With its unparalleled capacity for processing massive amounts of data, AI can drive significant advances in disease prevention, detection, diagnosis and treatment, but only if hospitals and health systems invest in an effective AI infrastructure of people, policies, resources and technology.

So argues a new report from the American Hospital Association’s Center for Health Innovation, “AI and Care Delivery: Emerging opportunities for artificial intelligence to transform how care is delivered,”which explores the use of AI as a clinical decision support tool while walking hospital and health system leaders through the “why and how” of successfully integrating AI-powered technologies into their operations. 

The report begins by walking through many of the ways that “AI technologies can improve outcomes and lower costs at each stage of the care cycle.” 

For example, in the “prevention” category, the report points to AI’s data-crunching capacity when it comes to data rich areas such as genetics, social determinants of health and tracking disease patterns.

In brief, says the report, “AI technology can take information and data from multiple sources — patient encounters, in-home evaluations of enrollees by health plans, patients’ medication use tracking by pharmacies and publicly reported demographic data — and analyze them to inform patient care and to improve population health outcomes.”

But these and the other potential improvements rely on hospitals and health systems build a clinical AI infrastructure based on four building blocks that are critical in healthcare.

People: Hospitals and health systems will need to set up organizational charts and assign responsibilities to a group of leaders who not only will oversee the priority and execution of AI projects, but also will be accountable for their outcomes.

Policies: Given the importance of effective data governance in a successful clinical AI program, healthcare organizations will need strong policies to protect the privacy and security of patient data flowing into and out of an AI algorithm.

Resources: Providers hoping to tap into AI to improve care across the continuum will need to allocate adequate financial resources to ensure their efforts achieve the outputs expected by senior leaders.

Technology: Finally, organizations will need to invest in technologies that not only integrate actionable AI insights into the workflow on the front end, but also technologies that feed accurate data into AI algorithms to generate insights.

In addition to listing AI’s opportunities and needs, the report looks at strategies and tactics to overcome common barriers to AI adoption in clinical settings.

“Far and away, the biggest challenges that hospitals and health systems will face when attempting to use AI in care delivery are concerns by physicians and patients,” notes AHA.

To address patient concerns, the report recommends using AI to engage with patients on a regular basis; leveraging AI and health chatbots to connect patients with clinicians; and utilizing AI to personalize and individualize the healthcare experience.

For physicians and other clinicians, AHA advises leveraging AI to augment clinical decision-making at the point of care, using AI to manage increasingly unsustainable workloads; and sharing clinical and scientific verification and valuation to confirm that the AI algorithm has been tested on a valid dataset, among other things.