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Black Book Polls Show Health Plans Shift from Digital Promises to Proof-Based IT Execution

Black Book Polls Find Health Plans Moving From Digital Transformation Promises to Proof ...

June 2, 2026Black Book Research

Summary

According to Black Book Research, U.S. health plans are moving beyond mere digital transformation promises and prioritizing measurable outcomes and operational value in their IT investments. Specific areas like authorization processes, data quality, and AI governance require concrete proof of improvement.

Details

Black Book Research's 2026 State of Payer Digital Technology report summarizes findings from a survey of 8,194 U.S. health plan and managed care organization IT users. The report reveals a significant shift in the payer IT buying cycle: moving away from broad 'digital transformation' messaging toward a more disciplined focus on 'measurable execution.' The key areas of focus are whether platforms and services can prove measurable improvements in specific metrics such as authorization speed, data quality, claims accuracy, member service, provider friction, compliance evidence, AI governance, and cost-to-serve. Among the findings, 82% cited interoperability, FHIR/API readiness, and usable data exchange as essential. However, buyers are increasingly focused not just on whether data is exchanged, but whether it is 'reconciled, governed, and usable within real payer workflows.' Furthermore, for AI-enabled workflows, there is a strong demand for 'human review, explainability, monitoring, and audit trails' before broader deployment in regulated payer operations. Overall, the report suggests that health plans are moving away from siloed quality and performance systems toward an integrated operating model centered on shared data assets. The most critical trend identified is the rejection of modernization efforts that merely digitize a broken process; instead, there is a demand for platforms that fundamentally change accountability, improve queue design, strengthen policy governance, and correct data-quality defects.

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