Cambia Health Solutions, Abacus Insights say their data crunching effort will enhance patient experience
20 Sep 2023
Preview
Source: FierceHealthcare
As part of a data aggregating effort, Cambia Health Solutions wants to “sunset all other data pipelines and warehouses,” which should decrease operating and capital costs.
Making sense of mountains of data continues to be an often elusive goal for most of the healthcare system, but Cambia Health Solutions said it hopes its latest effort will allow it to better corral useable information.
Cambia and Abacus Insights, a data management company that tackles the challenge of making healthcare networks interoperable, launched a new data aggregating system that processes information for about 3.4 million members across four Blues plans.
According to an Abacus case study (PDF), “Cambia recognized that to deliver care orchestrated around the unique needs of each individual, data must be actionable. To be actionable, case study data must be understandable, usable, timely, and have clinical utility.”
Cambia launched the data aggregating effort in Blues plans it operates in four Western states: Oregon, Washington, Idaho and Utah. As part of the data aggregating effort, Cambia wants to “sunset all other data pipelines and warehouses,” which should decrease operating and capital costs, according to the Abacus case study.
Cambia’s confidence continues to grow about the reliability and timeliness of the data as the transformation proceeds. David Haney, Cambia’s vice president of data and AI, told Fierce Healthcare in an email that “this system is up and running, and Cambia is building critical workloads. Thousands of legacy data pipelines are candidates for sunset, and the highly complex planning, execution, and change management are underway and will continue at a logical pace over time.”
Abacus Insights wants digitally interoperable payer data to meet six requirements. The data should be accurate, complete, timely, relevant, versatile and use case and application agnostic. The initiative would obtain claims adjudication data from several sources, such as electronic health records from health information exchanges or clinical case management information from patient advocates.
Haney said connecting high-quality, computer networks that process a very high volume of data messages with little delay lays the foundation for members to better utilize their benefits.
“Members experience this in many ways, including timelier and more personalized outreach from our health plans’ nurse case managers and care advocates, a deeper connection through improved engagement experiences, and improved coordination with providers through data sharing,” Haney said.
Abacus Insights validates and then standardizes data from structured, semi-structured and unstructured internal and external sources, according to the case study.
Haney explained what the categories mean.
Structured data:
Highly organized and defined data model
Stored in fixed fields within a record or file
Works well with relational databases
Examples: Claims records, SQL databases
Semi-structured data:
Does not conform to strict tabular structure
Contains tags or markers to separate semantic elements
Often formatted as XML, JSON, YAML
Examples: Emails, weblogs, sensor data
Unstructured data:
No pre-defined data model
Lacks identifiable structure or organization
Difficult for computers to interpret meaning
Examples: Images, audio, video, social media posts, PDFs
Haney said that “as the use of this data is extended across enterprise use cases, the ability to have ‘real-time’ or ‘intra-day’ updates of claims that make up this single source of truth advantages a broad array of enterprise use cases to include our care managers ability to support members in a timely and meaningful way.”
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