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White paper

Financial monitoring and revenue recognition in value-based care

1 August 2025

This is part two of a four-part white paper series examining the life cycle of value-based care models from the provider's viewpoint, focusing on essential factors for provider organizations involved in these arrangements. All articles published in the series can be found here.

Flowchart beginning at Evaluating, followed by Accounting & Monitoring, Reconciliation & Audit, and Opportunity Identification.

Introduction to accounting and monitoring

When engaging in a value-based care (VBC) arrangement, it is important to understand performance on an ongoing basis. Through accounting and monitoring, the provider organization can both anticipate and budget for the financial impact of these arrangements as well as course correct if performance is lagging in key areas.

This step includes obtaining reports from the payer throughout the performance period, using claims and billing data to estimate outcomes, and monitoring specific components that impact the ultimate financial settlement such as risk adjustment or quality metrics. When using these approaches, it is important to keep in mind potential limitations such as claim seasonality, suppressed claims due to sensitive conditions such as mental health and substance abuse, and access to data from other providers, among other considerations.

Provider groups should ensure they are focusing on monitoring elements that tie directly to the settlement calculation. Avoiding information overload by reducing unnecessary metrics in the monitoring process increases the ability to act on the elements that have a material impact.

This white paper covers claims lag, uses of electronic medical record (EMR) data and predictive modeling, strategies for tracking claims at nonaffiliated facilities, and best practices for revenue recognition.

Impact of claims lag on performance monitoring in VBC

In the context of healthcare, a claims lag denotes the period between the delivery of a healthcare service and the subsequent payment of the claim by the payer. This delay poses considerable difficulties for providers attempting to assess their financial performance in real time within VBC frameworks. Depending on the complexity and setting of the claim, finalizing a patient’s claim history can take three months or longer, with payers then taking additional time to share the completed claims data feeds. Given that many VBC agreements operate on performance periods ranging from six to 12 months, this extended wait for dependable claims data can substantially impede a provider’s ability to promptly respond to current developments in their VBC population.

Providers facing delays when monitoring and projecting their performance due to claims lag can adopt strategies to mitigate their impact. First, traditional actuarial completion factors can be employed to estimate outstanding claims based on historical financial data. The completion method leverages previous claim payment patterns to project the percentage of claims that are likely to remain unsettled at any given time. While this is typically a payer concern, effective estimation of outstanding claims by providers allows for a real-time estimation of performance within a VBC population. However, one limitation of this technique is its design for population-level application, which may become ineffective when analyzing subsets of patients or service types in detail.

Strategies for using EMR data and predictive modeling

Providers can also utilize their EMR systems and billing data to assess financial performance instead of relying on adjudicated claims data. EMR-based monitoring serves as a valuable resource, enabling providers to track cost trends and patient profiles in real time within their VBC population, without the delay of waiting for payer-adjudicated claims. Although this method doesn't provide insights into services conducted at external facilities, it aids in a timelier understanding of financial performance.

While completion factors can be an effective way to predict outstanding claims at the population level, predictive modeling is typically more effective for identifying patient-level cost drivers. Patient-level total-cost predictions can be used to estimate expenditures for an individual based on observed claims and other available data, such as EMR data. Understanding predicted total costs at the patient level enables VBC providers to allocate care management resources more effectively to the highest-risk patients. More advanced models can drill into specific types of care, such as disease-specific predictions, high-risk emergency room and inpatient utilizers, and potentially avoidable costs predictions to enable targeted care management in the areas it will make the most patient experience and financial impact.

Challenges tracking costs at nonaffiliated facilities

In addition to claims lag, providers face significant challenges monitoring performance during the VBC agreement performance period due to services rendered at nonaffiliated facilities. Internal EMR and billing data does not include records from nonaffiliated facilities, and methods like completion modeling or predictive analytics only provide estimates of these costs.

