Purpose
This descriptive, survey-based study extends earlier research by exploring how Critical Access Hospitals (CAHs) across the nation provide Culturally and Linguistically Appropriate Services (CLAS) for their patients.
Methods
Personal emails announcing a national electronic survey of CAHs in 45 states were sent to 968 of the 1,329 CAHs in the United States (73%). The survey was completed by 137 of the 1,329 CAHs (14.15% participation rate, 10.3% of all CAHs).
Findings
CAHs with larger non-White or non-English speaking patient populations had a greater variety and more frequent use of language services than CAHs that served less diverse populations. CAHs that collected cultural and linguistic information from patients were significantly more likely to have mechanisms in place to ensure this information followed the patient throughout the continuum of care. CAHs that collected cultural and linguistic information from patients offered significantly more mechanisms to address their patients’ cultural and linguistic needs. CAHs with larger non-White or non-English speaking populations were significantly more likely to employ FTEs related to CLAS than CAHs that served less diverse populations. CAHs with larger non-White or non-English speaking populations were not significantly more likely to provide CLAS training than CAHs that served less diverse populations.
Conclusions
Collection of patient demographic information may relate to use of that information in the patient’s healthcare encounter. Location in a diverse population may not be an indicator of the CAHs provision of CLAS services.
3. Background
The United States Supreme Court has interpreted Title
VI of the Civil Rights Act of 1964 to mean that all
healthcare providers who accept Medicare and Medicaid
must provide culturally and linguistically appropriate
services (CLAS) for their patients.
The United States Department of Health and Human
Services’ Office of Minority Health issued a set of
national standards for CLAS to “ensure that all people
entering the health care system receive equitable and
effective treatment in a culturally and linguistically
appropriate manner
Sources: Youdelman, 2008 ; United States Department of Health and Human Services Office of Minority Health, 2001.
4. Necessity for CLAS
Increase in diverse populations in rural areas
No reimbursement for CLAS expenses
Immigrants have higher rates of infectious diseases than
established US populations
Physicians order more diagnostic testing when there is a
language barrier
Lower patient satisfaction scores and decreased patient
compliance when there is a language barrier without
language services
Use of a professional trained interpreter results in less
diagnostic testing, lower cost & shorter length of stay.
Sources: Johnson, 2012; Armanda & Hubbard, 2010; Whitman & Davis, 2008; Hampers, Cha, Gutglass, Binns, & Krug, 2009
5. Language Services
Types of language services
Trained vs. Untrained
On-site vs. Off-site
Language concordant
Availability vs. Use
Clinical staff not encouraged to use language services
Depend on their own limited foreign language skills
Time and inconvenience
Sources: Hudleson & Vilpert, 2009; Diamond, Schenker, Curry, Bradley, & Fernandez, 2009.
6. Culture
Required collection:
Race
Ethnicity
Primary Language
Cultural health disparities affect outcomes
Need for education, awareness and understanding of
cultures in the patient population and community
Sources: Graves, Like, Kelly, & Hohensee, 2007
7. Research Questions
1) Do CAHs that collect cultural and
linguistic information from patients upon
admission have more mechanisms in
place to meet these cultural and linguistic
needs than CAHs that do not collect
cultural and linguistic information from
patients upon admission?
8. Research Questions
2) Do CAHs that collect cultural and
linguistic information from patients upon
admission have more mechanisms in
place to ensure this information follows
the patient throughout the continuum of
care than CAHs that do not collect
cultural and linguistic information from
patients upon admission?
9. Research Questions
3) Do CAHs that have larger non-white
and non-English speaking populations
have a greater number of CLAS specific
employees and provide more training
than CAHs that have smaller non-white
and non-English speaking populations?
10. Research Questions
4) Do CAHs that have larger non-English
speaking populations have a greater
variety of language services available
and use them more often than CAHs
that have smaller non-white and nonEnglish speaking populations?
