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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Public Health. Author manuscript; available in PMC Jul 22, 2012.
Published in final edited form as:
PMCID: PMC2951973
NIHMSID: NIHMS310968
Utilization of Enabling Services by Asian American, Native Hawaiian, and Other Pacific Islander Patients at Community Health Centers
Rosy Chang Weir, Ph.D., Heidi P. Emerson, Ph.D., MPH, Winston Tseng, Ph.D., Marshall H. Chin, MD, MPH, Jeffrey Caballero, MPH, Hui Song, MPH, MS, and Melinda Drum, Ph.D.
Rosy Chang Weir, Winston Tseng, Jeffrey Caballero, and Hui Song are with the Association of Asian Pacific Community Health Organizations. At the time of the study, Heidi P. Emerson was with the New York Academy of Medicine. Marshall H. Chin and Melinda Drum are with the University of Chicago.
Contact information for reprint requests: Requests for reprints should be sent to Dr. Rosy Chang Weir, the Association of Asian Pacific Community Health Organizations, 300 Frank H Ogawa Plaza, Suite 620, Oakland, CA 94612 (rcweir/at/aapcho.org).
Objectives
For underserved Asian Americans, Native Hawaiians, and Other Pacific Islanders (AA&NHOPIs) at community health centers, enabling services, such as interpretation, and eligibility assistance, are critical for access to appropriate care. However, little or no data exist nationally on the need, utilization, and impact of enabling services. This study seeks to examine the utilization and impact of enabling services among underserved AA&NHOPI patients served at four community health centers.
Methods
This project developed a uniform model for collecting enabling services data and implemented it across four health centers primarily serving AA&NHOPIs. We also examined differences in patient characteristics between enabling services users and non-users.
Results
The data showed that health center patients used many enabling services, with eligibility assistance being the most used. In addition, enabling services users compared to non-users are more likely to be older, female, AA&NHOPI, and uninsured (p<.05).
Conclusions
This project is the first to examine uniform enabling services data across multiple health centers serving underserved AA&NHOPIs. More enabling services data and evaluation are needed to develop interventions to improve quality of care for underserved AA&NHOPIs.
Community health centers (CHCs) are safety net providers for some of the country's most vulnerable patients, but many of these patients are unable to access and use this needed medical care without enabling services (ES).1 Major barriers to care include “patient inability to pay,” “culture and language,” and insurance status.2,3 ES have been identified by the National Association of Community Health Centers (NACHC) as key facilitators to health care delivery and defined as “non-clinical services that are specifically linked to a medical encounter or provision of medical services that aim to increase access to health care, and to improve health outcomes.”4 ES at CHCs include language interpretation, health education, and financial or insurance eligibility assistance. ES have been long considered to be critical components of health care delivery for CHC patients, who are disproportionately uninsured, minority and low-income. However, despite their perceived importance and potential for improving health care for vulnerable populations, there is little known about the utilization of ES at CHCs, or the impact of these services on health care access and outcomes of medically underserved populations. In particular, no studies have examined ES and their impact on medically underserved Asian American, Native Hawaiian, and Other Pacific Islander (AA&NHOPI) patients at CHCs.
Little or no studies have examined the effect of ES at CHCs on health care access and outcomes among people of color.5,6,7,8,9 These few studies suggest that ES can make a significant contribution to improved access and quality of care. For example, case management has been shown to be effective at improving specific disease conditions.10,11 Interpretation services have also increased timeliness of care12 and patient satisfaction, and reduced emergency room visits thereby reducing costs.13
Medically underserved AA&NHOPI patients at CHCs, in particular, rely more on ES such as interpretation and eligibility assistance for accessing medical care. The lack of ES lead underserved AA&NHOPIs and other people of color to underutilize medical services at CHCs and be underrepresented in the health care system.14,15,16 For example, communication difficulties due to language or cultural issues are common reasons for AA&NHOPIs to avoid health services17,18 and to feel less confident they can get needed care as compared to non-Hispanic whites.17 ES at CHCs can help underserved AA&NHOPI patients obtain culturally and linguistically appropriate and effective health care.
