JMIR Publications

ABSTRACT

Healthcare organizations face a "Triple Threat" of low analytics maturity, high workforce instability, and semantic technical barriers that together produce a crisis of "Institutional Amnesia." High leadership turnover, persistent digital health workforce shortages, and widespread intent to leave among informatics specialists systematically erase the tacit knowledge required to navigate complex clinical data schemas, trapping organizations in a cycle of low maturity where the rate of knowledge loss exceeds the rate of knowledge capture. Viewed through Nonaka's SECI model of knowledge creation, the root cause is a "Socialization Failure": high turnover fractures the social networks required for mentorship, rendering the traditional apprenticeship model of informatics unsustainable. To address this failure, we employ a Design Science Research (DSR) approach, synthesizing evidence from healthcare informatics, knowledge management, and natural language processing (2024-2026 workforce and NL2SQL literature) to develop a socio-technical framework called Human-in-the-Loop Knowledge Governance (HITL-KG). HITL-KG is designed to shift the locus of organizational knowledge from volatile human memory to durable semantic artifacts called "Validated Query Triples," each comprising a natural language intent, executable SQL, and rationale metadata. By embedding knowledge capture into the daily workflow of query generation, the framework aims to convert the ephemeral act of analytics into permanent institutional assets. The accompanying Three-Pillar Assessment Rubric provides a structured self-assessment tool that enables organizations to identify compounding vulnerabilities across analytics maturity, workforce agility, and technical enablement. A critical objection, the "Validator Paradox" (who validates the AI when experts leave?), is resolved by reframing validation through Lean "Standard Work": each validated query establishes the current known standard rather than eternal truth, functioning as a "knowledge ratchet" that prevents regression. By decoupling analytical capability from individual tenure, healthcare systems can ensure that analytics maturity advances even as the workforce evolves. This paper proposes and theoretically motivates the framework; empirical validation is deferred to a companion study.