Payer Solutions and It’s Challenges


Payer solutions sometimes present challenges. Optimizing the delivery of healthcare services that considers best practices, evidence, individual preferences, and cost is only possible if the data needed to inform the decisions is accessible. This data comes from an expanding ecosystem of disparate sources and must be aggregated and assimilated in ways that provide meaningful, actionable insight. For payers in particular, the top payer solutions challenges are effectively collaborating with providers to access and share data, and removing the data interoperability obstacles due to these disparate data sources.

With the goal of tackling these challenges in mind, Health Catalyst created a platform that would serve as a road map in helping healthcare organizations, clinicians and health researchers in dealing with payer solutions and its accompanying challenges.

Health Catalyst believes the traditional distinctions between payers and providers are evolving to embrace new models of delivering and paying for healthcare services. We believe that, with the right evidence, analytics, and methods, payers and providers can transform healthcare. The goal is to apply experience and technology to expand the data available to payers so they can improve the outcomes, enhance the experience, and reduce the cost of healthcare for their members. Many health practitioners and researchers don’t believe that payers and providers harbor a resistance to sharing data. Instead, they recognize that there are technical challenges in aggregating data due to interoperability between a myriad of sources.

This Analytics Platform resolves these challenges and provides an adaptive warehouse of data complimented by a library of analytical applications to help payers improve efficiency. These applications define populations, stratify risk, analyze PMPM costs/payments, support care management, and deliver a host of other uses for these aggregated data sets.

Data in the Analytics Platform needed to support a specific use case is selected (i.e., bound) and then exposed in a relevant Improvement Application or ad hoc query. Binding data, as required, provides the flexibility to support the expanding volume of data and related uses cases as they evolve over time.

Health Catalyst’s solution to the data interoperability roadblock is a flexible and open-access data Analytics Platform complemented with clients’ unique content and business requirements. Augmented with a wealth of Health Catalyst content and a focus on client ownership of the data, the Analytics Platform is the ideal framework for payer and provider collaboration. The different ways are though:

Collaborative Analytics: Collaborations that focus on the Triple Aim are dependent on a data framework that supports sharing analytics and insights, and populates other information related to managing patient/member care.

Any Data Source: Virtually any structured or unstructured data source can be integrated into the Analytics Platform to create a flexible and adaptable data warehouse that supports collaboration based on analytics.

Fingerprinted Client Content: Client-specific content, such as data sources, risk models, metrics, benchmarks, and business lines that can be customized according to the client’s requirements.

Health Catalyst Content: Valuable content provided by Health Catalyst, including a library of applications, industry standard and proprietary risk and attribution models, and professional services that support collaborative analytics.

Open Access: The Analytics Platform architecture provides access to the data to support development of analytics for client-specific use cases.

Data Ownership: A suite of data management tools and professional services that support client ownership over data from data integration to governance.

The Health Catalyst Analytics Platform’s Late-Binding architecture addresses interoperability challenges through direct integration with the data source systems and the ability to load data with minimal transformation. This data extraction process simplifies the aggregation of data into an enterprise data warehouse (EDW), where it is used in a just-in-time fashion to meet a specific use case. The result is a flexible, adaptable data extraction and warehousing process that provides return on value in as little as three months.