Patient Perspectives in Comparing Hospitals for Childbirth: Insights from Hawai‘i

Abstract

Childbirth is a national priority area for healthcare quality improvement. Patient perspectives are increasingly valued in healthcare, yet Asian American and Pacific Islander (AAPI) perspectives of healthcare quality are often understudied, particularly from individuals with limited English proficiency (LEP). Our study goal was to understand factors that consumers in Hawai‘i, including AAPI and those with LEP, use to compare patient care in hospitals, especially for childbirth. A total of 400 women ages 18 years and older with a recent childbirth completed an in-person interview in English (n=291), Tagalog (n=42), Chinese (n=36), or Marshallese (n=31) on O‘ahu, Hawai‘i. Participants described if (yes/no), and how (open-ended), they believed hospitals in the state varied in providing patient care. Open-ended responses were coded by two independent raters using the framework approach. Respondents were 53.3% Asian, 30.8% Pacific Islander, 13.5% White, and 2.5% other race/ethnicity; 17.8% reported limited English proficiency. Overall, 66.8% of respondents affirmed that local hospitals varied in patient care; Marshallese, other Pacific Islanders, and non-English speakers were significantly less likely to say that Hawai‘i hospitals varied in patient care. Among those who endorsed hospital variation, commonly reported themes about this variation were: (1) patient experience, (2) patient overall impression, (3) childbirth options (eg, waterbirths), (4) staff, (5) facilities (eg, “emergency capabilities”), (6) high-tech levels of care, and (7) the hospital's area of focus (eg, “women and children”). We provide insights into factors that diverse patients use to compare patient care in hospitals in Hawai‘i to add value, relevance, and engagement to healthcare quality research and dissemination efforts.

Despite substantial national, state, and local investment in building a publicly-available evidence base on hospital quality that consumers can use to compare facilities, these data are underused by the public generally.^1–14 Many patients are not aware of these measures, find them confusing, and/or are not using them to make healthcare decisions.^11–13 Healthcare quality can indeed be complex, conflicting, and even, controversial to measure.¹⁵ Yet there is an expectation that if healthcare quality information can be measured and presented to consumers in a way that is meaningful to them, they will choose healthcare facilities delivering higher quality care, thus driving overall healthcare quality improvement.^9–11 Hence, there is a growing interest in understanding what patients value in comparison of healthcare facilities in order to improve the relevance of, and engagement in, public reports of hospital quality.^13,16 These developments align with a growing interest in consumer perspectives on healthcare generally, including efforts by the Patient-Centered Outcomes Research Institute to align clinical research with outcomes of high relevance to consumers.¹⁷

Asian American and Pacific Islander (AAPI) perspectives on healthcare and healthcare quality may be distinct, but are often understudied, particularly from individuals with limited English proficiency (LEP).^18,19 AAPI subgroups experience health disparities across a diverse array of healthcare outcomes, including low birth weight, preterm deliveries, and inefficient prenatal care.^20–25 LEP presents a significant barrier to healthcare and is associated with poor outcomes, including poorer communication in healthcare.^21,25 AAPI patients may not have access to culturally competent care, or care that is sensitive to their unique social, cultural, and linguistic needs.^{18,19,21–26} It is critical to consider the perspective of AAPI patients as they comprise a growing proportion of the population in the United States (U.S.).²⁷ About eight percent of the total U.S. population over the age of 5 has LEP.²⁸

Our study goal was to understand factors that consumers in Hawai‘i, including AAPI population groups and those with LEP, used to compare patient care across hospitals, especially for childbirth. Hawai‘i is racially and ethnically diverse with 28% of individuals ages 18 years and older speaking a language other than English at home.²⁹ We focused on childbirth as childbirth is a national priority area for healthcare quality improvement,^30,31 and the most common reason women are hospitalized in the U.S.³² Childbirth is particularly well-suited for comparative quality engagement as pregnant women are typically engaged consumers with advanced notice about their upcoming hospitalization and interest in choosing a facility for delivery that is right for them.^8,13,14,19

