AI Helps Bring Healthcare Home

Healthcare is coming home, and artificial intelligence (AI) is rolling out the welcome mat.

AI is augmenting, expanding, and improving the good work that caregivers provide in the home setting. As home care grows, we expect the expansion of AI to make home care more effective, efficient, and safe while helping the patients recover where they are most comfortable, and provide a better answer to the question, “Am I getting better?”

According to McKinsey & Company, an estimated $265 billion worth of Medicare services representing 25% of total care costs could shift from traditional facilities to home care by 2025. That’s a three-to-fourfold increase over 2023 rates. The rapid expansion of the home health market – which was valued at $362 billion in 2022 and is projected to grow at a compound annual growth rate of nearly 8% from 2023 to 2030 – is driven primarily by the growing geriatric population and rising incidence of dementia, Alzheimer’s, and orthopedic diseases.

Technology advancement, and AI in particular, is also fueling this growth, both by reducing healthcare costs by as much as $150 billion in 2026 and by supporting a proactive approach to care that is focused on keeping patients out of hospitals and physician offices. That is according to one study whose authors noted that “AI-based technology will have an important role in helping people stay healthy via continuous monitoring and coaching and will ensure earlier diagnosis, tailored treatments, and more efficient follow-ups.”

In-Home Applications

Real-time, clinically relevant medical technology is changing the healthcare paradigm, improving patient adherence, and clinician access, as well as refining the medical economics of treating a wide variety of pathologies, from anywhere. The inclusion of AI has significantly expanded and enhanced care provided in the home, sewing together easy-to-use mobile applications, connected devices, data capture, patients, practitioners, and payers.

The success of this push to home health can be measured in part by reporting on the patient’s ability to perform activities of daily living (ADL) such as standing, walking, and sitting. Integrating AI into these processes and procedures for measuring ADL is opening the front door for more in-depth analysis, scientific research, and virtual assistance to help answer questions like “Is this normal? What should I do? Where should I go?” 

One of the astonishing benefits of AI is its ability to learn what a proper recovery trajectory would look like for any number of pathologies and for any and all patients. Healthcare stakeholders now have the capability to access precise patient progress based on real-world clinical metrics and diagnostics represents a major breakthrough in healthcare technology.

In-Home Functional Motion  

Driven by an increase in musculoskeletal disorders, sports injuries, fractures, and joint replacements, orthopedic therapy now makes up the largest segment of the home health market with a share of nearly 58% in 2022. It is also a segment of home health services where AI – specifically advanced sensor technology powered by AI and machine learning (ML) – is making significant inroads. The best example is advances in at-home treatment for those suffering from motion-related injuries, which can take years of exercise and physical therapy to correct.

Patients perform prescribed movements while wearing AI/ML-enabled motion sensors, which instantly and accurately provide feedback using graphics and numerical results. This instant feedback loop helps patients and clinicians more accurately perceive changes in neuromuscular activity, improving adherence and performance.

AI/ML-enabled sensors also provide an in-home option for pain management, as the technology can quantify previously unmeasurable parameters like the effects of pain and objectify physical, surgical, pharmacological, and cognitive therapies.

Bringing Healthcare Home

AI-powered technologies and remote monitoring are integral to the transition of health care from traditional care settings and into the patient’s home for many of the fastest-growing service lines like pain and orthopedics. Empowering patients to self-manage their chronic and medical conditions and perform prescribed therapies while under the watchful, albeit remote, eye of their provider is key to improving the efficacy and efficiency of in-home care, leading to better outcomes.  

Getting to this place was no small miracle. It began with proper science and technology followed by patient adoption. Meaningful AI completely depends on data quality and ultimately relevant output. The input data must be precise, accurate, and reproducible, the output must be clinically meaningful: Data for the sake of data can be misinforming and problematic.

 The path to AI in the home has been a long walk. Necessary components are state-of-the-art algorithms, machine learning, and predictive analytics; easy and accessible user interfaces, validated and portable medical devices, and last but not least, updated medical billing procedural guidelines and codes.

 The pandemic, which created reduced access to direct clinical care, offered an unprecedented opportunity for AI-enabled remote patient monitoring. This permits providers to monitor their patients’ progress at home and receive alerts when issues arise, while AI-powered Healthwear^TM delivers reminders and alerts patients to take action before health conditions worsen to the point where higher acuity intervention is required. Furthermore, because these AI-powered applications are used by the patient at their convenience and where they are most comfortable, engagement in and adherence to treatment plans increases. There is no debate that engaging the patient as a proactive health participant leads to better outcomes and the ability of these devices to continually monitor the patient has tremendous benefit.

AI-powered technologies and remote monitoring are integral for the transition of healthcare from traditional settings to the home. AI’s value is its ability to analyze massive amounts of complex datasets that may include patient self-reporting, laboratory analysis, and clinician observation/assessment,  Additionally, AI helps to learn and identify specific disease and recovery patterns; and evaluate, assess, and communicate patient status in real-time, from their home—increasing everyone’s health equity.