Culturally proficient health care reduces health disparities.19 Culturally and linguistically appropriate ES can help overcome barriers to navigating the healthcare system, improve patient-provider interactions, increase patient knowledge and understanding of treatments, and improve patient safety.20 Interpretation services can foster patient capacity to navigate care and improve patient-provider communication, resulting in increased medical visits and improved health outcomes. Eligibility assistance and enrollment in health insurance programs can alleviate patient financial concerns associated with care.
Federally qualified health centers (FQHCs) are required to provide reports annually to the Bureau of Primary Health Care (BPHC) as part of the Uniform Data System (UDS) and submit information on some of the enabling services provided by their health centers. However, the current UDS does not adequately capture the full scope of enabling services provided and needed by FQHCs to demonstrate the critical impact of these services and the need to adequately finance them to ensure full primary care access and utilization among medically underserved patients. As of 2007, the UDS report only includes the number of full-time equivalent (FTE) staff and encounters for case managers and education specialists, and FTEs only for outreach workers, transportation and a category for “other”.21
ES are often jeopardized during times of political and financial pressures because they are usually non-billable or non-reimbursable services.22 Some CHCs and federal officials have indicated ES improve health care access and outcomes for medically underserved patients, but have not been adequately supported and funded.23
This study provides important new information about the enabling service needs at CHCs, and the impact of ES on medical care and outcomes for medically underserved AA&NHOPIs. The data can be used by managers, executives, and policymakers for developing new programs and allocating their limited resources to best serve their underserved patient populations.
Data Collection- This project was a collaborative effort among the Association of Asian Pacific Community Health Organizations (AAPCHO), the New York Academy of Medicine (NYAM), and four AAPCHO CHCs. Senior staff from each CHC were members of the project advisory council and actively engaged in all phases of the project including project planning, design, implementation, evaluation and dissemination over a three year period. AAPCHO is a not-for-profit national association representing 27 community health organizations that primarily serve medically underserved Asian Americans, Native Hawaiians and Other Pacific Islanders (AA&NHOPIs). AAPCHO members, predominantly CHCs, provide comprehensive primary health care for over 350,000 underserved AA&NHOPIs annually, and are located in communities with high concentrations of medically underserved AA&NHOPIs.
Project implementation and data collection was conducted at four AAPCHO CHC sites. AAPCHO served as the data repository and led the data analysis and reporting. The project team developed a uniform data collection protocol for enabling services (ES),24 including a Handbook for ES Data Collection, encounter forms, data file layout manual, and ES quick reference card. After comprehensive examination of ES provided at each of the four participating CHCs, we developed nine categories of ES with common definitions across the four CHCs. We chose nine categories based on our conceptual model and to facilitate data collection including (1) eligibility assistance and financial counseling, (2) interpretation, (3) health education, (4) case management – assessment, (5) case management – referral, (6) case management – treatment, (7) transportation, (8) outreach, and (9) other.25 We included only ES data that were linked to a medical encounter. Data submission instructions, template spreadsheets, and databases were provided for each of the sites. During a pilot test, all ES providers (e.g., community health workers, medical assistants, interpreters) at each site were trained for data collection, and the data were evaluated for consistency and accuracy. Each of the four CHCs adopted their own ES encounter form based on the AAPCHO uniform data collection template.24 Thus, although some CHCs may have added additional categories or sub-categories into their enabling service encounter forms, they ultimately collected and reported the same broader categories of uniform data. Site 1 chose to pilot test the system in their social services department, where a large number of its ES are provided. Thus, data from Site 1 does not include all ES provided at that site. All other sites pilot tested their ES data collection center-wide. In addition, each of the CHCs collected data from their patient medical records using their respective medical records database systems.
