Imagine a world where healthcare operations have unlimited resources. What would you do for your patients?
As a nurse, I've spent my career working in a system defined by constraints. Clinicians regularly make difficult decisions about where limited time, staffing, and attention should be directed. As workforce shortages continue to challenge provider organizations, leaders are searching for ways to expand access to care without placing additional burdens on already stretched teams.
One area generating significant interest is agentic AI. These technologies show early promise in supporting routine, non-diagnostic patient engagement, including scheduling, follow-ups, check-ins, chronic disease management, education, and preventive care. By helping care teams reach more patients while directing human attention where it is needed most, agentic AI has the potential to extend the reach of care delivery.
At the same time, organizations must evaluate these tools thoughtfully. Questions surrounding governance, safety, accountability, workflow integration, and patient trust remain critical. The technology itself is only one piece of the equation.
As healthcare leaders explore solutions to growing workforce challenges, the conversation is shifting beyond what agentic AI can do to how it can be implemented responsibly and where it can create meaningful impact for patients, clinicians, and care teams.
Searching for Answers to Scarcity in Modern U.S. Healthcare
Workforce shortages are not statistics I have simply read about. I’ve experienced this firsthand. They shape daily decisions about where limited attention, time, and resources must be directed. On any given day, competing demands can delay care that would otherwise be delivered sooner.
A 2024 study by the Association of American Medical Colleges (AAMC) projected that the U.S. will face a physician shortage of up to 86,000 by 2036, while also indicating a current health professional shortage in areas where 74 million Americans now live.
The National Center for Health Workforce Analysis, a division of the Health Resources and Services Administration (HRSA), released a report in late 2025 projecting a shortage of nurses through 2038, including an 8% shortage of registered nurses as soon as 2028. Think about what this means for an already stressed and burnt-out nurse. Now consider the care that may be left behind: a missed symptom, an incorrect medication, or even a skipped bed bath.
For clinicians, the effects are visible in the routine moments where support matters, but capacity falls short: the follow-up call that cannot be made or the patient education conversation that must be shortened because another need is more urgent.
That’s why scaling patient care and outreach matters. If the projected clinician shortages occur, provider organizations will need more tools to extend that capacity.
Where Agentic AI Is Helping Relieve Pressure on Clinicians
The promise of agentic AI is not that it replaces doctors, nurses, or other clinical professionals. In fact, its purpose is most compelling when understood in the opposite way: it gives clinicians more capacity to do the work that only humans can do.
Many of the touchpoints that help patients stay engaged in their care do not require clinical decision-making, but they do require time. Scheduling appointments, answering routine questions, reinforcing discharge instructions, providing educational resources, and conducting follow-up outreach all play an important role in the patient experience. Yet these are often the very activities that become difficult to sustain when staffing resources are limited.
The reason I believe technologies like agentic AI are attracting so much attention is simple: healthcare's greatest constraint is often capacity. We know which touchpoints help patients stay engaged in their care, but delivering those interventions consistently across large populations has remained difficult.
As healthcare organizations explore ways to address this challenge, many are beginning to evaluate whether AI-supported outreach can help extend the reach of care teams.
One example is Universal Health Services (UHS), which used agentic AI for post-discharge patient engagement, including reviewing discharge instructions, discussing medications, and answering patient questions. According to UHS, the initiative enabled outreach to thousands of patients and generated strong patient satisfaction scores.
While examples like these demonstrate how AI can expand patient outreach, they also raise a more important question: How should success be measured?
The number of patients reached tells only part of the story. Success should be evaluated not only by reach, but by outcomes such as patient engagement, continuity of care, clinician efficiency, and overall care delivery performance. Provider organizations also need to understand what those interactions accomplished. Were patients more likely to follow through on care plans? Were questions addressed sooner? Did care teams gain better visibility into who needed additional support? And did clinicians have more time available for patients whose situations required direct intervention?
Generally, the long-term value of these programs will depend on their effect on patient care and daily operations. Expanding outreach may help strengthen follow-up, improve continuity between encounters, and extend support to populations that are often difficult to engage consistently. At the same time, organizations will want to understand whether those efforts help relieve pressure on care teams and contribute to measurable improvements in care delivery.
Onboarding Agentic AI with Governance and Safety in Mind
Generative AI agents can’t and won’t replace nurses or doctors. What it can do is broaden access to outreach so long as it is done with the right guardrails and safety measures.
For nurses in particular, this means spending less time on administrative tasks like admission and discharge paperwork and more time at the bedside. AI can help identify patients who require clinician intervention, with pathways that transfer patients to human caregivers. For healthcare organizations, this creates new opportunities to think about how care teams engage patients outside of traditional encounters while helping clinicians focus their attention where it is needed most.
As organizations expand the use of patient-facing AI, questions about governance become just as important as questions about functionality. Healthcare leaders should focus on three key areas to ensure these programs are implemented responsibly and effectively.
1. Establish Clear Accountability and Oversight
Healthcare leaders need clear accountability for how AI-supported programs are managed, who owns the workflow, and how success is measured. Patients should understand when they are interacting with AI-supported systems, what information is being collected, and how they can access a human caregiver when needed. Strong oversight helps organizations manage patient safety, compliance, and reputational risk while ensuring AI initiatives align with organizational standards and maintain patient trust.
2. Define Safeguards and Escalation Pathways
Effective governance requires safeguards around how patient information is handled, monitored, documented, and shared. Equally important are clearly defined escalation criteria that determine when an interaction should be transferred to a clinician, whether due to worsening symptoms, medication concerns, behavioral health risks, caregiver-reported issues, or a patient's request to speak with a person. These guardrails help ensure patients receive appropriate support when human intervention is needed.
3. Integrate AI into Clinical Workflows
AI-supported interactions fit within existing workflows. Information gathered during patient outreach has limited value if it doesn't reach the right care team at the right time. Successful programs depend on integration with clinical processes and confidence that patient concerns will be acted upon appropriately. As agentic AI becomes more common, the ability to connect AI-generated insights with care delivery workflows will play a major role in determining where the technology creates value and where human involvement must remain central.
Turning Potential into Practical Impact
Healthcare leaders are under pressure to improve access while supporting an increasingly stretched workforce. While agentic AI is generating significant interest, successful implementation begins with understanding where capacity constraints, missed outreach opportunities, and clinician burdens exist today.
From there, organizations can evaluate where AI-supported engagement may create the greatest impact, while ensuring strong governance, appropriate workflow integration, and meaningful outcome measurement.
As healthcare leaders explore the potential of agentic AI, the focus should remain on a simple question: Where can technology help extend the reach of care teams while keeping clinicians at the center of patient care?
Explore how provider organizations are evaluating agentic AI to expand capacity, strengthen patient engagement, and support care teams.
/1.%20Visuals/Website/Icons/x-twitter.svg){kind=icon}