11. Research Questions
5) Do CAHs that have written policies
and procedures for CLAS allocate
money to CLAS related services more
often than CAHs that do not have
written policies and procedures for
CLAS?
12. Methods
Population: 1,329 Critical Access
Hospitals located in the United States
As
designated by Centers for Medicare and
Medicaid Services
Connecticut, Delaware, Maryland, New
Jersey, and Rhode Island do not have
CAHs.
13. Methods
Web-based Electronic Survey
(Qualtrics)
Invitation
through National Rural Health
Association e-newsletter (December 2012
& January 2013)
Emails to CEOs, CNOs, or other executives
at 1,116 CAHs
Reminder emails sent 10 days after
original email
14. Methods
Each CAH was asked questions about CLAS in their
facility that corresponded to the following categories:
Collection of cultural and linguistic information from patients
Mechanisms in place to meet cultural and linguistic needs
Mechanisms in place to ensure cultural and linguistic information
follows patient throughout continuum of care
Percentage of race/ethnicity and primary languages of patient base
Established multicultural services departments and human
resources/employee training in CLAS
Variety of language services available and frequency of use
Written plans and policies for CLAS
Funding for CLAS
15. Results
270 CAHs opened the survey
183 CAHs responded to the first question
137 CAHs completed the survey (10.31% of U.S.
CAH population; 14.15% of contacted CAHs)
All of the survey questions were answered by 78 of
the CAHs (8.06% participation rate, 5.87% of all
CAHs)
16. Results
Less than 10% of the patients served by the CAHs
responding to the survey have a primary language
other than English
Less than 20% of the patients served by the CAHs
responding to the survey are not white
17. Results
85.5% of the CAHs participating in the survey have a
written patient care policy addressing provision of
language services
51.3% of the CAHs participating in the survey have a
written patient care policy addressing provision of
culturally appropriate services
68.0% of the CAHs participating in the survey are
certified by their State Department of Health, 22.7%
are certified by The Joint Commission
18. Results
CAHs that always use interpreter,
by interpreter type and patient population
18.00%
16.00%
14.00%
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
CAHS with higher
percentage of
patients who don't
speak English (>
8.00% , median
n=62)
CAHS with lower
percentage of
patients who don't
speak English (>
8.00% , median
n=76)
Trained
External
Interpreter
Trained Internal
Interpreter
Untrained
External
Interpreter
Untrained
Internal
Interpreter
19. Results
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
CAHs that never use interpreter,
by interpreter type and patient population
CAHS with higher
percentage of
patients who don't
speak English (>
8.00% , median
n=62)
CAHS with lower
percentage of
patients who don't
speak English (>
8.00% , median
n=76)
0.00%
Trained External Interpreter Interpreter
Trained Internal
Untrained External Interpreter Interpreter
Untrained Internal
20. Results
1) Do CAHs that collect cultural and linguistic
information from patients upon admission have
more mechanisms in place to meet these cultural
and linguistic needs than CAHs that do not
collect cultural and linguistic information from
patients upon admission?
There is a significant positive relationship
between collecting cultural and linguistic
information from patients and the availability of
mechanisms to address the cultural and linguistic
needs of the patients.
21. Results
2) Do CAHs that collect cultural and linguistic
information from patients upon admission have more
mechanisms in place to ensure this information
follows the patient throughout the continuum of care
than CAHs that do not collect cultural and linguistic
information from patients upon admission?
There is a significant positive relationship between
collection of cultural and linguistic information from
patients and having mechanisms in place to ensure this
information follows the patient throughout the
continuum of care.
22. Results
3a) Do CAHs that have larger non-white populations
have a greater number of employees with CLAS
specific duties than CAHs that have smaller non-white
and non-English speaking populations?
There is a significant positive relationship having a larger non-white
populations and having a greater number of employees with CLAS
specific duties than CAHs that have less diverse populations.
There is a significant positive relationship having a larger non-English
speaking populations and having a greater number of employees with
CLAS specific duties than CAHs that have less diverse populations.