Methods

Study Design

This was a mixed-methods study in which women ages 18 years and older who had delivered a baby in the previous two years (N=400) were interviewed on O‘ahu, Hawai‘i between July 2013 and January 2015. This study was designed as a convenience sample for insights from specific major ethnic and linguistic groups in the state, not as a comprehensive surveillance of the opinions of all population groups in the state. Our recruitment goal was 400 women with targeted racial/ethnic/linguistic combination of approximately 50 participants in each group to capture diverse perspectives and to ensure that no racial/ethnic group dominated results. This sample size met the need for a sufficient sample size to detect quantitative differences for moderate effects of at least 85% statistical power both across racial/ethnic groups and within racial/ethnic groups by LEP, while maintaining a realistic sample size to reach thematic saturation for qualitative analyses. To ensure diversity of this sample, women were recruited across a variety of locations, following recruitment methods used in previous studies of mothers in Hawai‘i.³³ We used community-based recruitment activities, such as tabling in-person at several new baby exhibitions and local events, and posting advertisements on the bus and Craigslist. Interested recruits were interviewed in-person at a location of their choice after completion of an informed consent.

Interview

In-person interviews, including both closed-ended questions (for quantitative analyses) and open-ended questions (for qualitative analyses), were conducted for about one hour in English (n=291), Tagalog (n=42), Chinese (n=36), or Marshallese (n=31). As it was not feasible to interview individuals across all relevant linguistic groups, we chose three non-English speaking groups of high policy relevance to Hawai‘i. These included Chinese and Tagalog, two of the three most commonly spoken non-English languages in Hawai‘i. Two Chinese dialects (Mandarin and Cantonese) were included because in Hawai‘i, only 37.9% of Chinese speakers can speak English very well and Chinese is the most common non-English language spoken in the U.S. after Spanish.³⁴ Tagalog, a Filipino language, was included because Filipinos are a large and growing AAPI population in the U.S.³⁴ In Hawai‘i, Tagalog is the leading non-English language spoken at home.³⁵ Finally, Marshallese, a language from the Republic of the Marshall Islands, was included due to the growing population of these individuals in Hawai‘i and the U.S., and because they have significant health disparities and a unique immigration status.³⁶

Bilingual research assistants translated all interview and consent materials. A separate bilingual individual then back-translated the instruments into English for each language. The two translators met to resolve discrepancies to ensure reliable instruments.³⁷ Interviews were pilot tested with members of the focal population in all study languages to ensure comprehension and relevance. More detail on interview methods can be found elsewhere.¹⁹ Multilingual respondents within these linguistic categories could choose to interview in the language of their preference. Participants received a $30 gift card to a local drug store as an incentive.

Variables

Personal Characteristics and Demographics.

Race/ethnicity was self-reported using an established method for the multiethnic and multiracial population of Hawai‘i; participants were asked to report all racial/ethnic groups with which they identified and then also asked for their primary racial/ethnic identity.³⁸ Primary race/ethnicity was used as the race/ethnicity variable. Those interviewed in a non-English language were asked to report their level of spoken English proficiency across four levels. Following standard procedures, any rating of less than the highest level (corresponding to “very well”) was considered as limited English proficiency.³⁹ Age, education, and location of the participant's birth were obtained. Self-reported low health literacy was assessed with the commonly used, validated question,⁴⁰ “How confident are you filling out medical forms by yourself?” Response choices were: not at all, a little bit, somewhat, quite a bit, and extremely. Those who reported less than “quite a bit” of confidence were considered to have low self-reported health literacy. Self-reported high-risk pregnancy was obtained from a “yes” answer to the question: “Have you ever been told by a doctor or other healthcare provider that you had, a high-risk pregnancy?” To determine if this was their first baby delivered in Hawai‘i, respondents were asked to self report if this was either their first baby or their first baby delivered in the state.

Comparison Information.

