Many suppliers express their confidence in their AI strategy, but relatively few have established the necessary governance structures to guarantee the deployment of responsible, according to new research.
Nordic Consulting published a report this month based on a survey of 127 leaders working in health organizations, mainly hospitals and clinics. The results showed that 70% of the leaders feel at least a bit safe in the governance frames of their organization, but only 15% reports that they have scalable infrastructure.
While there is a significant enthusiasm for AI, expanding it in a medical care company is demonstrating to be an incredible complex process, said Kevin Erdal, senior vice president of transformation and innovation services in Nordic.
To achieve scalability, suppliers have to deeply dive what really means “scale” in terms of sustained use. Many organizations underestimate the continuous management needs of artificial intelligence models, especially personalized tools that consume high computer resources, said Erdal.
The preparation for the data is also crucial for the success of the AI, he said. Many respondents cited the lack of infrastructure to access and process data from disparate systems as an important barrier to the scalability of AI, Erdal said.
“It can be a scenario in which you already have the data easily aviabable or stored, but it does not necessarily have the interoperability to communicate and take some of the data of your spill to be tied to be an atha, Yyyyyyyyyyy
When it comes to new artificial intelligence tools in the health market, there is a lot of exaggeration and llamas, but they are the insecure and fundamental elements, such as data management and computer infrastructure, which determines real victories, Erdal declared.
If organizations cannot capture the correct data, the models will fail, regardless of how promising technology is, he warned.
He also noted that health leaders can overestimate their preparation due to the wide availability of supplier models. In his eyes, true preparation includes governance, infrastructure, data and, critically, change management.
“It is one thing to turn on a model, but do you need general governance to bring these operational users to that conversation?” Erdal said.
The change management process is overlooked, and organizations do not explain the general objective of technology to their end users, he explained. For example, if a hospital displays an AI model to predict no shows, the organization must communicate its plan for what to do with that idea, Erdal said.
As AI’s adoption continues to evolve in medical care, the success gained comes from striking demonstrations, it will declare less glamorous work like this, he said.
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