23. Results
3b) Do CAHs that have larger non-white populations
provide more CLAS specific training than CAHs that
have smaller non-white and non-English speaking
populations?
There is not a significant relationship having a larger nonwhite populations providing more CLAS specific training
than CAHs that have less diverse populations.
There is not a significant relationship having a larger nonEnglish speaking populations and providing more CLAS
specific training CAHs that have less diverse populations.
24. Results
4) Do CAHs that have larger non-English speaking
populations have a greater variety of language
services available and use them more often than
CAHs that have smaller non-English speaking
populations?
There is a significant positive relationship between
having a larger non-English speaking populations
and having a greater variety of and more frequent
use of language services.
25. Scatter Plot of Correlation between
Language Services & Non-English Speaking Population
26. Results
Descriptive statistics (M & SD) and inter-correlations
Median
1
2
3
4
5
6
7
8
9
1
.330**
.408**
.090
-.021
.202*
.104
.410**
.108
131
124
129
130
129
116
87
129
131
1
.351**
.250**
.290**
.331**
.185*
141
139
128
127
118
87
.543**130
141
1
.093
.060
.191*
.132
.513**
.281**
150
135
134
125
87
137
150
1
.751**
.217**
.045
.131
.306**
138
137
121
87
134
137
1
.173*
-.012
.242**
.371**
137
120
87
133
136
1
.074
.266**
.247**
128
78
124
128
1
.398**
.137
89
88
89
1
.387**
139
139
1. Collect
cultural &
linguistic info
7.00
2. Meet cultural
& language
needs
1700
.860**
3. Follow
continuum of
care
2.00
4. Serve nonwhite population
5. Serve nonEnglish
population
10.00
4.00
6. Employ CLAS
FTEs
0.05
7. Provide CLAS
training
8.00
8. Maintain
written policies
for CLAS
6.00
9. Offer language
services
1
8.00
Notes: * p >0.05; ** p > 0.01
179
27. Limitations
Sample size
Response rate
CAHs may not be representative of other larger or
non-rural hospitals
28. Conclusion & Discussion
There is a positive correlation between CAHs that
gather information regarding race, ethnicity, and
language from the patient CAHs that have more
mechanisms in place to meet the CLAS needs of
patients.
There is a positive correlation between CAHs that
gather information regarding race, ethnicity, and
language from the patient and CAHs who pass this
information on throughout the continuum of care
within their facility. This could be due to the use of
electronic health records.
29. Conclusion & Discussion
There is a positive correlation between CAHs with
larger non-white or non-English speaking patient
populations and CAHs who provide more language
services for their patients.
There is no significant correlation between CAHs
with larger non-white or non-English speaking
patient populations and CAHs who provide more
CLAS training for their employees. This could be
because many hospitals do not use their own staff to
provide language services.
30. Conclusion & Discussion
CAH executives should review how they use the race,
ethnicity, and language information they are collecting from
their patients to determine how they could use this
information to better meet the CLAS needs of these patients
CAH executives should review the mechanisms in place for
meeting CLAS standards and determine how to implement
more mechanisms to better meet the CLAS needs of their
patients.
CAH executives should review the need to offer more CLAS
education provided to their employees and determine how to
measure CLAS competency of their staff.
31. References
Armanda, A.A., & Hubbard, M.F. (2010) Diversity in healthcare: Time to get REAL! Frontiers of
Health Service Management, 26(3), 3-17.
Diamond, L.C., Schenker, U., Curry, L., Bradley, E. H., & Fernandez, A. (2009) Getting by:
Underuse of interpreters by resident physicians. Journal of General Internal Medicine, 24(2), 256262.
Graves, D. L., Like, R. C., Kelly, N., & Hohensee, A. (2007) Legislation as intervention: A survey of
cultural competence policy in healthcare. Journal of Health Care Law & Policy, 10, 339-361.
