Healthcare organizations are generating more data than ever before. AI models, predictive analytics, automation tools, and digital platforms produce unprecedented volumes of information.
Yet leaders still face a fundamental challenge: How do you turn information into better decisions, better workflows, and better outcomes?
“More information is not necessarily helpful,” said Peter Bonis, MD, Chief Medical Officer at Wolters Kluwer Health. “The work is to bring the right evidence in context into a real decision.”
Building the Intelligent Healthcare Organization, a Wolters Kluwer webinar featuring a guest speaker from Forrester, examined how intelligent healthcare organizations are operationalizing these insights across clinical, operational, and patient-facing workflows to help inform data-backed decisions.
Intelligence is measured by outcomes, not technology
For intelligent healthcare organizations, technology is only part of the equation.
“The distinction is whether an organization can consistently identify a meaningful clinical or operational problem, bring credible knowledge and relevant data to the decision, fit that guidance into a workflow, and then measure what changes,” said Dr. Bonis.
Intelligence is reflected in measurable improvements such as:
- Care quality
- Access
- Affordability
- Clinician experience
- Patient outcomes
Healthcare leaders should focus less on how many AI solutions they have implemented and more on whether those solutions meaningfully change decisions and behaviors.
Aligning patient and provider perspectives
Clinicians and patients experience healthcare intelligence differently, but both judge it by whether it improves decisions, reduces friction, and makes care easier to navigate.
According to guest speaker Shannon Germain Farraher, Senior Analyst at Forrester, intelligence should reduce friction, improve decisions, and surface evidence when it’s needed most. For patients, that translates to seamless, coordinated interactions.
“Consumers experience healthcare in an intelligent way when it feels connected, when it feels personalized, when it’s responsive, when it’s trustworthy,” Farraher explained.
Patients are less concerned with the technology behind the experience than with whether the healthcare system remembers their needs, guides them appropriately, and makes it easier to receive care.
The real challenge: moving from insight to action
Healthcare organizations have no shortage of dashboards, alerts, predictive scores, or AI-generated recommendations. The challenge is deciding which insights matter, embedding them into the right workflows, and making clear who is accountable for acting on them.
Organizations often add tools without redesigning the workflows surrounding them. Healthcare professionals may encounter new alerts, dashboards, or recommendations without clear guidance on what action to take, and too many alerts can obscure priorities and lead to alert fatigue.
“Clinicians often experience what we call intelligence as more of an interruption rather than something that is useful within the context of their workflow,” said Farraher.
True clinical intelligence requires organizations to rethink workflows, ownership, incentives, and accountability, not simply deploy new technology.
AI governance and trust are foundational
Many organizations still approach AI governance as a one-time approval process, but healthcare AI requires continuous oversight as models, evidence, workflows, and user behavior evolve.
To manage these realities, healthcare organizations need clear accountability. “If everyone owns a piece, no one owns the clinical consequences for it,” Dr. Bonis said.
Strong governance helps build the trust needed for clinician adoption, patient acceptance, and organizational confidence. Dr. Bonis added, “Trust is not granted permanently at launch. It’s earned in use.”
Focus on impact before scaling
Resources often get spread across dozens of disconnected AI pilots, making it difficult to demonstrate measurable impact.
Farraher recommends focusing on a small number of high-value workflows first and making them intelligent end-to-end: identify meaningful problems, assign ownership, define success metrics, embed intelligence into workflows, and measure outcomes.
Dr. Bonis echoed: “Choosing a small number of consequential clinical or operational workflows, making accountability explicit, defining failure patterns, the intended outcomes, the clinical owners, the operational owners… those are the critical factors. Narrow, focus, go for impact.”
Organizations that move from insight to action in one or two areas establish a repeatable model they can expand later.
The future belongs to operationalized intelligence
Intelligent healthcare is about transforming trusted knowledge into action.
As Dr. Bonis observed, healthcare leaders often assume that technical capability is the primary challenge. In reality, “the harder work is deciding what should change, integrating the tool into care, managing the change and the uncertainty around it, and then sustaining accountability over time.”
Becoming an intelligent healthcare organization is not about acquiring more technology or generating more information, but about helping people make better decisions with it.
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