Agentic AI meets Hospital-at-Home: Breakthroughs and barriers

The dawn of agentic automation in Hospital-at-Home

Hospital-at-Home (H@H) has moved from a pilot project to a recognized care model over the past decade. Yet scaling it beyond niche use has remained elusive, hindered by staffing shortages, operational complexity, and the inherent unpredictability of patient needs outside the hospital walls. Now, a new generation of artificial intelligence, “agentic” AI, is promising to break through these barriers.

Agentic automation refers to AI systems that can observe, decide, act, and adapt on their own. Unlike traditional “assistive” AI, which reacts to human prompts, these systems proactively set goals, execute tasks, and refine their performance through feedback loops. This capacity for self-directed action could be transformational for H@H, where care delivery is distributed, continuous monitoring is essential, and logistical coordination often outstrips human bandwidth.

The market for agentic AI in healthcare, projected to reach $4.96 billion by 2030, is expanding at a 46% annual growth rate. Researchers have documented its capacity to enhance diagnostics, personalize care plans, and make treatment decisions that align with evolving patient conditions. Meanwhile, real-world deployments are beginning to hint at what fully autonomous H@H could look like.

The healthcare industry is facing shortages of 100,000 workers by 2028, and more than 187,000 physicians by 2037. Agentic AI offers a pathway to maintain care quality while addressing workforce shortages and reducing care costs. H@H programs have shown the ability to reduce care costs by 30% while achieving lower mortality rates across key diagnostic categories.

Already, companies like hellocare.ai are embedding AI into bedside systems, while Evidently is integrating it into the core of clinician workflows. These are early signs of a shift in how H@H will likely be organized and delivered moving forward.

Related Content: Hosptial at Home Technology Summit – On Aug. 28, 2025, join the leaders shaping the infrastructure, reimbursement strategy, and workforce models behind successful H@H deployments. Designed for CIOs, CMIOs, CNOs, and innovators in home-based care, this summit is the catalyst for meaningful dialogue, cross-sector collaboration, and the bold moves needed to scale care beyond the walls. Register Now!

Smart wearables and ambient technology are redefining remote patient care

In July 2025, hellocare.ai rolled out a native integration with Epic’s MyChart Bedside TV platform, extending its footprint to more than 70 health systems. Separately, AdventHealth is deploying hellocare.ai across more than 50 hospitals and over 13,000 patient rooms as part of an enterprise-wide virtual care implementation. The platform now offers virtual nursing, real-time patient engagement, ambient documentation, and even hospital-at-home capabilities — all wrapped in an enterprise-grade solution.

Voice-activated care requests, powered by Alexa integration, allow patients to summon assistance or initiate telehealth visits without waiting for a human intermediary. Behind the scenes, the system triages requests, initiates workflows, and keeps care teams updated, freeing nurses from repetitive coordination tasks.

Agentic capabilities are also reshaping remote patient monitoring (RPM). The BioIntelliSense-Medtronic partnership centers on the BioButton, a wearable that tracks vital signs up to 1,440 times per day. Its AI doesn’t just log numbers, it learns an individual’s baseline and flags subtle deviations that may precede a health crisis. In some cases, it can autonomously trigger alerts, suggest interventions, or adjust care plans without requiring constant clinician oversight. The 2022 acquisition of AlertWatch layered on clinical decision support for high-acuity settings like ICUs and operating rooms, bringing continuous, context-aware monitoring into the home.

Even diagnostics, long tied to in-person encounters, are being reframed for autonomous use. Eko Health’s SENSORA platform, granted a Category III CPT code effective July 2025, uses AI to detect structural heart murmurs with double the sensitivity of traditional stethoscopes. Clinical trials have validated its accuracy, and its inclusion in the Hospital Outpatient Prospective Payment System marks a milestone on the path to routine reimbursement. With AI-guided auscultation available in a patient’s living room, early detection of cardiac disease becomes not only possible but potentially standard practice.

Other diagnostic innovations are following suit: Scanvio’s AI-guided ultrasound and Teal Health’s at-home cervical cancer screening both illustrate how autonomous interpretation can bring traditionally hospital-bound diagnostics directly into the home.

Conversational interfaces cut through EHR fatigue and admin overload

While patient-facing tools grab attention, the most profound agentic gains may come from reducing the cognitive and administrative burden on clinicians. Evidently’s July 2025 partnership with Allina Health aims to process millions of patient encounters annually, automating information retrieval, coding support, and documentation review.

Evidently’s platform semantically indexes both structured and unstructured data, from clinical notes to scanned external records, using a combination of large language models, deep learning, and knowledge graphs. Clinicians can query this data through a conversational interface embedded in the EHR, dramatically reducing the time spent hunting for relevant information. At University of Iowa Health Care, providers using Evidently reported a 31.7-point jump in their Net EHR Experience Score, suggesting that relief from “EHR fatigue” is more than anecdotal.

The case for automation is getting more urgent. Administrative tasks now consume up to 20% of a health system’s budget, while physicians spend about 13% of their time on documentation. Agentic AI offers a promising path to reduce costs and improve efficiency, especially as the U.S. faces a projected shortage of 73,000 nursing assistants by 2028.

The same principles apply to care coordination and staffing. In an H@H program, visit schedules and staff assignments shift constantly in response to patient acuity, geographic constraints, and workforce availability. AI agents can absorb these variables, generating optimized route plans for nurses and paramedics, dynamically reassigning cases when emergencies arise, and even booking interdisciplinary team meetings without manual intervention.

AI agents can also bridge gaps in specialist access, as seen in systems that now use AI to seamlessly connect home-based patients with remote cardiologists, intensivists, or other experts. By triaging cases and automating consult logistics, these tools reduce delays and extend scarce expertise into the home.

