Six Challenges to Becoming a Data-Driven Payer Organization

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As healthcare payment models shift from fee-for-service to value-based, payers, like provider organizations, are motivated to develop population health management (PHM) strategies to lower the costs of healthcare. Managing a population is really about managing an individual member’s use of and outcomes from services along the continuum of healthcare. To manage a member’s healthcare journey, payers need data to appropriately assign the member to a risk category and accurately assess the outcomes of healthcare interventions. To support this complex and life-long process of managing members, payers must have the capabilities and technical infrastructure to support a data-driven strategy.

Multiple factors—including genetics, health behaviors (e.g., tobacco use and sexual activity), social and economic factors (e.g., employment, education, and income), and the physical environment (e.g., air and water quality)—impact a member’s health status, as well as how she uses health services and how much she engages in managing her own health. Members can receive healthcare services in acute or chronic settings across a broad range of providers and sites.

To weave together health status, engagement, and utilization into a cohesive picture that drives an effective health management strategy, payers must fully leverage data from disparate sources. This article explains the data challenges payers face in adopting and sustaining PHM strategies and how novel approaches to data management can help.

Six Payer Population Health Management Data Challenges

To effectively manage the complexities of a member population for the duration of their enrollment, payers must address six key data challenges:

  1. Data availability.
  2. Data access.
  3. Data aggregation.
  4. Data analysis.
  5. Data adoption.
  6. Data application.

#1: Data Availability

The amount of healthcare data available to support a PHM strategy continues to grow as health systems continue to ingrain data-centric EMRs and mobile devices into the workflow. The dispersal of relevant data among disparate sources, however, not the volume of data, can make data less available the payer users. This makes determining what data has a material impact on a PHM strategy, and making that data available, an important goal for payers.

Even though the acceleration of software solutions and devices fueling the expansion of healthcare data (e.g., EHRs and mobile platforms) appears to be pushing healthcare data into the realm of big data, the data critical data for managing a member’s health will likely be relatively small.

To succeed with a long-term PHM strategy, however, payers must be ready to manage increasingly available data. Later on, this report discusses the Health Catalyst® Data Operating System (DOS™), an open platform data solution designed to support healthcare’s increasing data needs.

#2: Data Access

Technical and legal barriers can make accessing healthcare data challenging:

  • The technical challenge: With improvements in data-sharing technology (e.g., as FHIR-based APIs), interoperability between disparate software and systems is improving. These custom solutions, however, are costly and use resources that could be applied to other PHM activities.
  • The legal challenge: Payers must follow federal HIPAA privacy regulations and other data use agreements when sharing data. As payers address these barriers and gain more access to data, they then need to combine data from these disparate systems into a useable format (e.g., dashboard or visualization).
  • The political challenge: The historical tension over sharing data between payers and providers is becoming less problematic with the move to value-based purchasing. Both payers and providers aim to share data that can provide meaningful insight into optimizing outcomes and reducing cost for members.

To overcome barriers to accessing data, payers must have a clear and compelling reason to challenge the status quo of relying on claims data. New value-based purchasing models that emphasize efficiency and promote optimal health are pushing expanding access to data that can help achieve these goals.

#3: Data Aggregation

Using a Late-Binding™ EDW as found with DOS, payers can combine data from multiple sources into a cohesive framework. Payers today, however, tend to have a legacy data warehouse based on a traditional warehouse technology that creates several barriers to a PHM strategy:

  • Legacy EDWs are expensive to maintain.
  • They can create interoperability issues due to their limited data integration capabilities.
  • Data management, which uses metadata to describe the source and track the lineage of the data, is often incomplete and inconsistent, putting the premium on familiarity with the underlying data rather than on the organization’s documentation of the data.
  • Flexibility and scalability in these legacy systems (which is critical for the future demands of PHM) may be limited.

Even with the limitations of legacy EDWs, payers continue to rely on them because the older systems are often prohibitively expensive and time consuming to replace. An alternative to ripping and replacing a legacy EDW is to adopt a dual environment strategy (using both the legacy data warehouse and a new IT model) in which users can incrementally move older data and add new data to a second data warehouse.

A data lake approach, such as Hadoop, is another option to help payers overcome legacy data warehouse limitations. A data lake can efficiently store and quickly process massive amounts of data. However, the time and resources required to build the EDW in Hadoop can be considerable, whereas complementing this effort with an open platform data solution that supports analysis and the development of analytical applications can provide value to end users much faster.

