AI is moving through healthcare faster than organizational readiness
As AI tools proliferate in the healthcare industry, some clinical and administrative teams feel they can’t wait for their organization to provide the approved solution they need, so they are using unauthorized solutions and introducing organizational risk. This is known as “shadow AI.” In a recent Wolters Kluwer Health survey, 40% of healthcare professionals said they had encountered unauthorized AI tools in the workplace, and 17% admitted to using them.
Wherever healthcare leaders currently are in their AI strategies, the user-level need for AI-driven workflows is not abating. Hospitals are experiencing:
- Increasing use of AI in prescribing, dosing, and medication review.
- Rising exposure to shadow AI driven by workflow pressure.
- Growing regulatory, legal, and board-level scrutiny of AI governance.
- Persistent patient safety risks tied to medication errors and interactions.
With rising demand for care, an aging population, and constrained workforce capacity, AI is expected to help teams act faster without sacrificing care quality or patient outcomes.
How is digital health technology reacting to new AI needs?
At the core, what matters most hasn’t changed — patient-appropriate treatment choices that align with best practices are still essential when it comes to medications. What has changed is what health systems expect from the data and content that power AI-enabled solutions.
The sector is responding quickly, but not evenly:
- Many vendors are ahead of the curve when it comes to layering AI on top of workflows to speed up access to information and reduce administrative burden. This has demonstrated early progress in summarization, retrieval, and automation — particularly in lower-risk, more predictable use cases.
- The industry continues to struggle with data foundation and trust. AI can accelerate decision-making, but only if it’s grounded in evidence-based, curated, and current clinical content. Vendors that lack strong underlying data — or that can’t clearly explain where AI outputs come from — risk losing credibility with clinicians and health systems very quickly.
- Nuance continues to challenge AI-enabled solutions. Clinical care includes edge cases, rare diseases, and atypical dosing scenarios that aren’t always well-represented in generalized models.
Medication intelligence: More than drug data
As AI increasingly influences medication decisions at the point of care, hospitals and health systems are no longer evaluating digital health vendors on model sophistication alone. They are assessing whether vendors can demonstrate evidence of continuity, deterministic logic, explainability, traceability, and enterprise‑ready governance, especially where error margins are smallest.
That’s where medication intelligence becomes critical.
Traditional drug data integrated into digital health solutions is largely referential. It gives users access to information, but requires them to read, interpret, and synthesize it before determining what to do next.
Medication intelligence goes beyond that. It combines drug data with clinical reasoning and context to help answer the question: “What should I do next?” It performs the synthesis step that previously sat entirely with the end user.
That might involve:
- Marrying drug data with patient-specific factors.
- Summarizing implications at the population level.
- Projecting downstream impacts for pharmacy operations or supply chain.
The key difference is that medication intelligence delivers actionable guidance, not just information.
Why medication intelligence matters: Top four factors
There are four key reasons AI-enabled solutions need a medication intelligence layer rather than relying on traditional healthcare or drug datasets:
- Actionability: AI increases speed and volume, but without medication intelligence it simply surfaces more information faster. Intelligence helps produce outputs that are prioritized, summarized, and usable in real-world workflows.
- Trust: If the underlying data is incomplete, outdated, or inconsistent, AI will produce unreliable results. Medication intelligence provides a curated, clinician-reviewed foundation that supports traceability and confidence in the outputs.
- Context: Traditional datasets generally don’t account for patient characteristics, population-level trends, or financial and operational considerations. Medication intelligence brings that broader context into AI-enabled drug clinical decision support, so recommendations align with how healthcare actually operates.
- Scalability: From a vendor perspective, medication intelligence can act as a reusable foundation for development. Without it, teams often need to rebuild clinical logic for each new use case, market, or care setting — and constantly rework that logic as drugs, guidelines, and costs change. With a medication intelligence layer, a single curated foundation can support multiple solutions, and AI capabilities — such as agents, prompts, or automation — can be developed once and extended across products more confidently and efficiently.
To learn more about key components of trustworthy medical intelligence and how digital health tech vendors are using them to build and scale successful AI-driven solutions, download the Fierce Healthcare and Wolters Kluwer whitepaper, “How medication intelligence scales trust in healthcare tech innovation.”
