By Abhishek Gupta, SVP, Healthcare & Life Sciences, Mastek
LinkedIn: Abhishek Gupta
LinkedIn: Mastek
It is said that value-based care (VBC) is meaningless without access to robust data. Data has been crowned by the foundation and is the backbone of VBC. While the premise is undeniable, I believe that this is only part of the whole truth. No doubt, data powers everything, from the big picture to nuanced insights, from real-time dashboards to predictive AI models, and from holistic population health to tailored needs of the individual. And as AI, in its generative and agentic forms, moves into production in value-based care, the responsibility of data could not be more critical.
Yet, despite the strong investments in data and analytics, VBC’s promise remains largely unfulfilled. Most organizations have invested in analytics dashboards that surface insights but don’t drive action. A closer look reveals why. The gap between promise and performance arises not from lack of data but from lack of connected data, which is really the operational backbone that integrates clinical, claims, utilization, finance, and patient engagement data into a single, actionable infrastructure.
The truth is crystal clear. The success of value-based care VBC success depends less on the sophistication of analytics and more on the integrity of the data ecosystem powering it.
Five data streams that must connect and work together for VBC
For true value in healthcare, quality of care, patient experience, and affordability must converge. This demands total accuracy, completeness, and timeliness of data, at the very least. A single source of truth, one may call it, from five streams of data and information:
- Clinical data, from EHR records, care plans, diagnoses, lab results, physician notes, etc. This plays a critical role in identifying high-risk patients and gaps in care to make in-the-moment decisions
- Claims data, comprising encounter history, procedure codes, prior authorizations, and adjudication records all play a major role in financial reconciliation and population health stratification
- Utilization data, which includes inpatient admission records, ER visits, specialist referrals, readmission patterns, etc., to identify avoidable cost drivers and inefficiencies in care pathway
- Finance data, which reveals cost-per-episode, shared savings calculations, risk corridor exposure, contract performance. These insights align clinical decisions with financial accountability
- Patient engagement data, such as adherence to appointments, patient-reported outcomes, remote monitoring signals, portal activity, etc., which helps in closing care gaps and reducing avoidable deterioration
Let’s say we have all of this as a single source of truth: data that is normalized, enriched, and accessible. Yet why does the gap in value-based care still persist?
Overcoming the ‘dashboard problem’ of insights without interoperability
Most healthcare organizations still struggle with challenges of data fragmentation, interoperability, lag, privacy, and architecture. They face problems with data silos between EHRs, payer claims systems, utilization management tools, and billing platforms; of lag time between clinical events and financial recognition that undermines risk adjustment and care management.
This is what we term the ‘dashboard problem’, a classic case of analytics tools that are built on sifting fragmented data, producing fragmented insights and decisions. The data foundation is reasonably laid, but the operational backbone is missing.
What does this operational backbone look like? It is underpinned by the following components and properties:
- A unified data layer (not just a BI tool) that normalizes and connects all five streams in near real-time
- Compliant with interoperability standards (HL7 FHIR, X12)
- Master patient index, longitudinal patient records, and bi-directional data flows between payers and providers
- Governance layer marked by data stewardship, consent management and audit trails
- Seamless integration between insights from clinical and administrative workflows. For example, a care coordinator must be able to see the patient’s claims history, recent lab values, and missed appointments in one view so they can act without switching systems
This is the data-driven and data-interoperable architecture needed for VBC to manage diverse patient populations, attribute them correctly for care and reimbursement, and identify opportunities to continuously enhance care delivery. Such an operational backbone minimizes the administrative burden of reconciliation and dispute resolution between payers and providers.
It also supports risk-based models at scale, ensuring real-time attribution, cost tracking, and quality. Additionally, it models financial exposure against utilization patterns and clinical risk scores and connects surgeons, hospitals, and post-acute data across the care continuum.
Implementation considerations for providers and payers
Building a strong and robust operational backbone with connected and interoperable data requires strategic planning and investment and meticulous execution discipline.
Start with a data maturity assessment before selecting the technology platform. This involves integration of data from EHRs, claims data, lab systems, and patient surveys for a unified view. It also calls for risk stratification by segmenting patients according to the likelihood of adverse health events and cost. Next, prioritize use of cases with the clearest financial and clinical return (such as chronic disease management and transitions of care).
Now, address the people and process the layer. Data integration fails when workflows don’t change. In selecting vendors, look for features such as API flexibility, pre-built payer-provider connectors, scalability across risk contract types, and alignment to regulatory mandates and standards such as HIPAA, CMS Interoperability Rule, TEFCA, etc.
Connecting the data to what matters is critical. Quality metrics such as Healthcare Effectiveness Data and Information Set (HEDIS), Consumer Assessment of Healthcare Providers and Systems (CAHPS), and CMS Star ratings require clean, complete clinical and claims data. Social Social Determinants of Health (SDoH) are gaining importance as a data stream for predictive risk stratification in addressing non-clinical barriers, and this must be considered during implementation. Patient engagement signals must be tied to outcome data in measuring the ROI of care management programs
Corroborating all the above points is the fact that organizations with integrated data platforms have demonstrated reductions in readmissions, avoidable ED visits, and total cost of care.
In the next few years, value-based care analytics will see rapidly accelerating changes. AI will play a pivotal role in value-based care analytics, and we will see ML models directly embedded into EHRs to provide recommendations at the point of care. A data-powered operational backbone is a non-negotiable imperative, and providers and payers that invest in connecting their data infrastructure will emerge as winners. The call is distinctly clear: evaluate where your data integration gaps are to win the competitive edge in value-based care.