss.
"People think that burnout is because you're not resilient enough," said cofounder Tiffany Chan. "It's not fair and it's not true. Most burnout is because you were overworking." Their data showed that small workflow fixes can drop turnover risk while freeing doctors to spend more time with patients.
For context on national efforts to reduce EHR burden, see the Office of the National Coordinator's strategy on clinician burden reduction: ONC's burden reduction strategy.
The right info at the right moment
Electronic health records hold years of history. Surfacing the one note that changes today's decision is the hard part. "I don't have the time to go through the records from five years ago," said Tom Schwaab, a urologist and health system leader at ChristianaCare.
AI-assisted summaries can reduce unnecessary tests and sharpen decisions. "We have the data," Schwaab said. "We just need a way to analyze."
Data quality over data volume
More data doesn't always mean better models. "As you add more data, you're adding more noise, so you have to filter down to what's actually important," said Connor Callahan, cofounder of Acellus Health. Structure is the fix.
Well-structured data preserves clinical judgment by making signals clear and context usable. Less scavenger hunt, more clinical reasoning.
Adoption requires proof, not promises
Health systems are right to be careful. "AI holds tremendous promise with regard to clinical decision support within our neonatal feeding management platform," said Adam Dakin, CEO of Keriton. "However, we are taking a thoughtful stepwise approach."
Validation, safety, and regulatory readiness come first-especially for anything near clinical decision-making. Pilot, measure, verify, then scale.
Practical steps for clinical leaders
- Map the work that steals the most minutes (charting, inbox, prior auth, discharge summaries). Start there.
- Set one success metric: minutes returned to patient care per clinician per shift.
- Pilot in a single unit. Co-design with frontline staff. Collect before-and-after time studies.
- Prefer tools that integrate into existing EHR workflows and reduce clicks, not add new screens.
- Structure your data. Standardize terms, templates, and orders to cut noise.
- Create a lightweight governance path for AI features: clinical oversight, bias checks, validation, rollback plan.
Augmentation, not replacement
The future might include robots in hospital halls, but the goal is the same: help clinicians practice at the top of their license. As Gibbs put it, the goal isn't automation. It's augmentation.
If your team is exploring practical AI skills for healthcare roles, see these AI upskilling paths by job.
Bottom line
AI's real value in healthcare shows up in the background. Fewer clicks. Faster summaries. Cleaner data. And more time for the one thing that matters most-care at the bedside.
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