AI and QI—a partnership made for healthcare
Within healthcare, artificial intelligence and quality improvement have some things in common. For starters, big picture, both have potential for making life better for patients and clinicians alike.
Drilling down into differences, researchers note that QI tools “require intrinsic and contextual training” if they’re to be effective—while AI “represents a family of tools already in use and available to the practicing clinician as well as the quality improver.”
The observations are from a paper published online July 11 in Current Problems in Pediatric and Adolescent Health Care.
The authors are pediatric neurologist Grant Turek, MD, and pediatric gastroenterologist Kelly Sandberg, MD, MSc. Both are with Dayton Children’s Hospital and Wright State University Boonshoft School of Medicine.
The paper looks at the role healthcare AI can play in healthcare QI—and vice versa. Here are five points Turek and Sandberg make about interweaving AI with QI.
1. QI science can guide sound strategies for successful AI integration.
If a healthcare system were to immediately implement AI interventions without sufficient training of staff or justification, the benefits could be received in widely dissimilar ways, Turek and Sandberg point out. More:
‘QI principles can contribute to effective implementation of any new technology, including AI.’
2. QI thinking suggests it’s best to start with small pilot projects.
QI theory also recognizes the importance of measured outcomes across time to monitor results of interventions, including AI tools, and determine if the system is moving in the desired direction.
‘When new AI tools are implemented effectively using QI principles, they are more likely to yield significant benefits.’
3. AI tools can be used in QI work.
Such tools “can be used to draw upon a body of literature to summarize evidence,” Turek and Sandberg explain.
‘If done at the beginning of a QI project, the time saved is twofold: (1) the time spent in literature review, (2) the time saved by not intervening in ways that the evidence does not support.’
4. QI interventions may be created in targeted ways or less formally.
Optimal interventions “depend on learning as the team progresses through a project, being very intentional in the study portion of a Plan-Do-Study-Act (PDSA) cycle,” the authors note.
‘During the study portion, teams take the results from their planned PDSA and observations. AI could be fed that same data, drawing its own conclusions and compared or combined with the human observations.’
5. Balance and wisdom are needed when using AI tools.
“The risk of employing AI is that the team becomes overly reliant on the AI tool to interpret data and results,” Turek and Sandberg write.
Such overreliance may decrease or compromise the human contribution.
The full paper is posted behind a paywall. A condensed version is freely available here.
- In other research news:
- University of Pennsylvania: Many in U.S. consider AI-generated health information useful and reliable
- University of Sydney: Scientists create ‘biological AI’ to design molecules for solving biotech or medical problems
- Ludwig Maximilian University of Munich: AI analysis: How an enzyme associated with Alzheimer’s chooses its target proteins
- University of Pennsylvania: Many in U.S. consider AI-generated health information useful and reliable
- Regulatory:
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Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.