March  23, 2024

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This week's issue focuses on wearable device metrics for seniors.

 

“In the end, it’s not the years in your life that count. It’s the life in your years.”

-Abraham Lincoln

 

"Wearable Devices for Seniors" by Howard Rankin

"Eldercare Opportunities for Continuous AI Health Monitoring" by Michael Hentschel

"Facts about Passive Monitoring for the Elderly" by Grant Renier

Current Prediction News

 

Wearable Devices for Seniors

The growing senior population and the expanding use of wearable devices measuring critical physical functions will lead to a massive market sector and a change in senior healthcare.

In addition to the obvious advantages of constant monitoring promising early intervention there is more importantly, with IntualityAI, actionable alerts  that will prevent challenges like falls, rather than providing alerts after they have happened.

Seniors, and especially the senior seniors, are likely to be forgetful about a number of relevant behaviors, like taking meds or doing some planned activity. WDs can provide valuable and timely reminders that will help seniors stay on track with critical heath behaviors.

In a piece on SeniorHelpers.com, the author reports…

“Among seniors, failure to adhere to medication instructions as prescribed is sadly common. The Department of Health and Human Services reports that as many as 55% of seniors are non compliant with their medication, meaning they fail to take their medications according to their doctor’s prescribed instructions. Due to this, approximately 200,000 older adults are hospitalized every year due to adverse drug reactions.”

Vision loss and memory issues are amongst the leading causes of  failure to comply with medication routines. So, even simple alarms and reminders to take medication would on their own, bestow a significant benefit.

With anyone, but especially seniors, the earlier the intervention the more likely it will result in a better outcome. Early detection, ideally before the actual events have occurred, is a game-changer for senior care.

Such devices will also help keep caregivers and family members, up-to-date with a senior person’s mental and physical state, enabling them to offer timely and more effective suggestions about what the senior should be doing at any given moment.

The value of this technology is also critical because many seniors spend a large part of their lives, living alone. No-one knows how that elderly person is doing, what their current and future physical and mental states look like. It is estimated that about a third of seniors, around 14 million people, live alone. The connectivity offered by wearable sensors promises a revolution in elder care.

Reference

https://www.seniorhelpers.com/ca/visalia/resources/blogs/problems-with-medication-adherence-in seniors/#:~:text=Among%20seniors%2C%20failure%20to%20adhere,to%20their%20doctor's%20prescribed%20instructions.

by Howard Rankin PhD, Intuality Science Director, psychology and cognitive neuroscience 

ElderCare Opportunities for Continuous AI Health Monitoring

The Promise of Continuous Adaptive Prediction and Maintenance of Personal Health is predictably huge:

To Users: An unparalleled level of health monitoring, health insight and predictive accuracy, empowering people and medical professionals to make better decisions for continuous well-being like never before.

To Healthcare Industry: A transformative tool that enhances patient care, reduces healthcare costs, and sets new standards in preventive health practices.

To Investors: A unique opportunity to be part of a venture with a clear path to becoming a hundred-million-dollar company, driven by technological innovation, market demand, and a visionary approach to health and wellness.

Potential predictive benefits are immense: Despite promising past advances, the implementation of predictive analytics in healthcare faces several challenges, including data privacy concerns, the need for large and diverse data sets to train AI models effectively, and the integration of these technologies into existing healthcare infrastructures. IntualityAI offers a portable prediction technology that interprets any data stream to produce intuitive future data reflecting subtle trends that foreshadow important alerts to needed actions and decisions.

This group benefits significantly from real-time health tracking to manage chronic conditions and prevent emergencies. This segment is prioritized due to the significant and growing market size, with the elderly care market projected to reach USD 2,882.66 billion by 2030 at a CAGR of 6.80%. The increasing prevalence of chronic diseases among the elderly make this segment particularly urgent and expansive.

North America dominates the elderly monitors market because of the increased incidence and prevalence of patients with lifestyle diseases. Furthermore, well-established reimbursement policies and an increase in customer purchasing power will drive the growth of the elderly monitors market in the region during the forecast period.

Asia-Pacific is expected to grow at the highest growth rate in the forecast period of 2023 to 2030 because of the growing sub-segmentation around the world Furthermore, the presence of major key players is expected to drive the growth of the elderly monitors market in the region in the coming years.