Site selection- The CHC sites were selected based on their membership in AAPCHO, their geographic representation, and the diversity of their patient populations. All four CHCs share similar characteristics as other CHCs nationwide, with high percentages of uninsured and low-income patients.1,26
The sites are located in New York City, Seattle, Washington, and Oahu, Hawaii (2 sites). One site primarily serves Native Hawaiians and Other Pacific Islanders, whereas the other three sites are comprised of mostly Asian Americans.
Sampling Criteria and Data Collection- We collected patient data from all four sites for general health center descriptive characteristics including ES from January 2004 to December 2004. The patient data included all patients who had one or more primary care visits from January 2004 to December 2004.
We also collected patient and encounter data from three of four sites from June 1, 2003–June 30, 2004 to compare ES user and non-ES user characteristics by age, gender, ethnicity, insurance, and health condition. We defined an ES user as a patient who had a primary care visit in June 2004 and had at least one ES visit in the previous year (June 1, 2003 – June 30, 2004). A non-enabling service user was defined as a patient who had a visit in June 2004 but did not have an ES visit in the previous year. Ambulatory care sensitive conditions (ACSCs) were designated as conditions for which primary and preventive care could help reduce the need for hospitalizations or emergency room visits.27 In sum, we collected individual unduplicated total patient demographic data and total patient encounter data (including one or more encounters per patient) for enabling service and non-enabling service users by ACSCs across three of the four sites, as one Hawaii site had only partially completed the data collection at the time of study. We developed a uniform protocol and data collection template including ICD9 code which was used to extract the data and sent to AAPCHO for analysis. We examined differences in patient characteristics and the percentage of patients with chronic and acute ACSCs between ES users and non-users. Differences between groups were formally tested using logistic or generalized estimating equation (GEE) logistic regression,28 with enabling service use as the dependent variable, the patient characteristic as the independent variable of interest, and site as a covariate to control for site-to-site variation. GEE logistic regression was used when the model included patient condition (acute, chronic, routine), which varied within patient over encounters. Multivariable logistic models were fit to further evaluate the association between enabling service use and patient characteristics. In the multivariable models, AA&NHOPIs were aggregated into a single category and compared with Whites because the odds ratios for the racial categories were of primary interest. Similarly, Medicaid, Medicare and Other Public insurance were combined into a single public insurance category because the odds ratios for the aggregated category were of primary interest; moreover, the detailed insurance categories were all similar in that they were significant in the same direction as the aggregated category.
Description of Community Health Centers (CHCs) – Similar to federally qualified health centers (FQHCs) nationwide, the CHCs are located in high-need areas, open to all residents regardless of ability to pay, governed by community boards to assure responsiveness to local needs, and provide comprehensive health and related services.1 In addition, more than half the patients at each of the four CHCs have incomes at or below poverty level, and between 17% and 46% of the patients are uninsured (Table 1). Sites varied in size and geographic location. In 2004, the number of medical encounters by site was between 30,652 and 145,398. In addition, the composition of the AA&NHOPI patient population varied by site and by geographic location. The percentage of AA&NHOPI patients at the four CHCs was 82% compared to 3.2% at FQHCs nationwide.29 In addition, all of the three states (HI – 79%; NY – 7%; WA – 9%) where the CHCs were located had a higher total AA&NHOPI population proportionally in relation to the U.S. rate (5.1%) with Hawaii having the highest proportion of AA&NHOPIs nationally.30 The majority of patients at three sites spoke a primary language other than English, and at one site, the majority of patients were Native Hawaiian and spoke English as a primary language.
