Navigating the Intersection of AI and Virtual Care

The increasing AI activity is coming at a time when many digital healthcare tools and practices are gaining more acceptance among patients and practitioners. The increased use of virtual care, remote patient monitoring and consumer-oriented health apps and wearable devices has set the foundation for more extensive and visible AI use. In 2024, 64% of U.S. patients were supportive of their healthcare providers using AI as part of care delivery. That tracks closely with recent surveys about clinicians’ attitudes, as 65% of physicians said they can see an advantage to using artificial intelligence⁴ and 64% of nurses reported they’d like to have more AI tools.⁵

Clinicians’ and administrators’ optimism about AI is tempered by their concerns about potential risks to patient privacy and data, and about their organization’s ability to develop and manage artificial intelligence programs effectively. There are also worries about AI’s impact on patient-provider relationships and potential job losses. These concerns are all valid and are consistent with concerns about any new or expanded use of technology, including those raised about electronic health record systems and telehealth when those offerings were each in their emerging technology stage.

Product maturity can be a significant obstacle to safely and successfully applying AI tools and realizing their full value. There are now hundreds of healthcare technology vendors with various clinical AI offerings. Many of these companies, and the tools they sell, have only been around for a few years. They have not necessarily proven the efficacy of their tools or their ability to sustain and scale operations and do not yet have proven experience for client support. Internal operational maturity is also a potential challenge within hospitals and health systems. Many organizations lack high-level experience, knowledge and governance structures for setting policy, using AI responsibly, managing AI models and assessing the security and ethics of AI offerings from the vast array of relatively new healthcare IT vendors. These issues all need to be considered when evaluating clinically related AI tools and planning for potential adoption.

Roles that provide direct patient care are especially optimistic about AI’s ability to reduce the data entry and other administrative burdens of their jobs. Nearly 4 out of 5 nurses (79%) think AI would be very helpful for eliminating tasks that contribute to job dissatisfaction,⁶ while physicians reported their highest AI enthusiasm levels for tools that could reduce administrative burdens, such as completing documentation (cited by 54% of physicians surveyed) and prior authorization (48%). Sixty-nine percent of physicians said the most promise for AI is its potential to improve workflow.⁷ For clinical documentation, an area where an overwhelming percentage of clinicians express job dissatisfaction, AI can automatically generate conversation transcripts, extract relevant data and create a properly structured note in the appropriate documentation platform (electronic medical records, etc.) with all needed clinical, diagnosis and billing data. Clinicians then only need to review, edit if necessary and sign the final output (e.g., data entry, order). This all happens within a few minutes at most, saving clinicians valuable time that would otherwise have been spent producing the documentation themselves.

As noted, AI is integrated within some virtual care solutions that have been in place for years.⁸ AI has been embedded within virtual care platforms for functionality, such as predicting which patients will enroll in wellness, chronic condition management or other programs, selecting and delivering patient-specific educational content, supporting staff and infrastructure planning, matching patient consult requests with the appropriate clinician, conducting continuous performance monitoring and issuing predictive maintenance notifications for telehealth equipment.

Clinical applications for AI are continually evolving, including several that can leverage or augment existing virtual care tools and programs. These programs may leverage data inputs including ambient listening, video streams (aka “computer vision”), device signals and/or links to electronic health records (EHR) and other data sources, either independently or in combination, to facilitate substantial changes and improvements in care models. Some examples are shown in the table below.

These capabilities, in addition to AI-powered clinical support, raise the levels of care and responsiveness that can be provided, whether in a hospital or at an alternative site of care.

AI-enabled ambient listening functionality is a great example of how the technology can improve care delivery and quality. In this use case, the AI application “listens” to discussions and other activities at the point of care. The ability to capture conversations and other relevant sounds (e.g., alarms) and then make next-best-action recommendations to care teams is a key part of a “smart” healthcare environment, whether in a clinical or other facility, potentially including patient homes.

More advanced ambient listening solutions can provide advanced monitoring that can raise care quality. For example, AI solutions can detect an alarm, identify its source and direct a camera to locate the issue and provide a real-time stream to the virtual nursing station responsible for monitoring the patient. The camera might show that a patient is convulsing (determined by motion analysis algorithms) or that an IV bag is running low, or it could zoom in on a monitor screen. Combining this type of AI-powered audio and video analysis with signals from other devices is changing what’s possible in virtual monitoring programs, which is a reason why virtual sitter adoption growth has been strong in recent years. The advanced AI monitoring and patient protection features embedded within the Teladoc Health virtual sitter solution enable remote staff members to monitor up to 25% more patients than with solutions that do not include AI, ⁹ helping to increase the capacity of healthcare inpatient teams.