Background Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. Objective The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. Methods Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. Results The residents of the +CP and –CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. Conclusions The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.
BACKGROUND Wearables and AI-powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE The objective of this study was to analyze how an AI-powered digital health platform, wearable device, and location system could provide improved health outcomes for residents living in assisted living communities. METHODS A total of 490 older adults at six AL communities were observed over a 24-month period. Numerous facility and resident level outcomes were measured for the intervention and control group, including staff response time, hospitalization rate, fall rate, and length of stay (LOS). The intervention group consisted of 3 communities that utilized CarePredict (n=256) and the control group that consisted of 3 communities (n=234) that did not utilize CarePredict. RESULTS Results: The data shows that CarePredict installed communities (+CP) exhibited a 40% lower hospitalization rate, 64% lower fall rate, and 67% greater length of stay than control communities (-CP). The +CP communities exhibit a 40% improvement in staff take alert time and 37% faster reach resident time. CONCLUSIONS The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior. Staff can use this information to identify seniors at increased probability for a health decline, intervene much earlier, and take pre-emptive action to protect the senior against falls, UTIs, and other conditions that left untreated could result in hospitalization. In summary, the use of this system in AL communities can contribute to faster staff response times, reduced hospitalizations and falls, and increased length of stay. CLINICALTRIAL Informed consent was obtained from all of the communities and participants included in the study.
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