Table 1
Table 1
Health Center Characteristics (January 1, 2004 – December 31, 2004)
Utilization Trends of Enabling Services (ES) – Across the four sites, the number of ES provided at each CHC in 2004 ranged from 7,510 to 26,847 (Table 1). The average number of services per month was 1,335 per site and ranged from 626 to 2,237 services. On average, there were 2.7 services provided per patient. The average number of users per month was 519 per site, and ranged from 201 to 975 (Table 1). The average number of users was correlated with the number of providers and/or resources to provide ES. ES providers reported that patients usually required more than one service at each visit. The most common services across all four sites were eligibility assistance/financial counseling (36%), and interpretation services (29%), followed by case management assessment (9%) and health education/supportive counseling (9%).31 Financial counseling includes enrollment assistance for public insurance programs and linkage to drug discount programs.
The diversity of ES reflects the differing needs of the population that the CHC serves. A site with patients that have many different primary languages provided high levels of on-site interpretation services. A site with one dominant primary language has more bilingual providers, and thus, provided fewer on-site interpretation services. A site located in a community with few public transportation systems provided a high number of transportation services using their own van service.
The average length per enabling service encounter was 19.5 minutes across all four sites and services, and varied by type of service. Health education took the longest service time per encounter on average across all four sites (data not shown). For one site, eligibility assistance took the longest service time per encounter, on average.
Characteristics of Patients who Utilize Enabling Services (ES) –Between 61% and 69% of ES users were female, similar to the composition of CHC patients overall, and average age of ES users varied (Table 1). Sites also varied in AA&NHOPI subgroup composition as well as primary language, and reflected the characteristics of the overall CHC population. All AA&NHOPI subgroups utilized all types of ES. Some AA&NHOPI subgroups utilized interpretation more frequently, including Chinese, Vietnamese, Korean and Filipino patients.
ES users were predominantly covered by Medicaid (20% to 67%), or other type of public insurance, which included state insurance programs. There were also many uninsured ES users (18% to 40%).
Enabling Services (ES) Users vs. Non-Users – Based on unduplicated total individual patient demographic data (June 1, 2004–June 30, 2004) and total patient encounter data (June 1, 2003–June 30, 2004) for three out of four sites, we conducted analyses between characteristics of individual ES users and non-users and found significant differences by age, gender, ethnicity, and insurance type (p<.05) (Table 2). ES users were significantly more likely to be older, female, AA&NHOPI and uninsured than non-users. Multivariable logistic regression yielded similar associations between patient characteristics and ES utilization, after controlling for other factors (Table 4). After controlling for age, gender, insurance, and ethnicity, the proportion of total patient encounters by ES users and non-users with chronic and acute conditions was not significantly different (Table 4). For both groups by total patient encounters, diabetes was the most common chronic condition, and ear, nose, and throat infections were the most common acute condition (Table 3). Patients with diagnoses of acute conditions were more likely to use eligibility assistance services than those with chronic conditions (p<.05) (data not shown).
Table 2
Table 2
Patient Demographics of Enabling Service Users and Non-Enabling Service Users (June 1, 2004 – June 30, 2004)****
Table 4
Table 4
Patient Characteristics Independently Associated with Enabling Services*****
Table 3
Table 3
Chronic and Acute Ambulatory Care Sensitive Conditions of Patient Encounters by Enabling Services Users and Non-Users*
This project is one of the first to examine uniform ES data across CHCs in multiple states and the first such study to examine the impact of ES on medically underserved AA&NHOPIs. AAPCHO, CHCs, and the National Association of Community Health Centers (NACHC) have been collaborating to standardize ES data collection as part of a patient-centered medical home movement. ES are an integral part of CHCs nationally, and a large proportion of CHC patients need ES to access care effectively.1 Our study suggests that ES users are more likely to be people of color (AA&NHOPI), uninsured, female, and older.32 It is not surprising that we found financial counseling to be the most common enabling service given that patients without insurance coverage need eligibility assistance to link them to affordable health services.33 In addition, other studies show that individuals who are publicly insured also need assistance with navigating enrollment requirements including managed care choices, understanding their health coverage and rights, and obtaining provider referrals. People of color with limited English proficiency and that are foreign-born often need interpretation services and cultural liaisons to the U.S. health care system.13 In addition, the elderly population is more likely to have public insurance coverage, have greater language and cultural barriers, and often have co-morbidities and complex medical conditions that require case management.34,35 Further, our study suggests that more patients with acute conditions are uninsured and may delay seeking services and coverage. It is also possible that patients with acute conditions require more eligibility services because an acute as opposed to a chronic condition brings them into the clinic for the first time.
