Program Integrity & Compliance

The Case for Program Integrity in Medicaid Managed Care | Part Six Encounter Data Recommendations

The Impact of Incorrect or Incomplete Data: In our previous post on encounter data, we discussed how faulty encounter data can affect rate setting and create ongoing program risk. In this post, we offer recommendations for managing potential problems, along with an industry Case Study that underscores these encounter data issues.

October 17, 2025

6 minutes

Key Takeaways

Faulty encounter data in Medicaid Managed Care can undermine program integrity by inaccurately influencing capitation rates, payments, and oversight efforts, which may inadvertently create risk and reduce effectiveness in addressing fraud, waste, and abuse (FWA). This blog post provides recommendations for leveraging encounter data as a unified source of truth to enhance compliance, improve data reliability through validation, and integrate analytics for oversight improvements and increased accountability.

Accurate, validated encounter data ensures efficient program monitoring, compliance, and fraud prevention efforts
Data analytics can uncover inaccuracies in Medicaid Managed Care encounter data, promote transparency, and improve fraud detection.
Address EQRO limitations by aligning their scope with state quality strategies and emphasizing encounter data validation for stronger Medicaid program oversight.

Problematic Encounter Data

For those charged with PI oversight of managed care programs, faulty encounter data is problematic. If inaccurate data drives capitation rates and payments higher, managed care organizations (MCOs) may not be incentivized to pursue program integrity (PI) to discover fraud, waste, and abuse (FWA).

Recommendations

To address those issues and mitigate damages, we recommend leveraging encounter data for all ongoing administration, monitoring, and compliance activities. If encounter data is submitted to set rates, calculate the MLR, perform PI, and calculate quality measures, then there exists one source of truth. Creating and reinforcing that source of truth requires MCOs to correctly submit encounter data. Accurate management of a program requires high-quality encounter data, and a single source of truth enables more efficient allocation of the resources necessary to monitor and maintain that quality and accuracy.

To effectively utilize encounter data, the data must be both complete and accurate. If MCOs are blocked from submitting their encounters by a Medicaid Management Information System or data warehouse, the MCOs have an excuse to say rates, ratios, or measures are missing information and are therefore inaccurate. We recommend the following to enhance oversight and improve accountability:

Address Problematic Data with Analytics. Issues with encounter data often include missing fields, inconsistent formats, technology incompatibility, and more. One OIG report said that states may not be currently collecting the amounts of data necessary to monitor actions such as prior authorization (PA) denials, which can limit auditors’ ability to determine if claims were denied appropriately, if a PA was required to provide that benefit, or if the units authorized had been exhausted.

Data analytics can identify these problems, provide detailed expectations of rates and payments, and expose the encounters or groups of encounters with variances. Analytics can also flag cases that result in recoveries, which serve as benchmarks against other potentially superficial MCO investigations.

We have observed scenarios in which MCOs initiate cases that have limited scope and impact, creating the illusion of oversight and compliance. Rather than concentrating on developing oversight activities designed to maximize provider compliance and reduce FWA, unfocused PI activities diminish the effectiveness of those efforts and devalue overall Medicaid PI. Focusing on investigations that drive true provider compliance increases the efficiency of those efforts. Data analytics can provide the necessary detail and transparency that brings problems into bold relief where they are discoverable and can be addressed.

Understand the Importance of Validating Encounter Data Before Use. Encounter data MUST be validated before use. Validation can correct deficiencies and raise the quality and reliability of the data, which makes it trustworthy for everyone. Inaccurate or error-prone data will negatively affect the conclusions drawn from the encounter data.
Be Aware of External Quality Review (EQR) Scope Limitations. External Quality Review Organizations (EQROs) provide important services to assist states with MCO oversight. An article from Medicaid.gov states that EQRs and the EQROs providing the reviews help improve state oversight of MCOs and their plans’ contracted services and help plans improve their performance. However, the same article states that encounter data validation is one of seven optional activities EQROs can, but are not required, to perform.

While EQROs might review a few dimensions necessary for compliance with the MCOs’ contractually mandated policies and procedures, these reviews do not typically and broadly assess data effectiveness, nor do they typically provide root-cause analyses of issues contributing to MCO non-compliance.

A report by Medicaid and CHIP Payment and Access Commission (MACPAC) found EQRO reporting has limitations, beginning with the fact that EQROs focus on processes, not results. Be mindful to align EQRO and state quality strategies; be aware of variations across states on enforcement; and understand that the EQR report focus and intent may not be the focus of PI.

Ultimately, the primary issue is that states think someone, including the EQRO, is reviewing data at the necessary level of detail for accuracy, consistency, and completeness…and that may not be happening

A Case Study – PI Encounter Data

A report from the Office of the Washington State Auditor considered the audit findings of three of the state’s MCOs and their contractual obligations to provide PI to prevent FWA. While the report states that the MCOs met numerous contractual requirements and used basic data analytics, the Auditor recommended additional analytics to strength PI oversight and outcomes. The auditor stated the agency could further improve its results by including MCO-specific performance metrics in the contracts, thereby bolstering the incentive for MCOs to achieve compliance.

In our next installment, we present a Case Study from one of Myers and Stauffer’s own engagements, in which we helped our client transition from the fee-for-service model to the managed care model, along with the challenges and solutions.

Our Benefit/PI program area covers a range of services, disciplines, and areas of focus, including data analytics, root-cause analysis, and encounter data validation. We are here to answer any questions and help with any health and human services needs your agency may be experiencing. Contact a member of our team today.