“Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.”
Jeff Bezos thus elegantly explained the recent advances in machine learning, contrasting it with prior research and solutions in artificial intelligence.
Big data, predictive analytics and machine learning are revolutionizing areas as diverse as stock and derivatives trading, marketing, retail, e-commerce, mobile games etc. Healthcare is not far behind as an exciting application domain for such solutions, with predictive analytics and machine learning in healthcare becoming some of the most-discussed, perhaps most-hyped topics related to “digital health”.
According to IDC, a well-regarded analyst firm, about 30% of healthcare providers will start using cognitive analytics, AI, and machine learning technology for predictive analysis by 2018.
Cutting through the hype, while such solutions have an obvious role to play in specific healthcare realms like improving patient care, drug discovery, hospital administration and yield management etc., their ability to drive significant impact in chronic disease prediction, prevention and management is particularly exciting.
What can predictive analytics and machine learning do for chronic disease prediction and management?
The application of such technology solutions is not to replace opinions of qualified physicians or clinical practitioners, but to assist practitioners with analysis or engage and empower users to avoid chronic illnesses.
The glaring gaps in preventive healthcare today are that:
- to most people, preventive health ends with just a regular health check
- the health check report, which often contains readings for 20+ parameters is not easy to understand for most users
- the readings and results in a health check report primarily indicate prevalence or imminent onset of a condition (e.g. you are diabetic/pre-diabetic) and nothing about risk (e.g. your family history and lifestyle put you at increased risk of hypertension over the next 2 years)
- due to the inability to understand results and foresee risk, many who need help don’t visit a physician. A recent study carried out by healthi, as many as 26-28% of users who went through an annual preventive health check and needed help, do not visit physicians.
It is no wonder then that, in spite of millions of people going through some preventive health check every year, willing co-investors in the form of employers and government who partly or fully subsidize such efforts and availability of many qualified practitioners and facilities, chronic diseases lie diabetes, hypertension, heart disease etc. are becoming ever prevalent.
Predictive analytics and personalized user engagement, based on machine learning can help address many of these gaps to make preventive healthcare truly effective.
How can big data, predictive analytics and machine learning specifically address these gaps?
Predictive analytics, building upon scientifically validated chronic disease risk prediction models, can help users see a complete picture of their current and future health.
Machine learning can help improve efficacy of chronic risk predictions by constantly fine-tuning them and further honing their accuracy with each additional user.
Machine learning can also help craft personalized care paths for each user, taking into account their unique history, lifestyle and preferences, putting them in practitioners who can help them and adapting to their evolving health status.
The ability to rapidly analyze very large and often sparse data sets is the foundation for the predictive, learning and personalization pillars.
A combination of these can make the preventive health journey personalized and “one-size-fit-one”, thus making it insightful, engaging and effective.
As impressive as this progress is, it is just a beginning. The potential of predictive analytics and machine learning to drive meaningful outcomes for users and practitioners is enormous. As these technologies grow and evolve due to user adoption and advances from research, newer application realms will emerge within preventive healthcare and beyond.
For users, these technologies portend to make health increasingly personalized, demystified and unintimidating.
For practitioners, these technologies can be powerful aids helping them engage with users in a targeted manner and drive to measurable outcomes.
Companies, providers and actors in the extended healthcare realm will do well to integrate them into their growth plans and roadmap.
(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors. All the contents and images in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)
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About The Author:
At healthi, RV leads sales, marketing and user experience and delight.