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What You Should Know
- A national research study conducted by Wakefield Research for Cedar Gate Technologies (an IQVIA business) surveyed 100 senior U.S. health plan executives (VP level and above) across actuarial science, clinical operations, VBC strategy, and IT.
- The Adoption-Performance Paradox: 83% of payer executives report they are not achieving the cost and quality outcomes they expected from value-based care (VBC), yet 72% intend to continue or expand their alternative payment programs.
- Severe Infrastructure Deficit: Only 24% of health plans report they are fully equipped from an operational and technological standpoint to support their planned VBC expansions.
- The Value-Based AI Bottleneck: An overwhelming 92% of executives identify the absence of a single, connected data source as a major barrier to scaling artificial intelligence across value-based arrangements.
- Forecasting and Data Fragmentation: Only 30% of health plans can accurately forecast financial risk and performance under alternative payment models (APMs); 47% struggle with poor data quality, fragmentation, or cross-system inconsistency, and 85% report that interoperability failures directly degrade VBC success.
Core Infrastructure Deficits Undermining Performance
The research—which surveyed 100 VP-level and C-suite health plan executives across actuarial science, clinical operations, provider partnerships, and healthcare analytics—reveals that data silos and legacy technology architectures remain the primary drivers of underperformance:
- Interoperability & Data Fragmentation: 85% of respondents state that interoperability roadblocks negatively impact their VBC success, with 47% struggling directly with poor data quality, siloed feeds, or cross-system data discrepancies.
- Actuarial and Risk Forecasting Gaps: Only 30% of surveyed executives report having the analytical capability to accurately forecast financial risk and margin performance within alternative payment models.
- The AI Scaling Hurdle: As health plans look to generative and predictive artificial intelligence to automate risk stratification and care gap closure, 92% cite the lack of a unified, connected data source as a major barrier to scaling AI within value-based contracts.
Where Health Plans Are Directing Capital Next
To bridge the gap between contractual risk and operational execution, health plans are prioritizing enterprise IT budgets toward core data ingestion, contract modeling, and near-real-time analytics engines:
- Advanced & Predictive Analytics (51%): Generating actionable insights across clinical, financial, and operational performance to proactively address care and cost variances.
- Near-Real-Time Data Infrastructure (47%): Moving beyond 60- to 90-day claims latency to enable agile, proactive management of quality, utilization, and financial risk.
- Scalable VBC Platforms (45%): Supporting multiple payment mechanisms—including bundled payments, shared savings, and capitation models—from a single technology foundation.
- Advanced Risk & Contract Modeling (45%): Improving actuarial forecasting accuracy, stress-testing alternative payment models, and projecting financial performance.
- Unified Data Platforms (45%): Consolidating disparate data feeds into unified platforms and data lakes to reduce fragmentation, improve interoperability, and power enterprise AI.
Strategic Rationale: Addressing the Execution-Adoption Disconnect
The survey underscores that health plan dissatisfaction stems less from the design of alternative payment models and more from the administrative friction required to manage them. Traditional payer systems—built around transactional, fee-for-service retrospective adjudication—cannot effectively surface point-of-care attribution, monitor mid-year benchmark drift, or coordinate multi-specialty care pathways.
As a result, payers are seeking composable platforms that pair upstream real-time data streaming with automated risk adjudication to ensure downstream shared savings actually materialize.
