We are getting older. Fast.
The population of American adults 85+ is projected to triple between 2015–2050, and more than 70% of people 65+ will require long-term care. Most of the available care options are expensive, inefficient and depressing. The space is ripe for innovation.
CarePredict, a “people company” with the goal of improving quality of life “for our aging parents, grandparents and loved ones,” is working towards change by pairing deep-machine learning (ML) and data analytics with a wearable device.
What does CarePredict do?
CarePredict developed TempoTM, a wrist-worn wearable that detects a person’s location and activities of daily living (i.e. eating, drinking, bathing, walking, sitting and sleeping). They take the data and use analytics to detect behavioral pattern changes. When a meaningful change occurs, family and caregivers are alerted by text and email. They can also track behavior in real-time on web-based dashboards.
With more and more data collected, CarePredict applies ML to understand what different behavioral changes might mean. It combines data with the latest industry trends to understand the impact of patterns. For example, frequent bathroom visits at night may signal early signs of a urinary tract infection. This annoyance for young people could lead to sepsis or even death in the elderly if not contained.
While CarePredict may note what a pattern may mean, the ultimate diagnosis is left to the caregiver.
Why does it matter?
Nursing homes are understaffed, low-tech, paper-filled environments. Vital signs are checked on a regular basis, but everything else – including patient feedback, behavior, and complaints – remain undocumented. “They’re not doing the kind of analysis that needs to be done, so they’re making the same errors again and again,” says Patricia McGinnis of California Advocates for Nursing Home Reform.
Seniors who live on their own do not track their health or behavioral statistics. When a fall or fever occurs, they take costly (and often unnecessary) trips to the emergency room. Countless hours and dollars are wasted because of a lack of information.
CarePredict’s easy to use technology product closes the information gap. It collects information around the clock, whether or not a caregiver is present, portrays weeks of data in graphs, and most importantly, uses ML to understand which behaviors are unique and normal for each individual person.
Charles Turner, president of LifeWell Senior Living said, “If you can measure it, you can improve it.” With the help of CarePredict, caregivers can act and improve situations. They can work to prevent illness, depression and falls by watching for when seniors move less, eat less, or sleep more.
This can lead to immense financial savings in trips to the ER and in trained staff being brought into care facilities unnecessarily. It can also reduce the stress on seniors’ families and friends. In essence, more information and understanding will lead to healthier seniors, cost savings, and peace of mind.
So, what’s next?
CarePredict has raised $10.2M since 2014. In the short term, they will use the money to bring in more electrical and systems engineers to further build out the ML and artificial intelligence (AI) platform and its reporting applications. Additionally, they are creating formal documentation and guidance for customers. This will help the marketing team when trying to partner with care facilities.
In the medium term, CarePredict plans to work with developers of senior living communities. They know from the data which rooms in care facilities have the heaviest usage and at what times – valuable information for developers and architects building efficient and intuitive building flows.
I recommend that in the short term CarePredict work with caregivers to complete the information loop, and then use ML/AI to enhance the app and analytics systems. What did the caregiver do when she noticed Charlotte sleeping more? What happened after Max’s fall?
When there is a change in behavior, the app should alert caregivers and prompt them with a question asking what action was taken, and what the results were. CarePredict should use these responses to further enhance the data set and prediction algorithm. Over time, CarePredict may even detect patterns that are unfounded in current research.
In the long term, I recommend that CarePredict be conscious of differences between patients. Age, gender and pre-existing conditions may lead to varying recommendation outputs. With more patients and more data, these trends should become clear.
Furthermore, CarePredict should focus on usability in future development. Having feedback sessions and focus groups will help mature the product. Plus, the more goodwill CarePredict has in one care facility, the more likely another facility will be willing to join a trial.
Senior care desperately needs more care, attention, and innovation.
- What are other applications for ML/AI/data analytics in senior care?
- Should the data captured be regulated?
- Is the industry ready for technological advances?
 Flinn, B. and Houser, A. (2017). Capped Financing for Medicaid Does Not Account for the Growing Aging Population. [online] Aarp.org. Available at: https://www.aarp.org/content/dam/aarp/ppi/2017/01/Capped-financing-for-Medicaid-Does-Not-Account-For-The-Growing-Aging-Population.pdf [Accessed 12 Nov. 2018].
 Longtermcare.acl.gov. (2017). How Much Care Will You Need? – Long-Term Care Information. [online] Available at: https://longtermcare.acl.gov/the-basics/how-much-care-will-you-need.html [Accessed 12 Nov. 2018].
 CarePredict. (2018). CarePredict™ Elderly Monitoring Systems to Improve Senior Care & Living. [online] Available at: https://www.carepredict.com/about-us/ [Accessed 12 Nov. 2018].
 CarePredict. (2018). Improving Senior Care | The CarePredict Difference. [online] Available at: https://www.carepredict.com/why-carepredict/ [Accessed 13 Nov. 2018].
 Bhattacharya, A. (2016). Researchers have developed sensors that can detect which senior citizens are at risk of falling. [online] Quartz. Available at: https://qz.com/769867/sensors-to-keep-senior-citizens-from-falling/ [Accessed 13 Nov. 2018].
 Schwartz, A. (2014). How Can We Reduce Adverse Events in Long-Term Care Settings?. [online] Scienceofcaring.ucsf.edu. Available at: https://scienceofcaring.ucsf.edu/research/how-can-we-reduce-adverse-events-long-term-care-settings [Accessed 13 Nov. 2018].
 CarePredict (2017). CarePredict Benefits for Developers of Assisted Living Communities. Available at: https://www.youtube.com/watch?v=9FEE9UJWpbw [Accessed 13 Nov. 2018].
 crunchbase. (2018). Company Overview. [online] Available at: https://www.crunchbase.com/organization/carepredict#section-overview [Accessed 13 Nov. 2018].
 CarePredict. (2018). CarePredict Careers. [online] Available at: https://www.carepredict.com/careers/ [Accessed 13 Nov. 2018].
 CarePredict. (2018). Actionable Insights for Independent Senior Living | CarePredict Benefits. [online] Available at: https://www.carepredict.com/independent-living/ [Accessed 13 Nov. 2018].
 Thumbnail photo: First Plymouth (2018). Smiling seniors. [image] Available at: http://www.firstplymouth.org/firstplymouth-events-page/2018/10/3/state-of-senior-care-in-our-community [Accessed 13 Nov. 2018].