Keywords electronic health record - limited English proficiency - language barriers - equity
- sociodemographic data
Background and Significance
Background and Significance
Equity is a core component of health care quality, and yet it is often viewed as the
“forgotten aim” of quality improvement.[1 ]
[2 ] As a result, the collection of race, ethnicity, and language (REaL) data from patients
is advocated as a first step to identify, monitor, and improve health inequities.[3 ] Without such data, inequities in care may be overlooked. In the United States, the
Centers for Medicare and Medicaid Services mandates the collection of REaL data and
recommends that these data be captured in electronic health records (EHRs).[4 ] Patients with limited English proficiency are a vulnerable population. They are
at increased risk of adverse events when hospitalized and poor health outcomes when
compared with English-proficient patients.[5 ]
[6 ]
[7 ]
[8 ] Data on a patient's preferred language is therefore required to inform clinical
care, such as the need for interpretation services, as well as to drive quality improvement
and research.[3 ]
[9 ]
[10 ]
However, for language data to be useful, it must be accurate. Several studies have
demonstrated issues in the accuracy of language data located in EHRs, including discrepancies
between documented language preferences and patients' self-report.[11 ]
[12 ]
[13 ] Previous studies on the accuracy of EHR language data were conducted in the outpatient
setting in jurisdictions with requirements to collect REaL data.[12 ]
[13 ] Little is known about the accuracy of EHR language data for hospitalized patients
in jurisdictions without mandates to collect such data in EHRs.
Our project aimed to systematically evaluate the quality of EHR language data at two
hospitals in Toronto, Ontario, an ethnically diverse city in Canada with nearly 45%
of residents reporting a mother tongue other than English or French, the official
languages of the country.[14 ] The five most commonly spoken nonofficial languages in Toronto are Mandarin, Cantonese,
Tagalog, Tamil, and Spanish.[14 ] Health care institutions in Ontario are not legally mandated to collect REaL data
in their EHRs. Given that self-report is the most accurate method of collecting sociodemographic
information,[15 ] we sought to audit the accuracy of a patients' preferred language (English or non-English)
as recorded in the EHR, by comparing it to their stated preferred language by interview
(English or non-English). We hypothesized that the EHR language would be moderately
sensitive and highly specific for detecting a non-English preferred language at both
hospitals based on previous work in this area.[11 ]
[12 ]
[13 ]
Methods
Participants
This prospective audit was conducted at two large urban academic hospitals in Toronto,
Ontario, Canada. Patients admitted to the internal medicine wards of either hospital
were eligible to participate. Data were collected over the course of several days
between August 1, 2017 and November 22, 2017. A research assistant reviewed the census
list for each internal medicine ward on the days of assessment. All patients were
interviewed consecutively to identify their preferred language. Patients who were
not available during the interview period were flagged for follow-up. If these patients
could not be interviewed during a second attempt, they were excluded. Patients were
also excluded if they were under airborne isolation precautions or were unable to
communicate (e.g., severe cognitive impairment and aphasia).
Ethics
The Research Ethics Boards at both hospitals formally granted waivers for this project
per local guidelines for quality improvement projects.
Data Collection
The research assistant asked patients a single question in English during the interview:
what is your preferred language for health care communication? Interpretation services
were not used.
At both hospitals, a patient's preferred language is recorded in the EHR by admitting
clerks at the time of registration. At hospitals A and B, the default entry of the
EHR language field is English. We dichotomized the preferred language listed in the
EHR and the preferred language reported by participants to English or non-English
to allow for a clear calculation of the accuracy of the EHR preferred language field.
From a clinical perspective, a non-English preferred language in the EHR should prompt
a health care professional to confirm a patient's preferred language and to assess
the need for interpretation services.
Analysis
Following the interviews, the participants' responses were compared with the language
preferences recorded in the EHR. We defined the overall accuracy of EHR language data
as agreement with the participant's preferred language as determined by interview.
We constructed 2 × 2 tables in Microsoft Excel and computed the sensitivity, specificity,
and positive predictive value (PPV) of EHR data for identifying patients with English
language preference and for identifying patients with non-English language preference.
We calculated two-sided 95% confidence intervals for each measure using the Wilson
score method with a continuity correction.[16 ]
Results
A total of 447 patients were screened for participation and 124 were excluded. Of
the excluded patients, 32 (26%) were excluded because they were unable to communicate
and 92 (74%) were excluded because they could not be reached on the second attempt
([Fig. 1 ]). There were no instances in which language data were missing from the EHR. Of the
323 patients who met inclusion criteria, 96 (29.7%) preferred a language other than
English. Of the 96 patients who preferred a non-English language, 60 (62.5%) were
correctly identified in the EHR language field. Of a total of 227 patients with a
stated English language preference, 225 (99.1%) were correctly identified in the EHR.
