Healthcare's digital skills gap has a measurement problem, and new research is pushing for a fix

Most health systems investing in digital transformation have no reliable way to measure whether their staff are actually equipped to use the tools they are deploying. That is the practical conclusion sitting beneath two separate peer-reviewed studies published in 2026, one in the International Journal of Medical Informatics and one in npj Digital Public Health. Together, they expose a structural weakness in how the industry approaches workforce readiness: the assessment infrastructure has not kept pace with the technology rollout.

A toolkit inventory that falls short of the need

Researchers at an Australian institution conducted a rapid review of all available validated tools for assessing digital health competence in the healthcare workforce, publishing their findings in the International Journal of Medical Informatics in July 2026. The search covered PubMed, CINAHL, Google Scholar, and grey literature through April 2025. Twenty-eight publications met inclusion criteria.

The findings are pointed. Of the 20 tools designed specifically for the healthcare workforce, 45% targeted nurses. Psychometric properties were reported for 71% of included instruments, but evidence quality varied considerably across them. Most strikingly, only two tools met the study's criteria for being both valid and scoped to the full interprofessional healthcare workforce. For a health system with dozens of clinical roles interacting with a shared digital infrastructure, that is a narrow foundation.

The review also noted that 61% of the 28 included publications appeared in the last five years, signaling accelerating research interest. But volume has not yet translated into breadth: the field is still disproportionately weighted toward a single professional group.

Europe's public health workforce faces a parallel gap

The second study, published in npj Digital Public Health, takes a wider lens. Researchers conducted a scoping review of competency frameworks for digital public health across Europe, following PRISMA-ScR methodology and searching Medline, Embase, Web of Science, and ERIC. From 994 deduplicated records, 13 studies were included, contributing a combined 222 competencies.

After assessment and refinement, the researchers organized those competencies into a framework of 19, grouped into three domains: Health Data, Digital Public Health Services and Functions, and Analytics and Artificial Intelligence. The AI and analytics domain is notable. As health systems begin embedding predictive tools and population health platforms into operations, the workforce skills required have grown beyond basic digital literacy into territory that most current training programs do not address.

The Nature study also flags a foundational problem that complicates everything downstream: there is no consensus definition of digital public health. The term is used to describe anything from converting paper records to digital formats, to transforming entire operational models and organizational culture. Without an agreed definition, curricula and competency standards diverge across institutions and national contexts, making cross-system benchmarking nearly impossible.

The operational gap behind both findings

What connects both studies is a shared diagnostic: health systems are being asked to digitally transform at speed, but the mechanisms for understanding and verifying workforce readiness are underdeveloped. Digital tool procurement decisions are frequently made at the system or enterprise level, with implementation plans that assume a level of staff capability that has not been independently measured.

The International Journal of Medical Informatics review explicitly frames the problem as one that affects researchers, educators, and health services alike, noting that understanding which tools are validated and fit-for-purpose is essential for organizations seeking to improve digital health competence. The Nature study adds that limited evidence, particularly from European contexts, means that curriculum integration and training investment decisions are often made without a strong empirical foundation.

For workforce development and operations leaders, the practical implication is straightforward. An EHR rollout, a telehealth expansion, or an AI-assisted clinical decision support deployment each assumes specific staff competencies. If the measurement tools used to assess those competencies are validated only for nurses, or not validated at all, the organization is flying partially blind into implementation.

What this means for your team

• Audit current assessment tools: verify whether the digital competency instruments your organization uses carry published psychometric validation and whether they are scoped to all relevant clinical roles, not just nursing.
• Evaluate interprofessional frameworks: with only two validated interprofessional tools identified in the literature as of mid-2026, procurement and workforce leaders should scrutinize vendor-supplied competency assessments carefully before treating them as benchmarks.
• Map against the 19-competency DiPH framework: for public health and population health teams, the Nature-published framework offers a structured starting point for identifying where AI and analytics skill gaps may exist before deploying data-intensive platforms.
• Tie competency measurement to digital rollout timelines: build workforce readiness assessment into procurement and implementation cycles, not as an afterthought post-go-live.