There are two main approaches to understanding external healthcare service utilization:

  • Payer claims data
  • Health information exchanges

Obtaining comprehensive claims for the attributed population from the payer

Payer claims data provides the most thorough overview of claims activity for a VBC population, as payers track all paid claims that directly impact the final financial settlement for any VBC arrangement. However, relying solely on payer claims data has its limitations. First, data is affected by the previously mentioned claims lag delays, making it suitable for assessing retrospective financial performance evaluation but potentially too delayed to inform real-time patient interventions during the performance period. Second, some payers are reluctant to share claims data from external facilities due to confidentiality regarding contracted rates. Providers may address this concern by agreeing to receive data with the payment amounts obscured, although this can reduce the data’s usefulness. Alternatively, the payer and provider can agree on a trusted third party to access, analyze, and aggregate the complete claims data in a manner that is both beneficial to the provider and protective of the payer’s confidentiality.

Utilizing health information exchanges for real-time insights

Providers can also leverage health information exchanges (HIEs) to gain better visibility into patient care delivered at external healthcare facilities. These data-sharing networks offer near-real-time access to medical records, diagnostic results, admission, and lab data from participating institutions. This information helps providers enhance predictive modeling accuracy and ensure timely interventions for high-risk patients based on recent hospitalizations or newly identified conditions outside their facility. While using HIE data integrations can be complex—these records are not typically formatted like traditional claims data and lack payer adjudication details—the timely nature provides a critical advantage in tracking patient care pathways throughout the VBC performance period. Key HIE data flags such as hospital admissions, emergency visits, and newly diagnosed conditions can help VBC providers identify high-priority follow-up needs, allowing for proactive patient engagement and improved financial and clinical outcomes.

Best practices for revenue recognition in VBC models

Recognizing revenue under VBC agreements from an accounting perspective can be challenging. This is particularly true for VBC models that distribute lump-sum shared savings payments months after the performance period ends.

Providers new to VBC financial models may initially adopt a cash accounting approach, recognizing shared savings settlements as they occur. However, as VBC organizations gain experience and diversify their portfolios, they may transition to accrual accounting, linking shared savings settlements back to the performance period in which they were earned. This transition can be complex, and it is strongly recommended that providers seek guidance from a licensed accountant, auditor, and credentialed actuary to determine the appropriate method for accruing VBC revenue.

Providers who use accrual-based financial monitoring typically create periodic projections of the final settlement amount and then proportionally accrue toward that estimate throughout the performance period.

For instance, if a provider predicts their annual shared savings settlement at the end of each month—estimating $1 million in January, $900,000 in February, and $900,000 again in March—they would follow this accrual schedule: At the end of January, they will recognize one-twelfth of the January estimate as their VBC settlement revenue ($1 million/12 = $83,333). By the end of February, they would recognize two-twelfths of the February estimate minus January's accrual ($900,000 x 2/12 - $83,333 = $66,667). At the end of March, they would recognize three-twelfths of the March estimate minus the amounts accrued through February ($900,000 x 3/12 - $83,333 - $66,667 = $75,000). Through the first three months of the year, the provider in this example would have accrued a total of $83,333 + $66,667 + $75,000 = $225,000.

Annual estimated settlement revenue of 1 million, across January February and March.

Projecting the final shared savings in a VBC arrangement during or immediately after the performance period can be challenging. The difficulty level is often influenced by the intricacy of the VBC agreement, delays in obtaining claims data necessary for creating an estimate, and the overall quality of the data provided. Collaborating with actuaries to develop a structured revenue recognition methodology helps align accounting and performance monitoring requirements, the availability and timing of data, and organizational risk tolerance for budgeting.

To mitigate financial uncertainty, many providers incorporate a conservative margin into their estimated shared savings accruals, reducing the risk of overstating expected settlements. This approach improves the chances that the actual settlement amount will meet or exceed recorded projections, strengthening financial stability within the VBC arrangement.

Conclusion: Enhancing financial performance in VBC

Financial monitoring throughout the performance period is crucial for VBC providers aiming to assess and optimize their VBC population's financial performance. Although accessing fully adjudicated claims data may not always be available in real time, healthcare organizations can leverage alternative data sources and analytical models to gauge their performance. Ultimately, those organizations that effectively utilize these financial monitoring strategies will be strategically positioned for long-term success within their VBC models.


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