The overall sensitivity and specificity of the EHR language field for detecting a
non-English language preference were 63 and 99%, respectively.
Fig. 1 Project flow diagram. EHR, electronic health record.
The sensitivity of the EHR language field for detecting a non-English language preference
differed substantially between the two hospitals ([Table 1 ]). At hospital A, EHR data identified patients with non-English language preference
with 81% sensitivity and 100% PPV, whereas at hospital B, EHR data identified patients
with non-English language preference with 12% sensitivity and 60% PPV.
Table 1
Comparison of sensitivity, specificity, and PPV of the EHR for detecting non-English
and English language preference
Study (n = 323)
Hospital A (n = 163)
Hospital B (n = 160)
Non-English language preference (95% CI)
Sensitivity
0.63 (0.53–0.72)
0.81 (0.72–0.91)
0.12 (−0.01–0.24)
Specificity
0.99 (0.98–1.00)
1.00 (1.00–1.00)
0.99 (0.99–1.01)
PPV
0.97 (0.92–1.02)
1.00 (1.00–1.00)
0.60 (0.17–1.03)
English language preference (95% CI)
Sensitivity
0.99 (0.98–1.00)
1.00 (1.00–1.00)
0.99 (0.99–1.01)
Specificity
0.63 (0.53–0.72)
0.81 (0.72–0.91)
0.12 (−0.01–0.24)
PPV
0.86 (0.82–0.90)
0.88 (0.81–0.94)
0.85 (0.80–0.91)
Abbreviations: CI, confidence interval; EHR, electronic health record; PPV, positive
predictive value.
Discussion
To our knowledge, this is the first multi-site inpatient audit of the accuracy of
EHR preferred language data. We found that EHR language data were highly sensitive
and moderately specific for identifying patients with a language preference for English.
However, the accuracy of the EHR for detecting non-English language preference differed
greatly between the two hospitals, which was inconsistent with our initial hypothesis.
While the EHR of hospital A performed well, the sensitivity of the EHR at hospital
B was lower than expected when compared with previous work. However, such work was
conducted in settings where the collection of REaL data in EHRs is mandated, unlike
in Ontario. For example, Azar et al compared patients' self-reported preferred languages
to the values recorded in the EHR of a large health care organization in Northern
California and found a mean concordance rate of 95%.[12 ] In a study conducted in Massachusetts, Klinger et al demonstrated modest performance
of an EHR used in primary care in detecting non-English language preferences with
a sensitivity of 79%.[13 ]
The absence of a legal mandate to record a patient's preferred language in the EHR
in our jurisdiction may account for some of the differences seen between the two hospitals.
Institutions may be more likely to develop processes to ensure the accurate collection
of REaL data if they are required by law. In addition, the hospitals differed in their
approach to language data collection in important ways. Admitting clerks at hospital
A receive training on how to collect preferred language data, whereas clerks at hospital
B do not. Training frontline staff how to ask about race, ethnicity, and language
in a sensitive and effective manner is integral to collecting accurate data.[17 ] Clerks at hospital A are trained to ask all patients about their preferred language,
and clerks at hospital B typically ask only those who appear to have difficulty communicating
in spoken English. Self-reported language data, as collected at hospital A, are considered
more accurate than observer-reported data.[15 ] Furthermore, when the default setting in the EHR is English, admitting clerks may
be less likely to make changes if they have not received training on the importance
of preferred language data collection.
Given the importance of accurate REaL data in identifying and improving health inequities,
the Institute for Healthcare Improvement (IHI) recommends regular quality assurance.[18 ] It suggests validation sampling, comparing a patient's self-reported race, ethnicity,
and language to the EHR in a random sample of patients. In addition, the number of
unknown, other, or declined responses in the EHR should be tracked. The IHI also recommends
that staff be observed to determine if REaL questions are posed in a manner consistent
with the best practices and that patients also be observed to see how they respond.[4 ] As little as five observations can identify a lack of consistency in following institutional
protocols.[18 ]
The findings of our audit support the IHI's call for quality assurance. Audits have
been shown to improve the accuracy of language data in ambulatory care environments,
but have not been examined in inpatient settings.[12 ] A recent study showed that targeted quality improvement interventions including
(1) standardized training for staff, (2) EHR automatic alerts to prompt staff to enter
REaL data for new patients, and (3) alerts to enter data for missing fields, increased
the collection of REaL data from 71.7 to 84.1% at an academic health center serving
a racially diverse patient population.[19 ] However, the accuracy of the data was not reported. Using quality improvement interventions
to enhance the accuracy of REaL data remains an important priority for future work.
