March 9, 2024

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"Common Sensors Make Common Sense", by Dr Howard Rankin

"Progress in Predictive Personal Health Analytics" by Michael Hentschel 

 " ", by Grant Renier

Current Prediction News

 

Common Sensors Make Common Sense

The revolution in gathering personal health data continues apace. The development of biosensors, either in the form of advanced medical devices or wearable sensors, like watches, is progressing exponentially.The issues especially for wearable monitors are: validity, reliability, ease of use, connection to other monitors, as well as ethical concerns about data access.

In a recent 2023 paper  Jegan and Nimi  write…

“Vital signs provide the critical information about the body organs and show the variation in an organ’s functioning inside the body. Wearable devices play an important role in monitoring these vital signs continuously either at a clinic or at home. Vital signs may vary from person to person based on their age, health status and weight.”

The reliability and validity of wearables is already in the realm of useful and will continue to improve. Moreover, the transmission of data to more sophisticated  devices will also allow more effective management and care.

One of the obvious advantages is the ease of use of wearable devices. In a 2020 paper Bruno et al., looking at ease of use amongst those using epileptic seizure monitoring, reported that the two biggest challenges with the wearables were pairing the device and charging it. In their conclusions the authors raise another interesting issue.

“Overall, participants demonstrated good performances in self-managing a wrist-worn device. Digital inequalities may extend to variations in how different individuals feel about their own disease and, consequently, manage the technology. These aspects should be addressed when technological solutions are delivered to users.”

As someone who ardently believes that a person’s perception of their illness and prognosis is critical in outcome, I totally endorse the notion that the person’s views and experience about wearables is a critical factor in the value of such technology.

Another psychological variable that is enhanced by wearable monitoring is the fact that it is not done in a medical setting but in “real life”.

You go to the doctor’s and wait almost an hour to be called. By the time you are taken into a  medical office and interact with a practitioner who now takes your blood pressure, you are almost bored to death. How reliable is that  reading? Is it a reflection of your daily life? And what if you have anxiety about such medical settings?

Constant monitoring allows measurement in settings that reflect your daily life.

As Bizzego et al, (2020) write…

“The quantification of peripheral physiological nervous signals is a core step in measuring the functioning of the autonomic nervous system. Being able to observe these phenomena in real-life and without constraints imposed by laboratory settings is a key reason for adopting WDs in scientific research. Wearable technologies enable the acquisition and quantification of physiological signals in a wide range of contexts, from personal uses to industrial and scientific research. In a general sense, wearable devices (WDs) are portable, non-invasive devices that allow the acquisition of physiological signals during daily life, without the need for external equipment .”

We are marching rapidly on to the ability to record vital physiological data in a way that is easy to use, reliable, can be communicated to other sensors and personnel and which will provide more realistic data about a person’s everyday functioning.

And then add reliable IntualityAI into the equation.

 

                                                                                                   References

Bizzego A, Gabrieli G, Furlanello C, Esposito G. Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals. Sensors. 2020; 20(23):6778

Bruno, A. Biondi, S. Thorpe, M.P. Richardson. Patients self-mastery of wearable devices for seizure detection: A direct user-experience Open Archive Published: August 23, 2020 DOI:https://doi.org/10.1016/j.seizure.2020.08.023

Jegan R, Nimi WS. On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review. Z Gesundh Wiss. 2023 Apr 3:1-16. doi: 10.1007/s10389-023-01893-6.

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

Progress in Predictive Personal Health Analytics

 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.

Continuous health monitoring and AI analytics heralds a future where healthcare is more predictive

The evolution of medical predictive analysis and the advent of continuous health monitoring AI analytics represent a transformative journey in healthcare, reshaping the landscape from reactive to proactive and personalized care. This essay delves into the history of medical predictive analysis, the emergence of continuous health monitoring AI analytics in the current decade, and identifies the pioneering companies leading these innovations.

The Genesis of Medical Predictive Analysis

Historically, predictive analytics in healthcare has focused on utilizing a wide range of data, including patient medical histories, genetic markers, demographics, and environmental factors, to anticipate future medical conditions, optimize treatment plans, and improve patient outcomes. Techniques such as regression models and machine learning have been pivotal in developing predictive models. These methodologies allow for the analysis of complex data sets, identifying patterns and relationships that can predict health-related events with remarkable accuracy.

A New Age of Continuous Health Monitoring AI Analytics

The AI-associated healthcare market is expected to reach USD 6.6 billion by 2021, highlighting its rapid growth and potential. AI applications in healthcare now extend beyond administrative tasks to include clinical documentation, medical device automation, and patient monitoring. These advancements are paving the way for a shift from reactive healthcare to a proactive model focused on health management, potentially saving billions in healthcare costs annually.

Leading Companies in Medical Predictive Analysis and Continuous Health Monitoring AI Analytics

Companies such as Fitbit and Apple, soon Samsung, with their health-tracking wearables, have been at the forefront of integrating health monitoring with predictive analytics. IntualityAI also sees some smaller players as most likely to rapidly innovate in actual personal health trend prediction, and these more nimble consumer watchmakers are already offering far wider sets of personal health statistics from a variety of watches, rings, and other skin-attached sensors at very reasonable prices, along with impressive analytical software on common smartphone platforms.

In clinical settings , companies like Biofourmis and Current Health specialize in advanced health monitoring and predictive analytics, focusing on personalized care and the management of chronic diseases. Other notables are:

Wysa: Founded by Jo Aggarwal, Wysa is a cognitive-behavioral-therapy AI chatbot used by millions worldwide. It has received recognition from the UK's National Health Service and the FDA.

Ada Health: Co-founded by Claire Novorol, Ada Health uses AI to help patients understand their symptoms and receive appropriate care. The company raised $120 million in funding in 2022.

Elemental Cognition: Founded by David Ferrucci, the inventor of IBM's Watson, Elemental Cognition is developing machine learning that aims to increase trust from human users.

The Future of Personal Health AI is Predictive: As we move forward, focus will increasingly shift towards developing AI-powered tools that not only enhance healthcare operations but also deliver personalized patient care, ultimately leading to improved health outcomes and reduced healthcare costs.

References

The rise of artificial intelligence in healthcare applications https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Definitive Healthcare: What is predictive analytics in healthcare?

NantHealth: Using Predictive Analytics to Impact Healthcare

Journal of Electrical Systems and Information Technology: Healthcare predictive analytics using machine learning and deep learning techniques

"How AI is Revolutionizing Global Health" by The Economist Intelligence Unit. This report explores the impact of AI on global health, including continuous health monitoring and predictive analytics. https://www.economist.com/intelligence-unit/ai-in-global-health

 

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

 

 

 

 

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