In value-based care, nothing happens without access to robust data. It powers everything from advanced analytics to predictive AI models. It provides a holistic picture of population health and individual patient needs, informing appropriate care interventions that improve outcomes and reduce costs. As AI tools become more prevalent in healthcare generally, and in value-based care specifically, it’s critical to get the data foundation right.

But healthcare data is, and has always been, extremely challenging. Organizations need enterprise data management capabilities that can securely bring extensive datasets together seamlessly and move beyond retrospective auditing of what happened in the past to proactively managing patient care today, and anticipating patients’ needs in the future to personalize care.

Why Data is Foundational To VBC

In VBC, performance is measured across three dimensions: quality, cost, and patient experience. To succeed, organizations need to monitor progress in all three – identifying care gaps, tracking benchmarks, and aligning incentives. That’s impossible without accurate, complete, and timely data to:

• Monitor and manage patient populations effectively
• Attribute patients correctly for care and reimbursement
• Reconcile payments under complex contract structures or payment models
• Pinpoint opportunities for improving care delivery and outcomes

Having all of this in a single source of truth – normalized, enriched, and available at the right time to the right team members – is a challenge for even the most seasoned VBC participants. Most organizations still struggle with fragmentation, interoperability, data lags, privacy concerns, and limited resources – and it is slowing down our collective progress in value-based care.

Data + AI

Artificial intelligence (AI) and advanced analytics hold enormous promise in VBC. With these capabilities, healthcare organizations can predict the health trajectory of populations, predict performance in VBC contracts with downside risk, and recommend interventions tailored to individual needs – and all of this can be done faster and more accurately than was ever possible before. But AI is only as good as its data foundation, which comes from multiple sources:

• Claims data that provides advanced analytics insights and capabilities, but comes with a significant lag time (90+ days in some cases) and lacks current clinical context
• Clinical data from EHRs, labs, imaging, and other sources, which is critical for making in-the-moment care decisions
• SDoH data that informs care teams about patients’ potential access barriers or risk factors outside of what is contained in a medical record or claims data
• Pharmacy data on current and former medications, including adherence data
• Patient-generated data from tools like wearables and home health care and can supplement data from the EHR on patients’ current health
• Cost and utilization data for better benchmarking and contract reconciliation in complex value-based or risk-based alternative payment models

Accurate AI models require extensive data libraries, training algorithms to improve care while avoiding pitfalls, such as:

• Incomplete data leads to biased or inaccurate models – and the stakes are too high in healthcare to get this aspect of data management and AI development wrong.
• Delayed data results in missed intervention opportunities for providers and payers, and potentially worse care and higher costs when teams don’t have the information they need to understand what’s happening with patients and members today.
• Lack of governance and guardrails dramatically increases risk, and can lead to serious harm, inappropriate care, or even death in the most severe cases.

The key to successfully using AI to advance VBC is finding healthcare tech companies that balance speedy development and deployment with transparency and appropriate safeguards to protect the integrity of the insights, predictions, and care recommendations coming from AI tools.

Building a Data-Driven VBC Infrastructure

Strong data foundations don’t happen by accident. They require strategic planning and investment in tools that can:

• Aggregate data from across the healthcare ecosystem
• Normalize and enrich data so it’s clean, consistent, and usable across your entire tech platform
• Act as a single source of truth with the ability to seamlessly share data across multiple platforms, applications, and systems
• Safeguard data and partition access to protect against breaches and ensure that everyone is working with the data they need to ensure optimal patient outcomes

With these tools in place, organizations can then build a culture of data literacy and data-driven decision making that builds trust among collaborative partners to improve care, reduce costs, and create positive patient experiences.

Our Collective Healthcare Future Depends on Data

Data isn’t just part of value-based care – it’s the crux of VBC success. Without robust, timely, and actionable information, even the most well-intentioned initiatives will fall short. To harness AI, improve patient care, and succeed in risk-based contracts, organizations must first get their data in order. When the right data is in place, the possibilities for transforming U.S. healthcare from a broken fee-for-service system expand exponentially.