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Regenstrief to Implement COVID-19 Dataset Codes with ONC Funds

Health IT leaders at Regenstrief Institute said it will utilize nearly $2 million in funding to develop additional COVID-19 standardized codes to the LOINC dataset.

Regenstrief Institute announced it would develop and add COVID-19 standardized codes to the Logical Observation Identifiers Names and Codes (LOINC) dataset to enhance mapping services and upgrade health IT infrastructure.

After receiving roughly $2 million in funding from the Office of the National Coordinator for Health Information Technology (ONC) and the National Library of Medicine (NLM), health IT professionals at Regenstrief said they intend to boost LOINC standards adoption to increase interoperability between public health organizations and health systems.

“LOINC is invaluable as a tool to help address public health crises such as the current pandemic,” Marjorie Rallins, MS, executive director of LOINC, said in a statement. “These grants will help strengthen the capabilities of LOINC, leading to greater and more rapid information exchange and reporting. Accurate and expeditious data sharing is critical to developing solutions to this or any public health crisis.” 

LOINC aims to streamline health data standardization for more efficient EHR use and health data exchange, which has proven critical during the COVID-19 pandemic.

As one of the most widely-used code systems, LOINC seeks to provide standardization in medical test result identification, observations, and various other clinical measures. LOINC promotes interoperability with new terms for tests and clinical observations available through requests from end users.

A universal coding system should ensure that the receiver can recognize and assess test results, resulting in increased data accuracy and completeness.

“LOINC powers everyday interoperability,” said Steven Posnack, MS, MHS, deputy national coordinator for health information technology at ONC. “From lab tests to clinical observations, LOINC is continuously updated to support innovation in medicine and public health emergencies like we’re in today.” 

The LOINC team will utilize the approximately half-million dollars from NLM to develop content based on federal agency requests. The organization said it would work with several standards development organizations to connect terms with other classification systems.

Since the coronavirus began to spread in early 2020, leaders at LOINC worked with the Centers for Disease Control and Prevention (CDC) and public health labs to create codes to collect and share COVID-19 testing results.

In July, Regenstrief Institute added new COVID-19 standardized codes for laboratory testing and clinical observations to the LOINC dataset.

LOINC, which has users in 179 countries, added over 1,200 new standardized terms, with roughly 150 codes affiliated to COVID-19.

After adding 1,231 total terms, the LOINC database holds over 93,600 total terms.

Along with the coronavirus terms, LOINC added terms for laboratory and clinical test results, surveys related to social determinants of health (SDOH), and food security.

This is yet another step forward in the long road toward improving data exchange and interoperability. Formed in 1994, Regenstrief experts have enhanced the dataset with more relevant codes, aligning with clinical innovations.

Regenstrief added a more extensive set of SDOH codes to LOINC back in March 2020, helping to address the broad problem of SDOH data integration.

The lack of interoperability between SDOH data and EHR systems is the leading cause of this disruption. This is where LOINC and the Strategic Health Information Exchange Collaborative (SHIEC) came into play.

Regenstrief leaders explained the LOINC team has added a handful of SDOH terms over the last couple of years and most recently, the team developed a domestic violence screener.

The developers are codifying specific questions related to domestic violence that can trigger an alert to the provider. Even if it’s a simple yes-or-no question, LOINC creates standards that allow researchers to identify all SDOH-related questions that health systems can utilize. 

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