Supply chain automation: the hidden engine of scalable H@H care

If patient care is the visible front end of H@H, supply chain management is the hidden engine. The logistics of delivering hospital-grade care at scale require precise coordination: kits for lab draws, wound care, or infusion therapy must arrive exactly when needed, in the right condition, and often at short notice. Agentic AI can track inventory in real time, predict consumption patterns, and trigger resupply orders autonomously.

Mass General Brigham’s partnership with Best Buy Health, announced in late 2023. offers a glimpse of this model in action. By pairing Current Health’s monitoring platform with Best Buy’s Lively Mobile Plus devices and nationwide Geek Squad support, the collaboration aims to shift 10% of inpatient care to the home within five years. Since 2020, the system has cared for roughly 3,000 patients at home, and leaders point to gains in both patient satisfaction and staff retention. AI-driven logistics could amplify those results by ensuring that the physical infrastructure of care keeps pace with its digital transformation.

The technical infrastructure requirements for agentic H@H are substantial but manageable. Healthcare organizations need robust broadband connectivity, secure cloud computing platforms, and interoperable devices capable of real-time data transmission. The convergence of 5G networks, edge computing, and advanced wearables is creating the technical foundation necessary for seamless autonomous care delivery. However, successful implementation requires careful attention to cybersecurity frameworks, as home-based care expands the attack surface for potential data breaches.

Roadblocks on the way to fully autonomous home care

Despite the momentum, the path to fully autonomous H@H is neither automatic nor guaranteed. Several strategic considerations stand out:

Regulatory and reimbursement frameworks remain fluid. The CMS Hospital Care at Home Waiver, covering 400 hospitals in 39 states, is set to expire at the end of September 2025. Legislation to extend it through 2030 has bipartisan backing, but uncertainty persists. CMS has begun developing new quality measures and reporting requirements specifically for AI-driven care models, signaling regulatory recognition of the technology’s growing role. The agency’s provisional inclusion of AI-powered diagnostic tools like SENSORA in reimbursement frameworks suggests a pathway toward broader coverage of agentic technologies.

Competitive landscape dynamics are intensifying as venture capital investment in agentic AI approaches $2 billion in 2025, with nearly 30% of healthcare startup funding going to AI-focused companies. Specialized healthcare AI companies are emerging rapidly. Key competitors in the H@H space include traditional telehealth providers adapting their platforms, health system-developed solutions, and AI-native startups targeting specific workflow challenges. Major tech players are also signaling their intent: Microsoft’s AI Agent Orchestrator points to a future of multi-agent systems, while Google’s work on autonomous orchestration and AWS’s health cloud initiatives both aim to make agent-driven workflows a standard enterprise capability.

Implementation challenges extend beyond technology to organizational change management. Clinician adoption remains a critical hurdle, with many providers expressing concerns about AI decision-making transparency and potential liability issues. Successful deployments require comprehensive training programs, workflow redesign, and careful attention to user experience design. Organizations must also navigate complex integration requirements across existing EHR systems, medical devices, and administrative platforms.

Workforce transformation and upskilling will be essential as agentic AI changes the nature of clinical work. Rather than replacing clinicians, these systems are more likely to shift their roles toward oversight, exception handling, and higher-level patient engagement. Preparing teams with new skills and redesigned workflows will ensure AI serves as augmentation rather than disruption. 

Ethical governance is equally pressing. Autonomy raises questions about accountability: If an AI agent makes a harmful decision, where does responsibility lie? Bias in training data, lack of transparency in decision-making, and inconsistent oversight could erode trust among both clinicians and patients.

Data privacy and security challenges multiply in a distributed environment. Home-based care involves transmitting sensitive health data over consumer-grade networks, often to and from multiple devices. Ensuring compliance with HIPAA and other frameworks requires more than encryption; it demands rigorous segmentation and monitoring to prevent data from leaking into unauthorized channels.

Integration and interoperability could be the ultimate bottleneck. Healthcare already generates more than a third of the world’s data, yet only a fraction is used effectively. Connecting agentic systems to legacy EHRs, remote monitoring platforms, and logistics software requires technical and organizational commitment that many providers underestimate.

Momentum is building for sustainable, equitable AI-driven care at scale

The shift from assistive to agentic AI is a clear inflection point. In 2024, fewer than 1% of enterprise applications featured agentic capabilities, but forecasts indicate that number will quickly climb to 33% by 2028. For H@H programs, this signals a transition from human-led models supported by digital tools to systems where software takes the lead and humans step in only when necessary.

The financial returns are already becoming evident. Early adopters report significant improvements in key performance metrics: reduced readmission rates, shorter recovery times, and enhanced patient satisfaction scores. As CMS continues to evaluate the positive clinical outcomes demonstrated by H@H programs, the economic case for agentic automation becomes increasingly compelling.

This future will not arrive fully formed. It will require deliberate policy design, rigorous evaluation of ethical implications, and sustained investment in interoperability. But the outlines are already visible: a home that acts as an extension of the hospital, with AI agents quietly coordinating care, managing resources, and anticipating problems before they escalate.

Beyond outcomes, ROI is becoming clearer as organizations report gains from more efficient staffing, streamlined logistics, and new reimbursement pathways for AI-enabled diagnostics.

At the same time, market consolidation — such as the DispatchHealth and Medically Home merger — underscores the scale pressures driving this shift. Taken together with big tech’s multi-agent orchestration push, these moves suggest that Hospital-at-Home may serve as one of the earliest proving grounds for autonomous systems that could ultimately transform the broader healthcare enterprise.

For healthcare leaders, the question is no longer whether agentic automation will shape H@H, but how to ensure it does so in ways that enhance equity, safety, and sustainability. The window to influence that trajectory is open now, but it won’t stay open forever.