Article by Eric Wicklund – HealthLeaders Media
As healthcare leaders rush to implement AI tools, some are questioning whether they’re equipped, both technically and organizationally, to use the technology…
- Health systems are developing and launching AI programs at a fast pace, aiming to address back office and administrative issues and fill care gaps.
- Some experts within the healthcare industry say many organizations don’t understand the challenges of using AI or have the maturity to manage how it’s used.
- Healthcare organizations need to carefully plan how to develop and use AI, with a management strategy that addresses who, how, and why the technology is used and how to address errors and misuse…
Among those developing maturity models is MI10, a for-profit consultancy launched by Anthony Chang, MD, MBA, MPH, MS, chief intelligence and innovation officer at the Children’s Hospital of California and founder of the AIMed conference. The company’s model, called MIQ, uses 11 factors, both technological and human (along with one factor called ‘intangibles’), to measure a health system’s readiness and maturity, giving out a number on a scale of 1 to 100.
According to Arlen Meyers, president and CEO of the Society of Physician Entrepreneurs, a professor emeritus at the University of Colorado School of Medicine and Colorado School of Public Health, and a strategy advisor to MI10, the MIQ tool was used to evaluate dozens of health systems across the country, and found many that hadn’t even met readiness standards yet. Those systems scored between 26 and 88, with a median score of only 56.
“Our understanding and intelligence is that most hospitals don’t even know how to start,” he says. “And many don’t know where they are now” on AI maturity.
Meyers says healthcare organizations across the country are developing their own AI innovation centers. Some, like Vanderbilt University, have established an AI advisory board, and others, like Duke Health and Microsoft, are collaborating to launch centers of excellence that include a deep dive into AI ethics. Still others, he says, are relying on maturity models created by advisory firms and think tanks that sit outside the healthcare ecosystem.
“There are several descriptions of what have been referred to as maturity models,” he says. “I don’t think anybody has been able to validate the assumption that any of these models are accurate.”