There is a gap between AI confidence and expansion capabilities of healthcare leaders
According to the new research, many providers have expressed confidence in their AI strategies, but relatively few have established the governance structures needed to ensure AI deployment.
Nordic Consulting released a report this month based on a survey of 127 leaders working in healthcare organizations, mainly hospitals and clinics. The results show that 70% of leaders have at least some confidence in their organization’s AI governance framework, but only 15% of leaders report 15% with scalable infrastructure.
Kevin Erdal, senior vice president of Transformation and Innovation Services for Nordics, said that despite AI’s passion for AI, scaling in healthcare businesses is a very complex process.
To achieve scalability, providers must delve into the true meaning of “scale” in terms of ongoing use. Erdal said many organizations underestimate the ongoing management needs of AI models, especially custom tools that consume high computing resources.
He pointed out that data preparation is also crucial to AI success. Erdal noted that many survey respondents believe that the lack of infrastructure to access and process data from different systems is a major barrier to AI scalability.
“In this case, you can already get data or stored data at any time, but you don't necessarily have interoperability to reach out and get some data from relevant systems in the collective organization. Being able to store data is one thing, and being able to process data is another.”
There is a lot of hype and glitz when it comes to new AI tools in the healthcare market – but that determines the real victory, and Erdal declares real fundamental elements such as data management and computing infrastructure, which determines the real victory.
He warned that if an organization cannot capture the correct data, the model will fail—no matter how promising the technology is.
He also noted that healthcare leaders may overestimate their AI readiness due to the wide availability of vendor models there. In his opinion, real preparation includes governance, infrastructure, data and – critical change management.
“It’s one thing to turn on the model, but do you need overall governance to bring these operational users into the conversation,” Eldar said.
He explained that the change management process is often overlooked and organizations are often unable to explain the overall goal of the technology to the end user. For example, if a hospital deploys an AI model to predict undistribution, the organization must communicate that insight.
As AI adoption develops in healthcare, successful presentations won’t come from flashy presentations – this will come from less fascinating works like this.
Photo: Erhui1979, Getty Images
