How to Consider Implementing Artificial Intelligence Models and Solutions in Health Care
Artificial intelligence is increasingly considered to be a crucial instrument in health care. But with its tremendous potential comes a variety of hurdles that need to be considered to implement AI successfully.
In December last year, the Department of Health and Human Services was the first of the federal agencies to release its 2024 AI use case inventory, which reported a roughly 66% increase in uses from the previous year, according to Fedscoop. Used for everything from operation and maintenance to acquisition and development, the numbers increased from 163 use cases in 2023 to 271 in 2024, the piece continued. While this increase demonstrates how essential it is that we come to understand AI’s potential, it also points out how important it is that we consider how it’s properly implemented into health care practice and the real world.
“We are at the start of a medical revolution, potentially similar in scale to the discovery of X-rays,” says Santiago Romero-Brufau, M.D., Ph.D., Program Director of Implementing Health Care AI into Clinical Practice, and Adjunct Assistant Professor in the Department of Biostatistics at Harvard T. H. Chan School of Public Health. “Over the next few years, AI will become more ingrained in the day-to-day clinical workflows and operations. And it is critical that clinical leaders understand how to assess and plan the implementation of these algorithms and technologies into clinical practice.”
New Advances Are at Risk of Falling into An Implementation Gap
The rise in AI usage is a great indication of how it will be increasingly influential in the future.
“Recent advances in large language models make clinical information much more accessible for AI and machine-learning models,” says Romero-Brufau. “This will only help accelerate this revolution.”
Unfortunately, an implementation gap composed of the space between what is developed and what is used often restricts the models’ potential. Within that gap, there are a variety of barriers that obstruct AI’s implementation in a clinical setting. These include difficulty with information technology and change management issues, as well as some physicians not seeing the benefit. To help patients in clinical practices, leaders must understand how to navigate this space successfully.
Why Should Health Care Leaders Understand How to Apply This Technology?
Organizations are eager to learn how AI can be of value to their institution and the patients they treat. People in leadership roles in those institutions who understand how to apply this type of technology can then translate AI or machine-learning work into value for both patients and the institution. Also, employees in leadership positions are often responsible for deciding which AI vendors each institution works with. Some of these positions include:
- Clinician
- Physician Leader
- Chief Executive Officer
- Chief Information Officer
- Chief Innovation Officer
- Chief Medical Informatics Officer
- Chief Medical Officer
- Data Scientist
- Director
- Innovation Specialist
- Implementation Specialist
- Product Manager
“It is also important for startup leaders and technology companies to understand how to design and implement AI solutions into the clinical workflow,” Romero-Brufau adds.
The power of artificial intelligence and machine learning is unlimited. To avoid this information falling victim to an implementation gap, professionals need the education to properly put these technologies to use. Equipped with the skills to properly implement AI solutions, clinicians and executives can revolutionize the future of patient care.
Harvard T.H. Chan School of Public Health offers Implementing Health Care AI into Clinical Practice, which focuses on providing clinicians and executives with the knowledge and skills to ensure AI solutions are successfully implemented in the clinical setting.
