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Encounter Data Improvement

What will IHA do as the Encounter Data Governance Entity (EDGE)?

As the Encounter Data Governance Entity (EDGE), IHA is coordinating a statewide effort to improve encounter data. Through a multi-pronged approach, including technical assistance, performance measurement, industry engagement, and communication efforts, we’re aligning the healthcare community around a shared goal: to ensure that all future reporting dependent on encounter data reflects actual provider and health plan performance and an unbiased understanding of population health needs. 

Why and how was EDGE created?

As a contingency of Centene’s acquisition of Health Net, the California Department of Managed Health Care (DMHC) required Health Net to invest $50 million in improving encounter data submissions in California, with a focus on Managed Medi-Cal providers. Health Net launched its Encounter Data Improvement Program (EDIP) in 2015 through a series of grants. In 2019, Health Net oversaw an industry listening process led by Manatt Health. This listening process highlighted the need for a Governance Entity to steward cross-industry alignment. Through a competitive RFP process, Health Net selected IHA as the Governance Entity in March 2021 with the charter of overseeing a multi-year, cross-industry effort to improve the completeness and reliability of encounter data in California.

How is EDGE funded?

IHA’s role as the Encounter Data Governance Entity (EDGE) is funded by Health Net, Inc as part of the California Department of Managed Health Care’s undertakings for Centene Corporation’s acquisition of Health Net. IHA’s work as the Encounter Data Governance Entity is governed through a milestones-based contract with Health Net, Inc.

What problem is EDGE seeking to solve?

Encounter data across California’s healthcare delivery system is fragmented and inconsistent due to the complexity, administrative burden, and a lack of standardization in how the data is submitted and processed. As a result, data gaps, rejections, and duplications threaten the reliability of the many reports and processes that are dependent on encounter data.

While this problem is particularly acute in California due to the prevalence of managed care and capitation payment arrangements in the state, encounter data management is a challenge for the healthcare system nationwide, especially as more stakeholders adopt or expand population-based alternative payment models.

Who is IHA working with for Encounter Data Improvement?

To pull off a statewide encounter data improvement effort, we’re working with leading organizations in provider technical assistance, industry collaboration, and those who manage encounter data for their programs. This includes California Medical Association, California Primary Care Association, the Department of Health Care Services, the Department of Managed Health Care, and Health Industry Collaboration Effort.

Learn more about the organizations taking part in this effort.

Is this work only focused on Medi-Cal?

Our work as EDGE has a strong focus on Medi-Cal. However, we seek to implement industry-wide advancements across all product lines and geographies. This is because the challenges associated with poor quality and missing encounter data are not limited to Medi-Cal, and many of the stakeholders that submit and process this data operate across multiple lines of business.

About Encounter Data

What is encounter data?

According to the Centers for Medicare and Medicaid Services, encounter data is “detailed data about individual services provided by a capitated managed care entity. The level of detail about each service reported is similar to that of a standard claim form.” A “capitated managed care entity” refers to providers and health plans who receive a per-member-per-month capitated rate to care for an assigned patient population.

What is encounter data used for?

Many important healthcare processes rely on encounter data, including:

  • Risk-adjustment: Encounter data include diagnoses and other information necessary to understand the risk-level of a given population. This information is then used to determine the appropriate payment levels for both managed care plans and their contracted providers within a capitated healthcare setting.
  • Clinical quality measurement and incentives: In capitated healthcare settings, encounter data serves as the basis for understanding clinical quality performance across prevention, screening, and disease management measures.
  • Consumer cost-sharing: In capitated healthcare settings, encounter data provides information on services provided, which is then used to determine patient cost-sharing and to track accumulation of deductibles and out-of-pocket maximums.
  • Transparency: Encounter data offer information to identify the complete picture of services provided to patients. Whether it’s a self-insured employer tracking utilization and costs for rate-setting, benefit design, and provider selection or a regulator tracking population health outcomes against statewide quality and access goals, transparency into how care is delivered within a capitated healthcare setting depends on encounter data.