Factors of relevance to patients in comparing patient care across hospitals was measured with both closed- and open-ended questions. As illustrated in Figure 1, we first asked if respondents felt that “some hospitals in Hawai‘i provided better care than others in general” and “specifically for women who are delivering babies” to which they could respond yes or no. Respondents who responded “yes” to either item were considered to endorse a belief in variation of patient care across hospitals. These respondents were then asked the open-ended questions: “…what sorts of things do you think some hospitals in Hawai‘i do better than others in general” and specifically, “for women delivering babies.” All answers to open-ended questions, as well as detailed notes of the answers, were recorded using the iSurvey tool (Harvest Your Data, Wellington, NZ).

Figure 1.

Study and Interview Design

Quality-specific Themes.

All interviews were reviewed by at least one reviewer. Two of the three independent reviewers (CM, CD, or NC) listened to a 10% sample of audio recordings to standardize the transcription process. Due to the large sample size and the length of the interview, not all open-ended questions for all participants were transcribed. These transcription notes were compared descriptively with interviewer notes for content overlap of salient issues. Strong content overlap was seen between the interview notes and the targeted transcriptions from the two independent reviewers, but was not assessed with formal metrics. For the rest of the interviews, one independent reviewer listened to the audio recording and made a targeted transcription for each open-ended question. In targeted transcriptions, reviewers transcribed responses only when respondents answered questions of relevance to the analyses, rather than creating a verbatim transcription of the full interviews. When coding themes in the qualitative analyses, independent reviewers had at least one targeted transcription as well as interview notes to consider, ensuring all salient details were captured.

Quantitative Analyses

Demographics and belief that quality varied were compared descriptively in univariate analyses using chi-square tests for categorical variables by race/ethnicity and English proficiency combinations. Quantitative analyses were performed in STATA 12.0 (StataCorp, 2011; College Station, TX). Statistical significance was set at a P-value of < .05.

Qualitative Analyses

Using both interview notes and targeted transcripts, themes were identified using the framework approach by two of three independent raters (TS, CM, or MN) with expertise in childbirth, healthcare quality, and/or AAPI communities.⁴¹ Coders first reviewed and coded all material independently, then met and used an iterative approach to confirm themes. Coders also met regularly with the study team (including a provider, researcher, and healthcare quality expert) to discuss and contextualize emerging results. After 400 responses were all reviewed, coders re-reviewed consensus coding documents to ensure congruence with the final study themes. The final consensus coding was used for analyses.⁴² Responses could be coded to one or more themes. Coders considered respondent-reported thoughts about healthcare quality, capturing expected issues from previous research and theory (eg, cleanliness, patient-centeredness) and emerging factors based on their perspectives.^1,5,13,15

To be clear about our distinct themes, “staff” were the type of staff that were employed ubiquitously across hospitals, for example, nurses, or front desk staff. While in some health systems midwives are commonly part of healthcare staff, this has not been the case in Hawai‘i.⁴³ Only some hospitals in the state had midwives on staff during the study period. Yet some women have a strong preference for midwives and make hospital decisions specifically to have access to midwives. Thus, we put this in the “childbirth options” theme. Waterbirths are similarly part of the “childbirth option” category as only some hospitals allow waterbirths and some women have strong preferences for them. “Facilities” included factors like its cleanliness, parking, or location.

Availability of Data and Materials

Data from the notes and transcripts used for coding and codebooks are available from the authors.

Institutional Review Board (IRB)

This study was approved by the University of Hawai‘i IRB (CHS # 20533).

Results

Demographics

As seen in Table 1, the sample was diverse with 53.3% Asian (22.5% Filipino, 13.8% Chinese, 14.0% Japanese, 3.0% other Asian, which included Korean, Thai, and Vietnamese), 30.8% Pacific Islander (13.0% Native Hawaiian, 13.3% Marshallese, 4.5% Other Pacific Islander, which included Samoan and Tongan), 13.5% White, and 2.5% Other (which included Black, Hispanic, Native American, Multiracial, or “Don't Know”) race/ethnicity. This was the first baby delivered in Hawai‘i for 83.0% of the sample. Almost 18% of the sample reported LEP. Compared to those who with English proficiency, those with LEP were significantly less likely to be educated or self-report health literacy. They were more likely to have public health insurance and did not differ significantly from the English speakers in terms of age group or if they had a high-risk pregnancy.

