Wolters Kluwer Health has released a 2026 predictions report outlining expectations for deeper adoption of artificial intelligence in healthcare enabled by workflow integration and stronger governance. The healthcare sector is entering a new stage of AI adoption, with predictions pointing to more strategic implementation of productivity tools and advancements in clinical decision-making technology that connect clinicians with improved information for patient care. To outline anticipated obstacles and opportunities in healthcare technology in 2026, experts from Wolters Kluwer Health have presented their outlook across a range of health technology topics in the organization’s 2026 predictions report.
The report is positioned as providing a balanced assessment of future developments and the requirements for implementing a more resilient healthcare technology environment. It outlines several key themes, including the growing impact of shadow AI, defined as unauthorized employee use of AI tools, which is described as a larger issue than many healthcare organizations recognize. The report also emphasizes ecosystem-based approaches, noting that partnerships and workflow integration can help avoid isolated AI deployments.
Additional themes include widespread workflow optimization affecting roles from nursing to medical research, AI tools at the point of care, and prior authorization processes. The report highlights increased attention on compounding pharmacies due to demand for popular GLP-1 medications and the need to ensure United States Pharmacopeia compliance. It also notes that clinical surveillance technology is increasingly based on FHIR data exchange, with infection prevention supported through interoperability and drug diversion monitoring identified as a prerequisite in 2026 AI budgets.
The outlook further indicates that clinical-grade generative AI is expected to grow when embedded in daily workflows, rigorously validated, protected by safeguards, and supported by expert oversight. Anticipated use cases include automating documentation, synthesizing clinical notes, identifying care gaps, and streamlining clinician–patient communications at scale.
The report also outlines expectations that, as shadow AI becomes more prevalent, healthcare organizations will increasingly rely on purpose-built generative AI systems trained on expert-validated evidence, transparent source citations, and the ability to deliver tailored recommendations. These systems are described as contributing to staff efficiency and care quality while requiring workflow redesigns that preserve safety and clinician–patient relationships and keep patients at the center of care.
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