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Common EHR Data Standards Hinder Health Equity for Deaf Patients

PCORnet EHR data standards systemically misclassify ASL as an “other” language, which presents health equity concerns for deaf patients.

Current EHR data standards present challenges for identifying deaf and hard-of-hearing (DHH) individuals, according to a study published in JAMIA that raises health equity concerns for care delivery and clinical research applications.

Healthcare quality organizations, such as The Joint Commission, require the collection of patient language preferences in the EHR to ensure that patients have access to services such as an interpreter or bilingual healthcare provider if needed.

Access to such communication services leads to improved patient experiences and better health outcomes among non-English speaking patients. However, accurate language status is not always documented in the EHR due to limitations of common EHR data standards, the study authors noted. 

For instance, the most recent version of the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model (CDM) systemically misclassifies American Sign Language (ASL) as an “other” language.

“This creates an issue of misclassification within health systems and clinical research networks who have adopted PCORnet CDM, limiting the ability for health services and clinical researchers to assess health disparities impacting patients who communicate in ASL—who have been lumped into a language category representing other languages of limited diffusion,” the researchers explained.

To further demonstrate the impact of this misclassification, the study authors used a self-serve data query tool to analyze data from the OneFlorida Clinical Research Consortium, a PCORnet partner that houses data for over 15 million patients across 12 partner organizations.

ASL is also not an available search query for language in i2b2, consistent with the PCORnet CDM. Additionally, as of July 2021, just three out of the 12 OneFlorida partner organizations reported free-text information for the “other” category of language which enables the identification of DHH individuals

“Impeding the accurate capture and reporting of language status embodies underlying values of EHR system developers and institutional decision-makers,” the study authors wrote. “Language status is an equity-relevant variable that enables the conduct of rigorous health equity focused research and dissemination of quality improvement interventions.”

The researchers emphasized that misclassification of DHH patients has implications for care delivery and health equity research.

At the bedside, misclassification of ASL, or other signed language, users as spoken-language users prevents the development of effective patient-provider communication by jeopardizing timely access to interpreters.

In clinical research, language misclassification makes the DHH population invisible, thereby hampering efforts to promote health equity amongst this at-risk population.

“As a patient population experiencing social and healthcare marginalization– language status may be a relevant variable to identify prospective patients in quality improvement projects or to deliver clinical decision support systems (CDSS) to support the care of these patients,” the authors wrote.

Improving the accuracy of EHR language documentation for DHH patients will require a multifaceted approach, they suggested.

First, health systems and informatics organizations, including PCORnet, should include ASL and other signed languages as options within their data frameworks. As one of the largest clinical research networks, PCORnet could set an example for other healthcare organizations by standardizing ASL as a language option within its CDM.

Healthcare systems should work with health IT vendors to ensure that data search engines allow for querying the frequency of patients and encounters based on language status for non-majority languages.

“This will enable researchers to identify opportunities for working with these non-English-speaking populations, and likely promote research and practice conducive to health equity,” the study authors noted.

After health IT organizations ensure the availability of an “ASL” or “signed language” option, clinicians must accurately record the patient’s preferred languages within the EHR.

Healthcare organizations should assess clinical workflows to identify opportunities to facilitate preferred language EHR documentation.

A promising method currently implemented in a primary care clinic serving DHH patients is to have front staff immediately document a new patient’s language preference in the EHR.

“Other opportunities to engage patients include populating information when patients are completing intake paperwork, commonly through patient portals, or when patients are checking-in,” the study authors added.

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