The success of AI in healthcare will rely largely on effective collaborations between AI experts, data scientists and analytics specialists, as well as vendors who provide solid customer training.

That’s according to a new study from KLAS and CHIME that interviewed 57 healthcare organizations and looked at use cases focused on clinical, operational, and financial applications of AI.

Among other things, the report’s authors found that the most successful AI users have worked to embed the technology in provider workflows, promote interdisciplinary collaborations, and drive change management by engaging staff members.

On the matter of consumer support, the report noted, “Through AI support services, some vendors offer resources like client success managers, dedicated data scientists, and field engineers to ensure models are built and deployed successfully and in a timely fashion. Customers benefit when vendors have healthcare experts on staff acting as these resources. Clients appreciate when vendors can evaluate an organization’s current AI state and provide support accordingly.”

Indeed, noting that AI in healthcare is still a relatively new market, the report’s authors observed that “clients need vendors who are transparent about their journey and acknowledge their need for customer partners in development. In this partnership, vendors learn best when they are responsive to constructive feedback and then correct course.”

According to a review of the report at HealthcareIT News, along with the vendor assessments, the report pointed to conversations with end users to help clear up some lingering misconceptions about how AI can work for healthcare, in particular “the notion that building data models is the most time-consuming task for AI deployment.”

As researchers interviewed see the problems, “Healthcare data is hard to clean and comes from many sources, and your organization may not have the expertise to feed the right variables or features into your models. Vendors and tools can help, but you need to do your own evaluation of the time and effort required to be successful with your models.”

Overall, the KLAS-CHIME report argued that success with AI is perhaps more dependent on an organization’s capacity to manage change than it is on the use of the technology itself.

With that in mind, it recommended a number of best practices, including:

• "Embed AI in the workflow: When creating models, observe clinicians’ workflows and find the appropriate places in which to embed models or insights so that they are located within users’ regular routines and are not disruptive. Promote AI insights to clinicians as extra information to act on, not extra hoops to jump through.
• "Bring together experts on AI, data science, modeling, analytics, and subject matter: Promote interdisciplinary collaboration. An AI project cannot be successfully rolled out unless all groups work closely together.
• "Take ownership for driving change management and operationalizing insights: Take a social engineering approach to get staff engaged in implementing changes. Report progress and successes to staff to encourage adoption.”