Table 1.

Demographic Characteristics by Level of English Proficiency (N=400)

	English Proficient	Limited English Proficiency	P-value	Total
n (%)	329 (82.2%)	71 (17.8%)		400 (100%)
	%	%		%
Education
Less than high school	6.1	29.6	<.001	10.3
High school	44.7	39.4		43.8
College degree or more	49.2	31.0		46.0a
Race/ethnicity
Chinese	7.0	45.1	<.001	13.8
Filipino	22.2	23.9		22.5
Japanese	17.0	0		14.0
Other Asianb	3.6	0		3.0
Native Hawaiian	15.8	0		13.0
Marshallese	9.4	31.0		13.3
Other Pacific Islanderc	5.5	0		4.5
White	16.4	0		13.5
Otherd	3.0	0		2.5
Self-reported low health literacy	21.3	35.2	.013	23.8
Mother's age group
18–24 years	26.4	22.5	.65	25.8
25–34 years	55.9	62.0		57.0
35 years and older	17.6	15.5		17.3
Had baby in last year	89.1	70.4	<.001	85.8
U.S.-born	68.4	2.8	<.001	56.8
Health insurance
Quest/Medicaid	31.0	65.2	<.001	37.0
Tricare/Department of Defense	12.0	1.4		10.1
Private	50.9	31.9		47.6
More than one insurance	5.5	1.4		4.8
None	0.6	0		0.5
Self-reported high-risk pregnancy	30.4	22.5	.186	29.0

^aNumbers do not total 100% due to rounding.

^bOther Asian included Korean, Thai, and Vietnamese.

^cOther Pacific Islander included Samoan and Tongan.

^dOther included Black, Hispanic, Native American, Multiracial, or “Don't Know.”

Quantitative Findings

As seen in Table 2, overall, 66.8% of our sample reported a belief that patient care varied by hospital; 74.2% of those who spoke English proficiently and 32.4% of those who were LEP reported a belief that patient care varied by hospital (P<.001). This finding varied significantly by LEP and race/ethnicity. Among those who spoke English, only 29.0% of Marshallese and 50.0% of Other Pacific Islanders believed that hospitals vary in patient care compared to 84.6% of Native Hawaiians, 85.7% of Japanese, and 77.8% of Whites (P<.001). Among those with LEP, only 9.1% of Marshallese and 34.4% of Chinese reported the belief that hospitals in Hawai‘i varied in patient care compared to 58.8% of Filipinos (P<.004).

Table 2.

Participants Endorsing the Belief That Hospitals Vary in Patient Care by Level of English Proficiency (N=400)

n	English Proficienta		Limited English Proficiencyb		Totalc
	329		71		400
	%	Standard Error	%	Standard Error	%	Standard Error
Race/ethnicity
Chinese	60.9	.10	34.4	.09	45.5	.07
Filipino	80.8	.05	58.8	.12	76.7	.04
Japanese	85.7	.05	—	—	85.7	.05
Other Asiand	83.3	.11	—	—	83.3	.11
Native Hawaiian	84.6	.05	—	—	84.6	.05
Marshallese	29.0	.08	9.1	.06	20.8	.06
Other Pacific Islandere	50.0	.12	—	—	50.0	.12
White	77.8	.06	—	—	77.8	.06
Otherf	90.0	.10	—	—	90.0	.10
Total	74.2	.02	32.4	.06	66.8	.02

^aThe comparison by race/ethnicity among those with English Proficiency varied significantly (p<.001).

^bThe comparison by race/ethnicity among those with LEP varied significantly (p=.004).