Hampers, L.C., Cha, S., Gutglass, D.J., Binns, H.J., & Krug, S.E (1999) Language barriers and
resource utilization in a pediatric emergency department. Pediatrics, 103(6), 1253-1256.
Hudelson, P., & Vilpert, S. (2009). Overcoming language barriers with foreign-language speaking
patients: a survey to investigate intra-hospital variation in attitudes and practices. BMC Health
Services Research, 9(187).
Johnson, K. M. (2012). Rural demographic change in the new century: slower growth, increased
diversity. The Carsey Institute at the Scholar's Repository, (159).
United States Department of Health and Human Services Office of Minority Health (2001).
National standards for culturally and linguistically appropriate services in health care: Final
report. Retrieved June 13, 2010 from
http://minorityhealth.hhs.gov/assets/pdf/checked/finalreport.pdf
Whitman M.V., & Davis, J.A (2008). Cultural and linguistic competence in healthcare: The case of
Alabama general hospitals. Journal of Healthcare Management, 53(1), 26-40.
Youdelman, M.K. (2008). The medical tongue: U.S. laws and policies on language access. Health
Affairs, 27(2), 424-443.
The electronic survey link sent via National Rural Health Association’s e-newsletter in late December 2012 and again in mid-January 2013 did not yield any responses. Next, a list of all 1,329 critical access hospitals (CAHs) in the US was obtained from CMS. Their CEOs, CNOs or other executives’ names and email addresses were obtained from facility websites. Personal emails were sent to leaders of 1,116 CAHs (a letter template is shown in Appendix D). Some emails (13%) were returned as undeliverable. Between January 31, 2013 and March 13, 2013, an individual email was sent to 968 facilities with valid email addresses received one individual email with a follow-up reminder email approximately 10 days later, sent as a blind carbon copy (BCC) in batches of 10-50 addresses. There were 213 CAHs for which no email address was available and these facilities were not contacted. A follow up analysis indicated that contacted facilities and facilities that could not be contacted had an identical average number of beds (22.4 beds vs. 22.4 beds, respectively). Of the 968 facilities with deliverable emails, 270 (27.89%) opened the survey and 183 (18.90%) responded to the first question. Complete survey responses were obtained from 137 (14.15%) of the contacted CAHs and were analyzed to answer research questions.
The electronic survey link sent via National Rural Health Association’s e-newsletter in late December 2012 and again in mid-January 2013 did not yield any responses. Next, a list of all 1,329 critical access hospitals (CAHs) in the US was obtained from CMS. Their CEOs, CNOs or other executives’ names and email addresses were obtained from facility websites. Personal emails were sent to leaders of 1,116 CAHs (a letter template is shown in Appendix D). Some emails (13%) were returned as undeliverable. Between January 31, 2013 and March 13, 2013, an individual email was sent to 968 facilities with valid email addresses received one individual email with a follow-up reminder email approximately 10 days later, sent as a blind carbon copy (BCC) in batches of 10-50 addresses. There were 213 CAHs for which no email address was available and these facilities were not contacted. A follow up analysis indicated that contacted facilities and facilities that could not be contacted had an identical average number of beds (22.4 beds vs. 22.4 beds, respectively). Of the 968 facilities with deliverable emails, 270 (27.89%) opened the survey and 183 (18.90%) responded to the first question. Complete survey responses were obtained from 137 (14.15%) of the contacted CAHs and were analyzed to answer research questions.
Discuss data review – Prior to analysis the data was reviewed for normality. Histograms looked fairly normal, skewness and kirtosis calculations showed that data may not be normal. Transformation of data through square root transformation and Box-Cox transformation did not correct the data. Therefore, the original untransformed data was used for all data analysis.
Correlations were performed in SPSS using Pearson’s R which is a very popular correlation coefficient that quantitatively represents the linear relationship between two variables. When it is close to 1 there is a strong positive relationship, when it is close to 0 there is a weak relationship, when it is close to -1 there is a strong negative relationship.