Cambia Health Solutions and Abacus Insights have unveiled an innovative data aggregation system to improve the patient experience in healthcare. This collaborative effort aims to process information for 3.4 million members across four Blues plans. By ensuring data is actionable and meets stringent criteria, such as accuracy and timeliness, Cambia hopes to deliver personalized care and streamline operations. Abacus Insights plays a key role in validating and standardizing data from diverse sources. This endeavor represents a significant step toward optimizing healthcare data management and enhancing patient care.
Cambia Health Solutions and Abacus Insights are joining forces to revolutionize the patient experience by harnessing the power of data analytics.
In the ever-evolving landscape of healthcare, navigating through vast amounts of data has remained a formidable challenge. However, Cambia Health Solutions is now taking strides to streamline this process and extract valuable insights from the data at hand.
Together with Abacus Insights, a data management company specializing in the interoperability of healthcare networks, Cambia has unveiled a groundbreaking data aggregation system. This innovative platform is designed to process information for approximately 3.4 million members across four Blues plans.
In a recent case study by Abacus, they emphasized the importance of actionable data in delivering personalized care tailored to each individual’s unique needs. To achieve this, the data must meet certain criteria: it must be understandable, usable, timely, and clinically relevant.
Cambia has introduced this data aggregation initiative within the Blues plans it operates in four Western states: Oregon, Washington, Idaho, and Utah. As part of their strategy, Cambia aims to phase out all other data pipelines and warehouses, a move expected to reduce both operating and capital costs, according to the Abacus case study.
With each step of the transformation, Cambia’s confidence in the reliability and timeliness of its data grows. David Haney, Cambia’s Vice President of Data and AI, explained that the system is already operational, and critical workloads are being developed. Thousands of legacy data pipelines are earmarked for retirement, and complex planning, execution, and change management are well underway.
Abacus Insights has set stringent requirements for digitally interoperable payer data, including accuracy, completeness, timeliness, relevance, versatility, and agnosticism in terms of use cases and applications. The initiative aims to acquire claims adjudication data from various sources, such as electronic health records from health information exchanges and clinical case management information from patient advocates.
According to Haney, establishing high-quality, high-speed computer networks forms the cornerstone for members to fully leverage their benefits. Members will experience the benefits through timelier and more personalized outreach from health plan nurse case managers and care advocates, enhanced engagement experiences, and improved coordination with healthcare providers through data sharing.
Abacus Insights plays a crucial role in this process by validating and standardizing data from both structured and unstructured internal and external sources, as outlined in the case study.
Haney pointed out that the scalability offered by Abacus has allowed Cambia to reallocate valuable internal resources from developing routine components to creating intellectual property directly supporting Cambia’s business and members.
Haney further clarified the different categories of data:
– Structured Data: Highly organized and adheres to a defined data model, stored in fixed fields within a record or file, and works well with relational databases (e.g., Claims records, SQL databases).
– Semi-Structured Data: Does not conform to a strict tabular structure, contains tags or markers to separate semantic elements, and is often formatted as XML, JSON, or YAML (e.g., Emails, weblogs, sensor data).
– Unstructured Data: Lacks a predefined data model, has no identifiable structure or organization, and is difficult for computers to interpret (e.g., Images, audio, video, social media posts, PDFs).
Finally, Haney stressed that as the use of this data extends across enterprise use cases, the ability to have real-time or intra-day updates of claims within this single source of truth provides a significant advantage across various enterprise functions, including timely and meaningful support for members by care managers.
