Alexis is director of content at AIMed, with responsibility for the research, development and delivery of products across events, digital and publishing. A highly experienced events executive with a career focus on the intersection between healthcare and technology, he is also a school governor leading on teaching, learning, and quality of education.
“One of the biggest issues we have in the healthcare system is not having enough money (AI) for healthcare, but it is how that money (AI) is spent (deployed).”
Health advocate Kat Lahr quote with editing (ACC)
We conclude our 5-part series on AI in healthcare systems by introducing a simple strategy for the measurement of AI capabilities in a healthcare system. This basic AI assessment score can be tailored to any healthcare organization – not only as an initial assessment, but also as a methodology to follow the ongoing progress of AI capability.
One strategy is to deconstruct AI capability to its component parts: technology, human, and both.
This part of the assessment can be based on four separate sub-scores in the following areas:
- Health data
- This includes access to data, quality of data storage, diversity of data, support from data engineers and database architects, etc.
- Data science
- This sector is the quality of data science, the level of data science in both clinical sectors as well as administrative areas, etc.
- IT infrastructure
- This is coupled to healthcare data and includes all aspects of information technology, from databases to EHR.
- This domain is vastly underemphasized in healthcare data discussions, but is vital for AI in the healthcare system.
This part of the assessment is focused on the people involved in the AI effort within healthcare systems:
- AI strategy
- An important part of the human aspects of the AI effort is how artificial intelligence is incorporated into, or emphasized in, the organizational strategy.
- AI leadership
- An essential aspect of successful AI transformation is the makeup of the leaders in this area in the healthcare system.
- AI team
- In addition to the aforementioned leadership, the AI human resources (or at least access to expertise) is also critical to maintain momentum.
- Team dynamics
- With leadership and supporting members in place, a key element of AI success is just how the team dynamics and esprit de corps work in this challenging domain.
Machine and human
- One key assessment is to honestly evaluate the workflow and process of deploying an AI solution to a problem.
- With all the nuances of AI in medicine, this sub-score takes into account technology-human aspects that can be helpful or obstructive that are not listed above.
The above ten category system can serve as a preliminary tool for a healthcare system to measure its AI capability and progress during this journey to enable AI to improve patient outcomes and experience as well as the Quintuple Aim.