The potential for artificial intelligence (AI) in healthcare settings is expanding at an astonishing rate. Beyond its applications in research, customer service, and administrative tasks, hospitals are now leveraging AI to monitor patients. Remote patient monitoring (RPM), which uses digital technology to track patients outside of traditional healthcare settings, offers significant value. For instance, for patients with chronic conditions, wearable devices collect and collate data over time, providing physicians with more insights during appointments. It gives visibility into patients' lives beyond scheduled visits, overcoming a barrier to timely diagnosis and management.

Expanding the Definition of RPM

Over the years, innovators have broadened the concept of RPM. Healthcare experts recognize its importance not only outside healthcare settings but also within traditional care settings, such as long-term care facilities and for high-risk patients. In hospitals, the American healthcare system faces a growing shortage of healthcare workers. The American Association of Medical Colleges predicts a shortage of nearly 86,000 physicians by 2036, and key studies indicate a shortage of nearly 63,000+ nurses by 2030. This shortage poses a significant challenge to the healthcare system.

Teladoc's Virtual Sitter Solution

Last week, Teladoc Health announced the launch of AI capabilities to provide a "virtual sitter" solution. In inpatient hospital settings, staff are often tasked with constantly monitoring patients that require extra attention. This is a labor-intensive process that takes time away from other tasks. Teladoc's solution aims to address this by using advanced algorithms, motion detection, and pose estimation to detect patient movement that could lead to falls. This enables faster intervention by bedside staff. For example, it can monitor patients at high risk of falling and alert staff if they leave their bed.

Care.ai's Smart Room Technology

Care.ai has pioneered "smart room" technology by leveraging AI and ambient sensors. Natural language processing (NLP) interprets and transcribes clinician and patient speech, automating documentation and reducing manual input. Machine learning sensors monitor patient activity and vital processes, learning from data patterns to predict needs and detect anomalies. This ensures timely and accurate data for decision-making. The technology can be used for various purposes, such as fall prevention, preventing pressure injuries, enabling virtual rounding, and ensuring staff follow protocols. In August, medical device giant Stryker acquired Care.ai to enhance its enterprise solutions.These are just a few examples of how healthcare and technology organizations are investing in AI tools to observe patients and improve workflow. While much attention is focused on physician use-cases and administrative burdens, AI has significant potential to transform the patient monitoring space.