As healthcare transitions from fee-for-service to value-based payment, payer organizations are increasingly looking to population health management strategies to help them lower costs. To manage individuals within their populations, payers must become data driven and establish the technical infrastructure to support expanding access to and reliance on data from across the continuum of care.
To fully leverage the breadth and depth of data that an effective health management strategy requires, payers must address six key challenges of becoming data driven:
1. Data availability.
2. Data access.
3. Data aggregation.
4. Data analysis.
5. Data adoption.
6. Data application.
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.
To effectively manage the complexities of a member population for the duration of their enrollment, payers must address six key data challenges:
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.
Technical and legal barriers can make accessing healthcare data challenging:
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.
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:
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.
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:
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.
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:
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.
For payers to succeed in PHM, they need to be able to use data efficiently to improve outcomes to meet core goals such as:
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).
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.
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.
Would you like to learn more about this topic? Here is an article we suggest:
Healthcare Analytics Platform: DOS Delivers the 7 Essential Components
