Three Reasons Augmented Intelligence Is the Future of AI in Healthcare

October 13, 2021
Jason Jones, PhD

Chief Analytics and Data Science Officer

Article Summary


Health systems increasingly turn to AI to help all team members make more informed decisions in a shorter time frame. Instead of an artificial-intelligence approach that threatens the critical role healthcare experts play in decision making, organizations should define AI as augmented intelligence. In his first podcast, Dr. Jason Jones, our Chief Analytics and Data Science Officer, explains how augmented intelligence can help health systems accelerate progress toward achieving the Quadruple Aim. The three unique opportunities augmented intelligence offers health systems include the following:

1. Augmented—not artificial—intelligence.
2. Think “change management.”
3. Address and overcome healthcare disparities.

Up next:
Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent and Action
Jason Jones, PhD

Chief Analytics and Data Science Officer

Trudy Sullivan, MBA

Chief Communications and Diversity, Equity, & Inclusion Officer

AI in healthcare

Under more pressure than ever before to deliver better care with fewer resources, healthcare organizations need augmented intelligence (AI) to achieve the Quadruple Aim. According to our chief analytics and data science officer, Jason Jones, PhD, AI in healthcare falls short because organizations fail to realize three key opportunities AI offers.

Opportunity #1: Augmented—Not Artificial—Intelligence

Augmented intelligence doesn’t replace humans but enhances human decision making. AI can quickly scour millions of data sets, then deliver the most relevant insight to decision makers when they need it. With access to more information than users would ever have the time to review manually, team members can make the most informed decision every time. In other words, AI helps every member of the organization make better decisions, but it won’t replace them.

Opportunity #2: Think “Change Management”

Predictive models are probably the most common type of AI in healthcare, yet many providers forget to prioritize a critical driver in predictive model successful: change management. Change management is the perspective that ensures the model supports the users’ needs and solves the problem it was intended to solve. Too often, end users start using predictive models for purposes other than for what they were intended or continue using models that don’t solve the intentional problem. With effective change management, team members constantly evaluate the model’s effectiveness, ensuring the model delivers according to its design and effectively supports team member needs.

Opportunity #3: Address and Overcome Healthcare Disparities

Healthcare disparities have a significant impact on a person’s health, and AI can help clinicians overcome those disparities. Healthcare providers can use AI to identify health disparities that put a patient at risk for a worse outcome, then use their expertise to decide which patients to focus on and how to best address their health needs.

Augmented Intelligence Is the New AI in Healthcare

Although healthcare can feel fractured at times due to so many data sources, organizations can find ways to use AI in healthcare to overcome those fractures and treat people in the best way possible.

Watch or listen to the full video podcast here.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:


Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent and Action