Confidence Pays: How Accurate Claims Data Drives Value-Based Care Success
At the recent HLTH 2025 conference, artificial intelligence in healthcare was the topic of the week. Healthcare data, the very foundation of what drives value-based care analytics, was the star of the show in one session.
“Go back to your organization,” one panelist said. “Choose to take the time and effort to have quality data, even if it’s not clearly an ROI.”
Value-based care lives or dies by data.
Quality scores, cost benchmarks, and shared savings all depend on the same thing: accurate, trusted data.
When claims data is wrong, your results may not measure up.
The Cost of Inaccurate Healthcare Claims Data
Incomplete or inconsistent claims data can lead to:
- Missed risk-adjustment opportunities.
- Incorrect quality reporting.
- Underpayments.
- Contract disputes and delayed reconciliation.
AI can’t fix these problems — it amplifies them. That’s why health systems are prioritizing data integrity as an investment. Data as a strategy will maximize investments made in analytics and AI tools layered on top of the data.
Why It Matters Now
Regulatory changes and payer policies increasingly demand proof — not estimates — of data quality.
The move to using AI in healthcare requires data quality and accuracy to drive better decisions for value-based care and population health in particular.
HDI helps you meet those standards today while future-proofing for AI, automation, and analytics.
Leadership Takeaway
Data confidence is financial confidence.
When your claims are accurate, complete, and current, your organization can measure risk, efficiently close gaps, and optimize contracts with certainty.
In value-based care, confidence pays.
Read more about the data “Confidence Gap” in the HDI 2025 Market Report:
