What is Machine Learning and What Does It Mean for Home Care?

Artificial Intelligence (AI) and its rising implications on our daily lives is almost a regular feature in the news these days, with commentary on everything from driverless cars to image recognition software.

AI is in the early stages of what will be a dramatic impact on health care. Research institutions and hospitals are investing significant amounts of money into revolutionizing the collection and analysis of data.

The benefits of AI on the home care industry are high too, as caregivers can monitor and ultimately improve their clients’ quality of life through machine learning — a branch of AI based on mathematical algorithms and automation.

Machine learning can help close the loop on preventable events such as injuries in the home and medication errors while also monitoring any changes in a client’s health status. With this emerging technology, clients can be safer at home, enjoy a greater quality of life, and can often avoid readmissions to hospital or trips to the local emergency department. 

Predicting the patterns of future events

With machine learning, algorithms are used to analyze data and predict future events, delivering a level of insight far beyond human capability. The technology can identify hidden trends and patterns in enormous reams data from a wide variety of sources. Because of this, its use in remote patient monitoring is invaluable.  

As a complementary piece to face-to-face client care, software such as AlayaCare’s remote patient monitoring solution combined with Big Data can help people remain in their homes longer with fewer interventions required. 

Streams of incoming patient vitals on everything from blood pressure and heart rate to weight and temperature can alert caregivers to a probable adverse outcome like falls or other events, and can also reduce inadvertent over-diagnoses and unnecessary procedures. 

Remote patient monitoring is a major element of the medical buzzword these days: personalized medicine.

Imagine a patient (“John”) is particularly prone to falls. By monitoring his vitals and tracking his blood pressure through alerts sent securely over cloud-based software, his caregivers can see how he’s doing and know if he might be at risk of an episode that would precede a fall. John doesn’t need to phone and report that he’s feeling off. His caregiver can proactively inform him in advance about his health status, which in turn engages John in his own care, and allows him to take steps to ensure his safety.

Our research shows that machine learning can improve event predictions by 11% while reducing over-diagnosis by 54% compared to manually set thresholds. This not only leads to better outcomes, but also permits care workers to prioritize their time based on the urgency of client needs. This, in turn, provides a tool for insightful long-term planning and, from a business perspective, can result in major bottom line savings for home care agencies.

We are excited to see this technology unfold. In fact, we strongly feel that it has the ability to transform the home health care industry, and become a game-changing tool that truly brings the care of clients into an even brighter future.

For a deeper dive on this subject, check out our 2015 white paper

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