A Qualitative Study of Electronic Health Record Data Collection Practices: Path to Standardization and Interoperability of the Interpreter Needed Data Element (Preprint)

Abstract

Poor health outcomes are well documented among patients with limited English proficiency (LEP). The use of interpreters can improve the quality of care for patients with LEP. Despite a growing and unmet need for interpretation services in the U.S. health care system, rates of interpreter use in the care setting are consistently low. Standardized collection and exchange of patient interpretation needs can improve access to appropriate language services.

This paper examines current practices for collecting, documenting, and exchanging interpreter needed data in the electronic health record (EHR). The paper identifies data collection workflows, use cases for interpreter needed data, challenges to data collection and use, and potential opportunities to advance the standardized collection and use of interpreter needed data to facilitate patient-centered care.

We conducted a targeted literature scan to identify current data standardization efforts for stakeholders, including EHR developers, health systems, clinicians, a practice-based research organization, a national standards collaborative, a professional health care association, and Federal agency representatives to fill in gaps from the literature review.

The findings indicate that key informants value standardized collection and exchange of patient language service needs and preferences. Key use cases for interpreter needed data identified from the discussions include: 1) person-centered care; 2) transitions of care; and 3) health care administration. The discussions revealed that EHR developers provide a data field for documenting interpreter needed data, and that this data is routinely collected across health care organizations through commonly used data collection workflows. However, this data element is not mapped to standard terminologies, such as Logical Observation Identifiers Names and Codes (LOINC®) or Systematized Medical Nomenclature for Medicine–Clinical Terminology (SNOMED-CT®), consequently limiting the opportunities to electronically share this data between health systems and community-based organizations. Key informants described three potential challenges to using interpreter needed data for person-centered care and quality improvement: 1) lack of adoption of available data standards; 2) limited electronic exchange; and 3) patient mistrust.

Collection, documentation, and use of interpreter needed data can improve the quality of services provided, patients care experiences, and health equity in care delivery without invoking a significant burden on the health care system. Although there is routine collection and documentation of patient interpretation needs, the lack of standardization limits exchange of this information among health care and community-based organizations.