Other potential strategies to improve the accuracy of REaL data include the use of
mobile applications on tablets or computers to facilitate self-report when patients
register for outpatient appointments or in the emergency department. A recent study
found that a sociodemographically diverse patient population could provide health
histories using a web-based platform.[20 ] A systematic review found that survey responses collected via mobile applications
were equivalent to responses collected using other platforms (paper, laptop, and personal
digital assistant) and may improve data completeness.[21 ] While the use of information technology tools at the point of registration could
minimize data entry errors and improve accuracy, hospitals must continue to perform
audits to assess the accuracy of collected data. In addition, non-English speakers
may require assistance from others to enter their data. In these instances, solutions
could include displaying data entry forms in different languages or training admitting
clerks to partner with interpreters to provide support.
When reporting of language data is mandatory, more accurate data may be collected.
EHRs could then be used to study the impact of language on health outcomes and to
inform interventions to improve quality of care and address health inequities. Such
data could also be used to match patients to language concordant staff and to track
if language needs are being met by an institution.[17 ] For example, through the collection of granular and accurate REaL data, the Palo
Alto Medical Foundation created a culturally sensitive consult service to provide
preventive cardiology care to South Asian patients, an identified high-risk group
within their catchment.[12 ] Moreover, the use of professional interpretation is associated with improved quality
of care, and accurate data on patients' language preferences may aid institutions
in allocating resources for interpretation and other support services.[22 ]
Limitations
Our study had several limitations. First, we categorized languages into English and
non-English. Although dichotomizing the language simplified our reporting of the accuracy
of EHR data, it limited our ability to assess and comment on the quality of EHR data
for identifying specific non-English languages. A non-English preferred language is
also not a proxy for limited English proficiency as individuals who prefer a language
other than English for their health care communication may still be proficient in
reading, writing, or speaking English. Second, our results were limited to those patients
who were able to be interviewed. As a result, 25%of patients we approached were excluded,
potentially introducing selection bias. Third, we did not collect additional demographic
data from patients such as gender or race/ethnicity. As a result, we are unable to
compare the samples of the two institutions or examine for relationships between these
demographic characteristics and language. This is an important area for future study.
Fourth, we did not use interpreters to help ascertain the language preferences of
participants. It is possible that some patients who reported an English language preference
did not have a complete understanding of the question. Fifth, given that we found
the EHRs of the two hospitals differed in their sensitivity for the detection of non-English
language preference, our findings are unlikely to be generalizable to other institutions
and local data audits are required.
Conclusion
We found important differences in the accuracy of language preference data in the
EHR of two urban academic hospitals. The EHR data were highly sensitive for patients
with English language preference at both hospitals, but the EHR of one hospital performed
poorly in identifying patients with non-English language preference. The differences
seen in data quality may be due in part to the absence of legal mandates for EHR REaL
data collection in our jurisdiction and differences between the two hospitals in how
they collect language data. However, for language data to be used in clinical care,
research, and quality improvement, it must be accurate. Our findings highlight a need
for standardizing the collection of data on preferred language and for regular quality
assurance.
Clinical Relevance Statement
Clinical Relevance Statement
The collection of race, ethnicity, and language (REaL) data from patients is critical
to identify, monitor, and improve health inequities. Organizations should develop
strategies to collect and audit language data using existing informatics infrastructure.
Multiple Choice Questions
Multiple Choice Questions
1. Which of the following is regarded as the “gold standard” for collecting preferred
language data?
Ensuring all staff receive cultural safety training.
Inferring language from conversing with the patient.
Asking the patient about their preferred language for health care discussions.
Reviewing charts after patients have been discharged.
Correct Answer: The correct answer is option c. It is well established that self-report is the best
way to collect data about patients' preferred language. The Health Research and Education
Trust recommends asking “what language do you feel most comfortable speaking with
your doctor or nurse?” Although it is important to ensure that frontline staff receive
training in cultural safety, such training alone is not sufficient. Patients must
be explicitly asked their language preference.
2. You are the new manager of Informatics and Quality Improvement (QI) at an academic
hospital serving an ethnoracially and linguistically diverse population. Your first
priority is to increase the collection of race, ethnicity, and language (REaL) data.
What is the best QI intervention you could deploy?
Standardized training for staff.
EHR automatic alerts to prompt data entry.
Alerts to enter data for missing fields.
All of the above.
Correct Answer: The correct answer is option d. All of the above. Lee et al[19 ] demonstrated a greater than 10% absolute increase in the collection of REaL data
after implementing all of the QI interventions above.
