Welcome to our AI in Healthcare blog series!
There is no doubt that AI is helping healthcare organizations perform at levels they never thought possible. While most AI approaches focus on replacing humans, our Augmented Intelligence software pairs people and machines to impact key healthcare constituents and processes. I look forward to exploring this broad and deep topic, and addressing the following questions through this series:
- What is Augmented Intelligence in Healthcare AI and how does this inform the use cases and solutions that leaders are leveraging to drive value?
- Which healthcare processes are best suited to Augmented Intelligence or Cognitive AI Solutions?
- How can a healthcare organization get started and drive success with AI?
- What are some best practices in enterprise-grade AI for healthcare organizations?
- What insights and competitive advantage can be gained from Augmented Intelligence?
Over the course of my career in Healthcare IT (HCIT), I’ve worked for both startups and large organizations that have implemented HCIT solutions. In my work, these solutions have driven process improvements with significant return on investment (ROI), but I’ve never seen so much opportunity to transform healthcare and to drive so much value, as I see with AI. This is being fueled by changes in healthcare regulations, adoption of innovative technologies by providers and payers, new member and patient engagement models, among other things.
At CognitiveScale, we are especially focused on AI solutions delivered through our Cortex AI software for payers and providers, their IT vendors, and those that leverage payer and provider data. I’ve worked closely in my past roles with many of CognitiveScale’s healthcare clients like Anthem, MD Anderson and Ascension, so it is interesting to now see these organizations through the lens of AI insights and transformation, both on the clinical and administrative sides.
From AI Trends and Challenges to Use Cases and Business Value
We all know that healthcare is dynamic, complex, and, of course, data-rich—and the industry is challenged to improve outcomes, lower costs, improve collections, eliminate inefficiencies, and more. It is also fraught with unique situations, sources of friction, improvement areas that point to opportunities to leverage AI, for example:
- Personalization: Healthcare clients have multiple personas at the same time: they are patients, members, customers, consumers, employees, and more; and, it is challenging to personalize solutions (clinical, financial, administrative) across these personas. Then, consider personalized solutions for providers (numerous personas there, too), business entities or groups (roll-ups of employee profiles, for example). Personalized insights via what we at CognitiveScale call Profile-of-One (hyper-personalized profiles of people, entities, even transactions) can then inform quite a few AI use cases like risk scores, prediction, cognitive service experience (serving insights in various customer service settings), advocacy (proactive reach-out), or edits/validations to transactions.
- Surveillance: Sifting through massive amounts of data from disparate healthcare sources to find signals that can trigger various alerts, improvements to care, fraud protection, patient and member advocacy, and more are key AI use cases.
- Clinical, Financial, Operational, & Strategic Transformation via AI: Driving quality care, improved outcomes, operational efficiencies, financial performance improvement, new business / payment / care delivery models, etc. is obviously challenging, and these topics have high demand for numerous AI applications.
- Trusted and Responsible Use of AI: With surveillance, personalization, healthcare data use and ownership, compliance, fraud protection, etc. comes the need to include auditing and control applications.
My belief that AI has the power to deliver on a very compelling value proposition, from ROI to strategic transformation, is borne out by experience and lessons learned with clients and partners over the years. But this belief is tempered by the need to filter through the hyperbole and ‘science projects’ and focus on practical and scalable AI use cases that can deliver on the promise of these new technologies, and then to help to prioritize solutions and initiatives as part of a realistic plan. To that end, the next couple of blogs in the series will focus on two key topics: Strategic roadmaps and Use cases for AI in Healthcare. Meanwhile, to learn more about Augmented Intelligence, take a look at our e-book here—“10 Questions About Augmented Intelligence: Executive Guide to AI.”
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.