Akshay Rajaram1, Daniel Thomas2, Faten Sallam3, Amol A Verma4,5, Shail Rawal5,6. 1. Department of Family Medicine, Queen's University, Kingston, Ontario, Canada. 2. School of Medicine, University College Cork, Cork, Ireland. 3. Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada. 4. Li Ka Shing Centre for Healthcare Analytics Research and Training and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Ontario, Canada. 5. Department of Medicine, University of Toronto, Toronto, Ontario, Canada. 6. Division of General Internal Medicine, University Health Network, Toronto, Ontario, Canada.
Abstract
BACKGROUND: The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed. OBJECTIVES: This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada. METHODS: The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV). RESULTS: A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B. CONCLUSION: The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required. Thieme. All rights reserved.
BACKGROUND: The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed. OBJECTIVES: This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada. METHODS: The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV). RESULTS: A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B. CONCLUSION: The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required. Thieme. All rights reserved.
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