GNS Healthcare Blog

GNS Healthcare Blog

How AI is Replacing Prediction with Discovery Using Data

  

Hardly a day goes by without someone publishing an article on how artificial intelligence (AI) is revolutionizing the healthcare industry. No doubt AI is impacting multiple areas of the healthcare landscape from biopharma to health systems, to heath plans to patients.

One recent survey reported that 90 percent of pharma companies believe that AI is critical to their success.[1] Another report has 42 percent of healthcare system leaders saying they have or are planning to add AI as a tool for disease management.[2] Some even suggest AI will eventually replace physicians when diagnosing patients.[3]

There is no question that AI is making an impact across the healthcare ecosystem from drug development to diagnostics to care interventions and physician tools to medication management. But among the wide-ranging applications of AI, one of the most important, and ultimately most beneficial, is its ability to accelerate the pace of discovery, helping healthcare stakeholders advance to the practice of precision medicine. The enormous power of AI, coupled with the increased availability of growing patient data sets and expanded computing power, is taking healthcare from population-level predictions of what may happen to novel discoveries of what will likely happen.

 

AI and Causal Machine Learning

There are several terms used with AI and it’s important to understand the different types. For starters, AI is a branch of computer science that deals with the simulation of intelligent behavior and the capability of a machine to imitate intelligent human behavior.

Machine Learning is the ability to learn concepts using data without being explicitly programmed by a human. Machine Learning is made up of different methodologies by which artificial intelligence can be achieved.

Machine Learning and AI have done a number of remarkable things finding correlations and patterns in big data and has worked well in the area of online advertising and retail. But in the world of healthcare and medicine, researchers, clinicians, and patients need something beyond the correlations. Healthcare demands causality, because in order to know why, it is crucial to understand the underlying mechanisms of systems.

The big breakthrough in AI that takes us from correlation to causality and therefore provides the best means for discovering valuable new insights is Causal Machine Learning (CML). The most powerful form of machine learning used for AI, CML provides healthcare stakeholders with meaningful insights that simulate what will happen that results in better care management and more successful drug development.

 

Benefits of Causal Machine Learning

CML is quickly proving to be beneficial for all major stakeholders in the healthcare community.

For biopharma companies, CML accelerates the drug discovery process by automating the scientific method of research. The expanding amount and types of patient data can be fed into the CML engine and help quickly identify patients that fit various clinical trial protocols. This eliminates the time consuming, expensive manual hunt-and-peck approach to finding eligible trial participants.

Health Plans can benefit from CML by helping them better meet the needs of their members, delivering personalized care pathways. They are able to target care management programs to the right members at the right time across geographies and lines of business. Instead of relying on the “average” patient as determined by data driven computer models that attempt to categorizes members based on risk of future health care events, CML allows them to identify the optimum care management programs that are most effective for each individual patient.

AI and CML has brought us to the cusp of an exciting new period in healthcare. We no longer have to rely on standard reports, statistical analysis and forecasting. The power of AI and CML is taking us from “what will happen next?” to “what will happen if I do this?” This advance to optimization and inference is the real breakthrough in AI and promises to revolutionize the future of healthcare.

 

For more on the pivot to AI in healthcare and the power of machine learning, check out our  infographic, Understanding the Reality of AI in Healthcare: From Prediction to Modeling to Discovery.

 

[1] Pharmaceuticals: When AI Adoption Has Gathered Most Momentum, Infosys report, 2017

[2] Top of Mind for Top U.S. Health Systems for 2018, Center for Connected Medicine, December 2017

[3] Your Future Doctor May Not Be Human. This is the Rise of AI in Medicine, by Abby Norman, January 31, 2018, Futurism

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