| Defining Key Terms |
|---|
Technology-Enabled Care (TEC): Health care services delivered with the assistance of digital technology Artificial intelligence (AI): A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action. (FDA) Machine Learning (ML): A set of techniques that can be used to train AI algorithms to improve performance at a task based on data. (FDA) Large Language Model (LLM): A type of AI model trained on large text datasets to learn the relationships between words in natural language. These models can apply these learned patterns to predict and generate natural language responses to a wide range of inputs or prompts they receive, to conduct tasks like translation, summarization, and question answering. These models are characterized by a vast number of model parameters (i.e., internal learned variables within a trained model). (FDA) |
How Digital Technology Supports Health Care
Digital health tools and services—such as telehealth, health care apps, and wearable health monitoring devices—have expanded how, when and where care is delivered. They offer people greater flexibility in how they engage with their health care providers as well as more continuous support in meeting their health goals inside and outside of the clinical setting. Innovations in technology-enabled care (sometimes also called technology-supported care) have the power to improve patient outcomes, improve quality of care and make care delivery more effective and efficient.
The CMS Innovation Center is exploring how technology-enabled care can support people with Medicare and Medicaid, giving them more options for achieving their health goals.
How Technology Can Empower Patients
Digital health technologies can make care and wellness more accessible to patients. For example, devices like fitness trackers help monitor sleep, heartbeat, movement and other functions 24 hours a day, and health care apps can integrate that data into personalized coaching to support health goals.
Other examples of how technology supports patients:
- Telehealth and messaging software helps people interact with their health care providers remotely, which can be particularly important for reaching and regularly monitoring patients who live in rural areas with no nearby clinician, are homebound, or have limited transportation access.
- Apps (digital applications) coach people to make lifestyle changes that can benefit both their behavioral and physical health.
- Wearable devices collect health data and analyze it in real time to help people and providers track anomalies and predict risk. Examples heart monitors that can detect arrhythmias and notify providers about potential cardiac events and watches that track sleep patterns. Other devices and digital tools can monitor symptoms for conditions like migraines and irritable bowel syndrome to supplement patient reporting, leading to better diagnosis and treatment.
- Some apps pair with portable devices like EKGs and blood pressure monitors to provide users with coaching that supports their heart health.
- AI-guided exercise routines can ensure physical therapy adherence or target specific issues such as low back pain for faster healing and recovery.
- Virtual medical assistants may help patients understand their benefits, respond to routine customer inquiries and policy questions, and offer basic claim status updates to help people get answers more quickly and at any time.
How Technology Supports Providers
Technology-enabled care can be used by clinicians to enhance care delivery, for instance by helping identify issues not observable by the human eye or streamline labor-intensive processes.
Additional examples of how technology helps providers for the benefit of patients:
- AI scribes listen and take notes for providers so they can have deeper, more personal face-to-face interactions with their patients without forgetting to record any details. As a result, providers can dedicate more of their workday to caring for patients.
- AI tools, including large language models, can aid doctors when making diagnoses, for instance, by analyzing patient symptoms against large clinical datasets.
- Other machine learning models can complement human review of x-rays and MRIs or reduce patient harm in hospitals by predicting negative drug interactions.
An Example of Technology in Health Care Delivery
After experiencing intense back pain, Lupita visits her primary care physician who orders x-rays and an MRI. With the help of a machine learning program that reviews Lupita’s scans along with her electronic health records, the doctor diagnoses Lupita with spinal stenosis. He refers her to a provider group that uses in-person and virtual care for physical therapy.
Lupita meets with a physical therapist who prescribes her a course of home exercises to strengthen her lower back. A virtual care coordinator calls Lupita to help her set up a digital application that monitors her movements in real time and shows how to make corrections. She has access to support 24/7 via app or phone. The software reports her progress to the physical therapist who adjusts her care plan accordingly. Lupita’s medical care team receives any updates to her care plan so they can integrate this information into her broader care.
After a month, Lupita’s pain subsides enough for her to resume activities like taking a daily walk with her girlfriends.
Safeguards to Protect Patients
CMS is committed to supporting the safe use of AI and technology in care delivery. CMS emphasizes the responsible use of technology and AI by ensuring strong privacy protections, maintaining human oversight in care decisions, and continuously monitoring tools for accuracy and safety. These safeguards help ensure technology enhances care while minimizing risks.
This includes reinforcing guardrails that govern technology use in care delivery, such as compliance with data privacy and security protections under the Health Insurance Portability and Accountability Act of 1996 (HIPAA); safety standards for technologies regulated by the Food and Drug Administration (FDA); applicable clinical licensure and scope-of-practice rules; and other model-specific requirements to support safe and effective technology use.
Additional Information
CMS Innovation Center models that focus on the latest digital health care technologies:
Learn more about how the CMS Innovation Center promotes competitive markets in health care and the future of value-based care: