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Low EHR Data Element Fill Rates Impede Quality Reporting Automation

Low fill rates and a lack of standardized EHR data elements across implementations pose challenges for quality reporting automation.

A lack of standardized EHR data elements across implementations has made automation unviable for quality reporting, according to a study published in JCO Clinical Cancer Informatics.

The CMS Merit-based Incentive Payment System (MIPS) provides specialty-specific clinical quality measures (CQMs) for value-based reimbursement. However, quality reporting can be a burdensome task. Clinicians often must sift through charts to extract the required data elements (DEs) and then manually enter measure statistics into a separate quality reporting system.

Automated data extraction from the EHR for MIPS reporting could help alleviate clinician burden.

To determine whether healthcare organizations can leverage EHR data elements (DEs) for oncology MIPS (oMIPS) reporting, researchers accessed patient records through a big-data platform called CancerLinQ.

The platform extracts data from disparate EHRs by importing data in structured data fields (DFs) and manually abstracting information through text mining. At the time of the analysis, the dataset included records of 1.63 million unique patients diagnosed with primary cancer from 63 participating practices representing eight EHR vendors.

The researchers noted that none of the studied EHR vendors adequately implemented the necessary clinical quality-related DEs. Even when the DEs were available in an EHR, they were poorly filled. The average fill rate for oMIPS-associated DEs across all practices was 23 percent.

“Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, EHR systems could only calculate two of the 19 oMIPS CQMs for more than one percent of the patients,” the study authors said.

The authors explained that the complexity and frequent changes of oncology DEs pose challenges to standardization across practices and EHR vendors, which impedes automated data extraction.

“Data in structured EHR fields vary widely among implementations because data capture standards have not been widely adopted by EHR systems, and also because practices do not routinely share data capture templates,” the researchers wrote.

The researchers also noted that EHR implementations frequently involve significant customizations to accommodate clinical workflows. While these customizations may improve end-user satisfaction, they decrease standardization which poses challenges for automated data extraction across care organizations.

The authors suggested several ways to improve automated reporting from the EHR.

From a policy standpoint, CMS could rescind quality measures that EHRs cannot automatically extract. The agency could also incentivize the community-based development of national standards for structured data capture of DEs.

Lastly, the researchers suggested that CMS incentivize the routine automated exchange of standardized DEs between disparate EHR systems and EHR modules.

“These results demonstrate that, in the oncology use case, the EHRs and oncology practices studied are incapable of satisfying oncology MIPS reporting requirements through retrieval of clinically recorded structured DEs,” the authors wrote.

“Crossing the chasm between quality measures and high-quality data will require widespread creation and adoption of common DEs along with improvements in the routine capture and exchange of structured data,” they said.

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