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CognitiveScale Blog

The What, How, and Why of Augmented Intelligence (AI) in Healthcare

By Jeffrey Eyestone Jun 25, 2019 9:33:00 AM

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One of my colleagues recently made a powerful statement: “It’s not possible to overstate the future impact of AI in all aspects of our lives. This notion of a cognitive multiplier will help augment individuals, knowledge workers, and institutions to perform in ways that no one ever anticipated.”  

How, specifically, in healthcare? The key questions are:

  • What is augmented intelligence in healthcare?
  • How does healthcare benefit from augmented intelligence?   
  • Why is augmented intelligence so valuable in healthcare and what is the cognitive multiplier?

What is Augmented Intelligence in Healthcare?

Consider this Artificial Intelligence Spectrum from our “Executive Guide to AI” that I linked to in my last post here:

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CognitiveScale is focused on the subset of AI to the right of this spectrum, related to augmented intelligence solutions like those that make knowledge workers smarter (e.g. caregivers or service reps in healthcare). CognitiveScale’s platforms also enable cognitive process improvement (e.g. cognitive claims adjudication in healthcare). Cognitive computing, and the AI solutions that leverage this technology and data science, analyze disparate data sources and observe, infer and learn in ways that mimic a human brain. When describing the “digital brain,” we at CognitiveScale refer to the D-I-A-L Loop: data → insights → action → learning. 

Next, consider the kind of insights AI can derive through analysis of data:

  1. Descriptive: What happened?  
  2. Diagnostic: What does that mean?
  3. Predictive: What could happen next?
  4. Prescriptive: What is the best course of action?
  5. Deductive: Which workflows learn and act continuously?

Deriving insights like these also starts to work in ways that mimic the human brain.  

How Does Healthcare Benefit from Cognitive AI?   

Let’s explore a couple use cases for AI in Healthcare and the application of AI solutions (based on current examples we are engaged in at CognitiveScale):

  • Cognitive Service Experience:  Service experience is challenging given the complexity of claims and benefits data for example. There are numerous AI use cases from how to engage service calls better to conversational AI solutions to more cognitive agents that can foresee service calls and equip agents with predictive (who might call based on real-time analysis of transactional data) and prescriptive insights (“next best action” suggestions).   
  • Cognitive Solutions/Program Match: Matching patients or members to wellness solutions or care management programs is possible with one reviewer analyzing one chart or file—but how does a huge organization with thousands of potential candidates per day free up knowledge workers to spend their time getting patients or members into and through valuable programs instead of spending their time figuring out who is a good candidate? Cognitive solutions perform this sort of “match” analysis on a lot of data from disparate sources, and then provide insights into which patients could benefit from certain solutions or programs (predictive and prescriptive insights)—potentially analyzing thousands of profiles at a time.

In each of these cases, the AI solutions improve over time via continuous learning as models consume more training data and feedback data from the users of these insights.  

Why is AI so Valuable in Healthcare (and What is the Cognitive Multiplier)?

Imagine a caregiver or service rep that starts each work day with descriptive insights on all of their patients or members gleaned from multiple data sources. They are presented with diagnostic, predictive, prescriptive and deductive insights that chart a well-informed course through best practices for certain situations. The intention is not to replace what they do, but rather to augment their ability to do what they do much better, more efficiently, freeing them to concentrate their time and effort on more valuable aspects of their work. Continuous feedback on these insights would then improve their quality over time. Instead of spending the whole day figuring out much of this, they start the day better-equipped to improve their key performance indicators (KPIs): clinical, financial, and operational metrics, for example. The same applies in sales and marketing, operations and technology.

AI is making a profound impact on Healthcare in all of these areas. Our clients at CognitiveScale are finding ways to drive sales, lower costs, automate processes, shorten service inquiries, etc.   Saving time and money then leads to an increase in the ability to chase new patients or members—generate more revenue—or make investments in people and other resources that allow staff to handle more valuable work.  When these sources of value start to compound on each other, you have the multiplier effect. Or, in our case, cognitive solutions deliver a “cognitive multiplier” on the value proposition.

In future posts we will elaborate on the value proposition for AI in Healthcare, and why we are

focused on practical, scalable, and trusted AI solutions.  We will look more at what that really means and why you need an AI platform in order to deliver cognitive AI solutions.  And we’ll look at Healthcare AI roadmaps as a way to prioritize the most valuable AI initiatives. Meanwhile, as a preview, check out David Murray’s (another of my colleagues) excellent overview of practical, scalable, and trusted AI in insurance here.  

About the Author:

Jeffrey Eyestone is CognitiveScale’s Healthcare AI Advisor. In this role, Jeff works with Healthcare organizations (primarily providers/healthcare systems, payers and technology vendors) on their AI journey—from strategic insight into how to develop AI competencies and centers of excellence to more tactical development of AI roadmaps and delivery of AI solutions.