We implemented a multi-site model across three states of ES data collection at four CHCs. This model has demonstrated it is possible to use uniform definitions and coding for ES and encounters, and to collect uniform patient data across CHCs. We aggregated the data for analysis, assessed differences between CHCs and their patient populations, and aim to communicate best practices in provision of ES, particularly for underserved AA&NHOPIs.
These data have several limitations. The results reflect only four CHCs that serve predominantly underserved AA&NHOPIs and are not nationally representative. Thus, it is not clear how the enabling service protocol may be appropriate for CHCs serving a small number of AA&NHOPIs and other underserved ethnic populations. The ES data collection varied across sites, and only one year of data were obtained. Since ES are not reimbursed by encounter, many CHCs are understaffed and found it difficult to find the time to document all the services provided. In addition, ES at one site were only collected in one of its service departments, not center-wide. Thus, the utilization of ES in our study is likely to be underestimated. Our estimates of actual ES may also be understated as a function of our specific definitions. For example, patients utilizing financial counseling services who do not complete an application for a sliding fee or health insurance program are not counted. Continuing CHC collaborations and collection of ES data over a number of years will allow the data collection to become more consistent as well as allow for assessment of trends over time. Documentation of encounters and services was found to be more complete and accurate after training sessions and feedback to ES staff. Annual training sessions will be important next steps. However, the pattern of services that were undocumented were not found to be systematic; thus the characteristics of ES users are likely to be representative. Furthermore, ES for the purposes of this study were linked only to medical encounters. We plan to expand future data collection to include all patients, including dental and mental health patients. We also plan to continue ES data collection with current and new CHC partners to build on these pioneer efforts and develop a larger, more comprehensive demonstration project. Further longitudinal data collection and analysis of types of ES by AA&NHOPI subgroups, types of insurance and specific acute/chronic conditions will also be important next steps.
Despite these limitations, this study was able to provide important new multi-site data across three states about ES at CHCs for medically underserved AA&NHOPIs. Uniform ES data will make it possible to better understand the integral role of these services at CHCs, and to examine the impact of ES in improving access to and quality of care. The data have provided CHC managers with the tools to allocate their limited resources to meet the needs of their patients, as well as inform the development of new strategies and interventions. In fact, the commitment and prioritization by CHC senior management to ES provision and data collection were critical to the success of this project. The utilization of uniform data collection tools nationally across the CHC system could provide valuable information for policy makers, managers, and providers in reducing health disparities and improving quality of care for the most vulnerable populations.
Acknowledgment
This project was funded in part by the Department of Health and Human Services: Office of Minority Health, the Agency for Healthcare Research and Quality, the California Wellness Foundation, and the MetLife Foundation. The authors acknowledge the FQHC Project Site Coordinators, Mary Oneha, Lynn Sherman, Monique van der Aa, and Janelle Jacobs for their valuable collaboration on the Enabling Services Accountability Project, as well as Katherine Chen for her research and administrative support.
Footnotes
Contributors: R. C. Weir and H.P. Emerson originated the study, supervised all aspects of its implementation, synthesized the analyses, and led the writing. W. Tseng assisted with the study and contributed significantly to the writing. M.H. Chin and J. Caballero assisted with interpretation of results and review of the manuscript. H. Song and M. Drum conducted analyses and contributed to interpretation and write-up of the results for the manuscript. All authors helped to develop ideas, interpret findings, and review drafts of the manuscript.
Human Participant Protection: IRB approval was obtained from the New York Academy of Medicine.
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