Google Cloud’s Healthcare Data Engine Brings Interoperability, Power Of BigQuery To Industry Data

Google Cloud has launched Healthcare Data Engine, an end-to-end solution for scalable data and analytics designed for the healthcare and life sciences industries to make sense of their increasing amounts of data in a protected, health information-ready environment on its cloud.

The new offering enables data harmonization at scale across multiple data sources — including medical records, claims, clinical trials and research data — to the FHIR healthcare interoperability standard mandated by the government, allowing organizations to see near real-time views of longitudinal patient records, according to Marianne Slight, Google Cloud’s product manager of cloud healthcare analytics. 

“That’s really important, because without that longitudinal patient record, you’re only looking at part of the data,” Slight said in a media briefing. “It also enables analytics and AI (artificial intelligence) at scale in that secure, compliant, scalable cloud environment.”

Healthcare Data Engine is in private preview. Google Cloud partners Deloitte, Maven Wave, Quantiphi and SADA are pegged as implementation partners to help healthcare and life science customers deploy it.

Chicago-based Maven Wave, a business and technology consulting firm and Google Cloud Premier Partner owned by France’s Atos, has been helping healthcare and life science companies transform their organizations and offerings with Google Cloud for the last several years.

“Maven Wave recently used Google’s Healthcare API, a core component of Healthcare Data Engine, at a large pharmacy benefit manager to ingest data, organize it, harmonize it to different formats and map it to FHIR standards,” said Gretchen Peters, the company’s healthcare practice lead. “With Healthcare Data Engine launching, transformational projects will be even faster and easier to execute. Healthcare Data Engine will help unlock the potential for clients to lower operational costs, drive innovation and realize deeper patient engagement, resulting in better outcomes for patients.”

Historically, healthcare organizations have relied on enterprise data warehouses and electronic health records for retrospective analytics, but today’s use cases need lower data latency to make faster sense of their proliferating data, according to Slight.

“It’s proliferating not just because of the number of data sources, but also because of the different types of data — because of the data that patients are entering themselves at home, as well,” she said. “Some of the use cases that we see our customers focusing on are things like population health — where…our healthcare organizations need to be able to bring in the data from patient wearables, as well as from their own clinical electronic health record systems — health equity for screening patients regardless of any social and demographic factors, and then decentralized clinical trials.”

Out of the box, Healthcare Data Engine can map more than 90 percent of HL7v2 messages such as medication orders or patient updates to FHIR across all leading electronic health record systems, according to Slight. The Health Level Seven International Version 2 (HL7v2) messaging standard allows for the electronic exchange of clinical data between systems.

“(Healthcare Data Engine) gets customers…up and running very, very quickly, and it reduces the cost of maintenance on an ongoing basis,” Slight said. “We understand health care data formats and healthcare data mapping and reconciliation natively. It also offers the fastest time to value.”

Amid the COVID-19 crisis last year, a hospital system with multiple hospitals and data sources went from inception to production in just three weeks with Google Cloud’s systems, enabling them to manage their COVID resource availability and triage where their COVID patients went in near real time, Slight said.

Healthcare Data Engine also opens up the healthcare industry to the analytics and AI capabilities of Google Cloud’s serverless Big Query data warehouse, enabling organizations to process and visualize petabytes of their own patient data for a more holistic view.

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