Challenges in encounter data quality

What data standards are missing for encounter data?

Data standards determine and align standard sets of codes, processes, or guidelines to help ensure data consistency and usability. ​ While instructions and formats exist for encounter data — such as various companion and policy guides from the Department of Health Care Services (DHCS), there is a need for additional defined processes, workflows, and consensus-based recommendations to improve how encounter data is collected, reported, aggregated, and analyzed.

For example, a multistakeholder workgroup can establish a standard guideline or crosswalk for translating local codes prior to submission. Such a solution would ensure that encounter data submissions aren’t inappropriately rejected or reporting doesn’t lose accuracy when aggregated across different organizations or regions. Another example of a data standard is defining modifiers and logic design for a specific coding scenario that feeds into encounter data reporting. Developing and disseminating this information can help more organizations access tools that have proven useful in California’s encounter data context.

How is IHA helping advance the implementation of data standards for encounter data?

As the Encounter Data Governance Entity, IHA is driving the adoption of standards throughout the data submission chain through the following mechanisms:

  • Under the auspices of our Data Governance Committee, IHA has partnered with the Health Industry Collaborative Effort (HICE) to convene its Encounters Standardization Team. This workgroup is charged with defining and prioritizing a set of guidelines and resources that can accelerate appreciable improvements in encounter data quality.
  • Through a memorandum of understanding with the Department of Health Care Services (DHCS), we are building alignment to ensure that solutions for more standardized encounter data can be put into practice across the industry.
  • Finally, we’re garnering industry stakeholders’ buy-in and commitment to make strategic technical and operational investments that can support the use of the guidelines. We will also facilitate access to the guidelines through our Resource Hub.

Encounter data quality challenges are extensive and require multi-faceted strategies to mitigate inaccurate and incomplete data submissions. A broad commitment among stakeholders, the value proposition for investing in in technical solutions and workflow redesign, and value-based incentive design are all critical to driving meaningful improvement and alignment. We’re pursuing an incremental approach that focuses on areas where there is early agreement, where the proposed remediations are relatively easy to implement, and where adoption can lead to measurable progress.

Do IHA’s performance measurement programs use encounter data measures?

Yes. Since 2007, our Align. Measure. Perform. (AMP) programs have collected and reported encounter data quality measures. Currently, we use the following measures:

  • Encounter Rate by Service Type (ENRST), which measures the number of encounters and claims per member-year, delineated by service type
  • Encounter Format (ENFMT), which assesses for correct coding and formatting of the content included in the encounter submission; and
  • Encounter Timeliness (ENLAG), which assesses the elapsed time in days between the date a patient receives care and the date when the claim/encounter is accepted by the health plan

What is IHA’s experience in encounter data performance measurement?

Since 2007, IHA has helped the providers and health plans understand encounter data performance through our Align. Measure. Perform. (AMP) programs. Here are a few significant milestones in IHA’s trajectory:

  • 2007: Collected and reported encounter data volume by service types (ENRST) across all programs (commercial HMO, commercial ACO, Medicare Advantage, Medi-Cal Managed Care).
  • 2015: With funding from the California Health Care Foundation, published an issue brief on encounter data issues within California’s capitated, delegated market.
  • 2016: Convened a multi-stakeholder work group to create a single industry interpretation of the most challenging and non-standard data elements in the 837 encounter forms.
  • 2018: Conducted an encounter data research study to understand market challenges across encounter data exchange in California.
  • 2019: With funding from Aetna, developed additional encounter data quality metrics including encounter data timeliness and completeness measures.
  • 2020-2022: Collected and reported encounter data quality suite of measures (volume, timeliness, completeness) for informational purposes.
  • 2023: For our Measurement Year 2022, we are collecting encounter data quality measures for reporting as “First-Year Measures.” This means we plan to use the results to establish a baseline with the intent of adopting these measures for programmatic purposes in future measurement years.