Why Providers Are Rethinking How to Scale Outpatient Care Without Adding Staff Burden

As the elderly population in the U.S. continues to rise, so does demand for outpatient care. In fact, a report from JLL estimates that in comparison to 2020 levels, outpatient volume growth will increase by 65.2% by 2030 for some age categories. This follows a period in which outpatient care’s share of hospital revenue grew from 30% in 1995 to 47% in 2016.

Of course, effective outpatient care doesn’t just involve in-person procedures and visits. It is also heavily dependent on what happens outside of healthcare facilities. Patient monitoring and followup between visits are an essential part of effective care, yet many providers struggle to deliver this care efficiently.

Current systems are time-consuming and often leave nurses overwhelmed, making it hard to give patients the ongoing support they need. As outpatient care continues to grow, providers are beginning to rethink how they can scale services without burdening staff who are already feeling the pressure.

Current Challenges In Outpatient Care

As Lukas Klaiber, co-founder, CPO and COO of KaigoHealth, an AI-powered care management platform focused on high-risk older adults, explains, the recovery process for patients can become alarmingly fragile after they leave the hospital.

“Patients are used to getting a high level of support while they are in the hospital, but as soon as they go home, that support fades. Staff shortages, fragmented systems and a lack of access to technology can make it especially difficult for older patients to manage chronic conditions or get appropriate care to avoid medical emergencies. We see health outcomes actually worsen because that human connection and support that was previously there 24/7 is gone.”

Klaiber understands this from personal experience due to the isolating circumstances surrounding his grandmother’s passing in an assisted living facility. In fact, this event was pivotal in Klaiber’s career focus in healthcare AI entrepreneurship, leading him to get selected as a Sigma Squared healthcare fellow and as part of a competitive AI fellowship run by a digital health venture capital firm before he went on to co-found KaigoHealth.

These experiences are hardly unique. As Harvard Health Publishing notes, post-hospital syndrome can affect patients up to seven weeks after hospital discharge because of the physical and emotional disruptions of hospitalization. While lingering effects of the original illness or injury also play a role, there is a level of shock in returning home, especially without an appropriate level of ongoing care. 

Unfortunately for patients, many healthcare facilities simply don’t have the necessary headcount to provide adequate support after they return home. The American Association of Colleges of Nursing reports a shortage of 78,610 full-time RNs in 2025, with some states projected to have a nursing shortage as high as 26% by 2035.

AI’s Entry Point In Scaling Outpatient Care

With outpatient care becoming an ongoing challenge for hospital facilities, an increasing number of providers are turning to technology — particularly AI — to scale outpatient care in a way that balances the needs of both staff and patients. Thanks to his time spent in the process-automation space with a major enterprise software company, where he worked on workflow automation and integrations, Klaiber was able to find direct applications to link AI agents with existing healthcare IT and EHR systems.

“With AI, we’re able to build an outpatient care workforce of autonomous agents that have the ability to continuously follow up with patients, monitor their adherence to care plans and advocate for their needs,” Klaiber says. “AI lets providers scale their outpatient care management up to 100 times without needing to scale their headcount. Using AI to regularly check in with patients helps providers and patients stay coordinated, especially in alerting providers when a patient needs an adjustment to their care plan or needs to come in for an earlier visit.”

With these consistent check-ins, Klaiber explains, patients are less likely to experience issues that require them to return to the ER, such as taking the wrong medication to manage a chronic condition. Studies have found that as many as 90 million adults in the United States have trouble understanding and following prescription instructions. Ongoing check-ins are vital for reducing hospital readmissions linked to these and other issues, which worsen health outcomes for patients while also increasing provider costs. 

In pilots with major U.S. health systems, Kaigo Health is already demonstrating how AI can strengthen between-visit management for high-risk patients and reduce hospital readmission rates. 

As to specific applications for AI in scaling outpatient care, Klaiber says, “Older adults are naturally going to be wary of interacting with AI. We’ve found that using a voice agent that can manage simple and accessible phone calls is a practical, straightforward way to handle these check-ins. The voice agent is easier for older adults to interact with, and then the AI can collect and deliver information to the human care team on the back end. Eventually, as trust in the tech grows, providers will be able to phase in more advanced technology, like home robot companions, to offer that same level of 24/7 personal health management that can make all the difference for at-home health outcomes.”

This approach has led to significant patient engagement rates in early pilot programs, while also helping nurse teams automate intake and triage. By using AI to maintain a connection with patients at scale, hospital systems can maintain their purpose and focus on acute care needs, while ongoing outpatient healthcare is handled in a more effective manner. Leveraging AI to streamline this work while still keeping human health professionals in the loop helps achieve much-needed balance for patients and providers.

Delivering Outpatient Care at Scale

By using AI systems to support and streamline outpatient care, healthcare providers can avoid overwhelming their existing staff while also reducing the costs that would come with increasing their headcount.

Even more importantly, using AI to help facilitate patient monitoring, follow-up, and advocacy ensures patients get the necessary support and care between visits to help them potentially achieve better health outcomes and fewer hospital readmissions.

This article is for informational purposes only and does not substitute for professional medical advice. If you are seeking medical advice, diagnosis or treatment, please consult a medical professional or healthcare provider.