What are other key AI software companies that take into account human bias in their predictive analytics programs? A few Medical industry players are listed below, but predictive analytics programs are not likely even as advanced as IntualityAI, since Prediction is still not considered reliable or mainstream, and their software is therefore not even oriented toward Prediction:

IntualityAI: Specializes in offering a portable prediction technology that interprets any data stream to produce intuitive future data. This technology reflects subtle trends that foreshadow important alerts to needed actions and decisions . It is dedicated to filling the human behavioral gap in machine knowledge so machines can help make better decisions, highlighting its focus on humanizing AI and dealing with the complexities of human psychology and decision-making.

Wysa: A cognitive-behavioral-therapy AI chatbot used by millions worldwide. It has received recognition from the UK's National Health Service and the FDA, indicating its reliable use of AI in considering psychological factors .

Ada Health: Uses AI to help patients understand their symptoms and receive appropriate care. Its foundation on psychological principles for symptom analysis and care recommendations makes it a notable player in the field. 

Elemental Cognition: Founded by David Ferrucci, the inventor of IBM's Watson, this company is developing machine learning that aims to increase trust from human users, indicating a focus on the psychological aspects of human-AI interaction .

SAS Healthcare Analytics, SAP Analytics Cloud, and MedeAnalytics: These software solutions process and analyze data collected by health monitors to provide actionable insights, implying a consideration of human factors in data interpretation.

While not explicitly stated as a priority, these companies, through their focus on health, decision-making, and therapeutic applications, inherently consider human biases and psychological factors in their predictive analytics programs. IntualityAI, in particular, places a strong emphasis on integrating psychological insights to bridge the gap between human cognition and machine intelligence, making it a prominent example of a software company that addresses human bias in its predictive analytics.

ElderCare is specifically focused on tracking patterns in continuous change. Any prediction methodology requires timely data points, and generates future timely data points while more loosely tracking correlations to data that influences the accuracy of those data streams. Past data patterns  are used to predict future data patterns, all as relevant as practically possible, including real-time recognitions that specific patters are CHANGING.

Conclusion: Huge markets await. Predictive Analytics is Critical, as is Continuous Monitoring.

by Michael Hentschel, Intuality CFO, anthropologist, economist, venture capitalist

Facts about passive monitoring for the elderly

Stony Brook University researchers, led by Fan Ye and Elinor Schoenfeld conducted discussions with 57 older adults, they found widespread acceptance for in-home sensors, especially if they improved the possibility of living independently. Despite concerns over privacy and data overload, most were willing to share data with providers and family. 

The proposed system uses non-invasive monitoring, showing promise in initial tests for accuracy and privacy. 

The proposed system uses non-invasive monitoring, showing promise in initial tests for accuracy and privacy. This approach aims to offer 24/7 real-time data collection without requiring senior interaction, focusing on automatic detection of health or behavioral deviations.

A study by the National Institute of Health explores the economic benefits of passive monitoring technology (PMT) for elderly and chronically ill patients. By comparing PMT with traditional care and facility-based care, it finds significant savings—up to $425 per member per month with PMT. The analysis indicates annual savings of $5,069 per person, totaling over $5.1 million for a thousand-person target group. The findings highlight PMT's potential to reduce healthcare costs while supporting independent living, suggesting substantial value for payers and healthcare systems in adopting this technology.

Now consider newly available smartwatches that are passively monitoring up to 16 sleep, heart and blood vital metrics, 24/7. Many studies, including our own, are showing that these devices are outputting reasonably accurate values and require charging only 2-3 times per week.

However, the problem still requires the individual wearers to understand and interpret the outputted charts and graphs and adjust the results to their individual health norms.

Predictive AI can significantly solve this problem by:           

Reducing the health data to ‘exception reporting’, where infrequent Alerts are outputted to the user in clearly stated narratives, like “Your stress level is predicted to be high in the next 10 minutes. Focus on relaxing.”

Personalize a wearer’s individual health norms through AI feedback learning for more accurate identification of unusual health events.

I’ve been an avid cyclist all of my adult life. Last week, my smartwatch dinged me as I was pushing up a 10% hill. My heart rate was 155. Max for my age would be 135. If it knew me better through feedback learning, I wouldn’t have cussed it out for being so ‘stupid’. Predictive AI could have been more friendly!

.                                                                    by Grant Renier, Intuality Chairman, engineering, mathematics, behavioral science, economics

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