Electronic Health Record Data Collection Practices to Advance Standardization and Interoperability of Patient Preferences for Interpretation Services: Qualitative Study.

Abstract

Background: Poor health outcomes are well documented among patients with a non-English language preference (NELP). The use of interpreters can improve the quality of care for patients with NELP. Despite a growing and unmet need for interpretation services in the US 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 assistance services.

Objective: This study aims to examine current practices for collecting, documenting, and exchanging information on a patient's self-reported preference for an interpreter in the electronic health record (EHR) and the implementation maturity and adoption level of available data standards. The paper identifies standards implementation; data collection workflows; use cases for collecting, documenting, and exchanging information on a patient's self-reported preference for an interpreter; challenges to data collection and use; and opportunities to advance standardization of the interpreter needed data element to facilitate patient-centered care.

Methods: We conducted a narrative review to describe the availability of terminology standards to facilitate health care organization documentation of a patient's self-reported preference for an interpreter in the EHR. Key informant discussions with EHR developers, health systems, clinicians, a practice-based research organization, a national standards collaborative, a professional health care association, and Federal agency representatives filled in gaps from the narrative review.

Results: The findings indicate that health care organizations value standardized collection and exchange of patient language assistance service needs and preferences. Informants identified three use cases for collecting, documenting, and exchanging information on a patient's self-reported preference for an interpreter, which are (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, which are 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 these data between health systems and community-based organizations. The narrative review and key informant discussions identified three potential challenges to using information on a patient's self-reported preference for an interpreter for person-centered care and quality improvement, which are (1) lack of adoption of available data standards, (2) limited electronic exchange, and (3) patient mistrust.

Conclusions: Collecting and documenting patient's self-reported interpreter preferences can improve the quality of services provided, patient care experiences, and equitable health 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 the exchange of this information among health care and community-based organizations.