Integrating Artificial Intelligence (AI) into Virtual Nursing Programs

Health systems are turning to virtual nursing to address staffing challenges, reduce workload for in-person nursing teams and improve patient safety. By shifting tasks that can often be safely completed remotely to virtual nurses (e.g., admissions, discharge and documentation), bedside staff can focus on hands-on patient care. This more efficient allocation of nursing resources drives measurable improvements in patient monitoring, staffing costs and nurse/patient satisfaction.

While still in the early stages of implementation, AI has potential to support virtual nursing adoption and scaling by improving provider and patient experience across the virtual nursing workflow:

• Admission, discharge and medication verification: AI tools can guide virtual nurses through standardized questionnaires based on a patient’s clinical needs and individual profile, reducing administrative burden for nurses and improving documentation accuracy.
• Ongoing clinical monitoring: AI-supported predictive analytics and motion detection can identify patient risks (e.g., fall risk, deterioration), prioritize alerts based on acuity and suggest nursing interventions, leading to improved patient outcomes.
• Care coordination: AI-supported predictive risk stratification can review patient records and identify needs across follow-up care, clinical services and social supports, reducing administrative time and effort nurses spend identifying patient needs and matching them to appropriate services.
• Patient education: AI technology can generate accessible patient education content (e.g., after-care instructions, condition-specific learning modules, medication guidance), leading to reduced administrative burden for nurses in developing content and improved patient understanding of care plans, discharge instructions and self-management guidance.

Together, AI-supported capabilities may enable virtual nursing programs to operate more efficiently and produce clearer evidence of impact. Read the full report .

This brief was developed by Manatt Health and the Telehealth Centers of Excellence at the Medical University of South Carolina and the University of Mississippi Medical Center as part of a collaboration to identify and describe opportunities to integrate AI within telehealth programs to support broader telehealth scaling and adoption. This brief is part of a series of four briefs, each focused on a different telehealth use case and based on background research and expert interviews with health system and telehealth vendor leaders. See previous briefs on the integration of AI into , and .