#4: Data Analysis

Data analysts need an effective tool to turn raw data into insights that payer leaders and other non-technical data consumers (C-suites, frontline clinicians, etc.) can use. Analysts often use Excel to analyze data. Excel, however, has limitations in identifying the data necessary for a particular analytic project (e.g., insufficient functions) and may not have the capacity for large data sets. To efficiently select and abstract the data, analysts can transform large datasets into visualizations before they are consumed in Excel by following a four-step process:

  1. Identify and evaluate the quality of the required data.
  2. Explore the data to find meaningful trends.
  3. Interpret the data to uncover meaningful insights.
  4. Present the data in a way that users can take appropriate action.

One of the dangers of multiple analysts using Excel is the fragmentation and modification of the data by the analysts, which makes it difficult to maintain the integrity of the data. The development of data visualizations helps payers avoid these data silos by standardizing the data sources and data presentation in a uniform and consistent fashion.

#5: Data Adoption

Perhaps the most overlooked and underappreciated challenge in using data is a culture and processes that promotes adoption of data analytics throughout the organization. A data-driven environment includes four elements that ensure data is useable and accessible:

  1. A commitment from senior leadership.
  2. Data governance.
  3. Evidence-based best practices.
  4. A strategy that ensures the use of data-driven insights to transform internal processes into meaningful outcomes improvement.

Using data governance, payer leadership must drive adoption towards becoming data-driven organizations and incorporating data into decision making. Leadership applies data management tools and processes to support the appropriate security and data accessibility measures, as well evidence-based process improvement practices that focus on the ultimate goal of applying data to improving processes and outcomes.

#6: Data Application

For payers to succeed in PHM, they need to be able to use data efficiently to improve outcomes to meet core goals such as:

  • Identifying and managing high-cost members.
  • Negotiating value-based contracts.
  • Creating an effective provider incentive program.
  • Enhancing quality scores.
  • Maximizing provider network performance.

The above goals require risk adjustment models, predictive modeling, social determinants of health data, and a host of payer-specific business rules (e.g., contractual payment provisions or pre-authorization requirements) to enhance the data. Frontline users must have data in a format that aligns with, rather than disrupts, workflows and impacts the desired outcome (e.g., care gap alerts for high-risk members with diabetes).

Managing Healthcare Data’s Complexity with Open Platform Capabilities

An open-platform data warehousing tool that can support emerging PHM strategies must recognize the immaturity of PHM data analytics, the tremendous growth of data available for PHM, and the exciting potential for applying data in unique ways to improve outcomes. The platform must be flexible to accommodate new data sources and analytic approaches and scalable to meet the expected growth in PHM data.

One example, DOS, can help payers address the data challenges inherent in PHM, and ultimately, lower healthcare costs and improve outcomes for their members. DOS combines the features of data warehousing, clinical data repositories, and health information exchanges (HIEs), giving payers an unprecedented ability to share data and create new opportunities to use data to more effectively manage a member’s health.

The process of transforming data into useful, actionable insights involves additional content, such as terminology and quality measures. A modular, scalable approach packages all content into libraries that can be easily integrated into an existing or new data warehouse. Data is then shared among multiple use cases, eliminating redundancy and optimizing data flow.

To avoid the challenges in adapting legacy EDWs to meet the data acquisition, aggregation, and analysis challenges of PHM and the prohibitive cost of ripping and replacing EDWs, payers might augment the EDW with open-platform technology, such as DOS. Using a data visualization tool, the open architecture of DOS can accommodate data from the existing EDW, or from new data sources, and support ad hoc analysis or analytic application development. DOS also provides a flexible integration point for exchanging data with EMRs or other unique solutions and can prepare data in the legacy EDW for transition to the open platform.

Data and a Data-First Technology Drive Population Health Success for Payers

As payers increasingly rely on PHM strategies to improve health outcomes, lower costs, and excel under value-based payment, one key element will bolster their success: data. The process, however, to becoming a data-driven payer organization is fraught with notable challenges in data availability, access, aggregation, analysis, adoption, and application. To meet these challenges, payers must adopt data management tools that expand on traditional EDW capabilities to fully leverage the growing breadth of data available for PHM. Open platform data warehouses, such as DOS, will give payers the insight to improve their members’ health.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. 3 Best Practices for Payer-Provider Collaboration to Improve Patient Care
  2. Healthcare Payers and Providers: The Best System for Process Improvement
  3. Top 7 Healthcare Trends and Challenges from Our Financial Expert
  4. Healthcare Analytics Platform: DOS Delivers the 7 Essential Components
  5. Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You?