^cThe comparison by race/ethnicity including the full sample varied significantly (p<.001).

^dOther Asian included Korean, Thai, and Vietnamese.

^eOther Pacific Islander included Samoan and Tongan.

^fOther included Black, Hispanic, Native American, Multiracial, or “Don't Know.”

Qualitative Findings

Key themes, brief descriptions, and illustrative quotes to explain factors that patients believed to be important from their responses to open-ended questions are seen in Table 3. The most common themes noted to describe relevant variations in patient care across hospitals were (1) the patient experience (reported by 44.6% of those who believed in variation); (2) overall impression (39.5%); (3) high-tech levels of care (25.8%); (4) childbirth options (25.1%); (5) staff (17.6%); (6) facilities (14.2%); and (7) others, don't know, or vague descriptions (16.4%). Individuals could report more than one theme within a statement. Participants seldom reported a specific clinical outcome (eg, rates of cesarean sections) as something they compared across hospitals. In fact, only eight respondents noted considering specific patient clinical outcomes, which are commonly shared with the public as quality measures. Also, few (10%) of the respondents among those who reported that hospitals varied, were able to explain why they felt that hospitals did so. In considering whether hospitals varied, at least seven respondents - who all had reported they believed hospitals varied - emphasized a theme of personal experience as necessary to understand quality variation (eg, “I did not go to another hospital, so how would I know how quality varied?”).

Table 3.

Factors that Patients Described as Varying in Patient Care by Themes and Subthemes (N=267)^*

Main Code	Percentage and Number Reporting	Theme Description	Example(s)
Patient Experience	44.6% (n=119)	This focuses on patient experiences of care. Subthemes include communication with providers, having a “family” atmosphere, having a good relationship with providers, cultural competency, discrimination, and the importance of the patient being treated as a unique individual rather than just a number.	“Some nurses, they take care of other people nicely just because of their race… If they know that you're Micronesian, it's like they don't really give you the same treatment as if it was a local person.” (Marshallese respondent)
Impression Focused	39.5% (n=105)	Individuals report the hospital's area of focus (eg, “women and children”) as a main variation in quality, which they claim contributes to “better care.” These factors seemed likely to be influenced by advertising, rumors, or impressions and were often from hearsay.	“Everybody comes to [Hospital Name], even from outside the state.” (Japanese respondent) — — — — — — — — — — [Because of their specialization on women and children] “I think [Hospital Name] has a better understanding of the nature of the woman in labor.” (Mixed-race respondent)
High-Tech/Level of Care	25.8% (n=69)	The ability to provide high-tech levels of care, including access to the “NICU” and “emergency capabilities,” was an important area of variation in terms of quality.	“There's no NICU in some hospital[s], so you'd have to be separated from babies if there's a problem. That would be a big factor in deciding for me. Not having a NICU would be a no-go.” (Native Hawaiian respondent)
Staff	17.6% (n=47)	This focuses on adequate staffing, and wanting staff to be kind, engaged, available and knowledgeable.	“They're just more family- like [Hospital Name], to me they care more for you especially with the nurse that I had, she just kept me comfortable, she kept talking to me, and didn't make me feel uncomfortable whatsoever. She was very helpful.” (Filipina respondent)
Childbirth Options	25.1% (n=67)	This includes options for different types of childbirth techniques, classes, and access to midwives.	“Access to prenatal care, and support. [Hospital Name] has breast feeding classes, car seat 101 tips, infant CPR.” (Native Hawaiian respondent) — — — — — — — — — — “[Hospital A] talk[s] with patients about financial availability.” (Native Hawaiian respondent) — — — — — — — — — — “[Hospital A] took care of husband, in addition to the mother and baby.” (Hispanic respondent) — — — — — — — — — — “Some hospitals offer more options to the midwives, more personalized options, like the option for a midwife, or the option for different sorts of supplies that they can bring into the birthing room, or better equipment. Like I mentioned before some hospitals offer birthing pools, where you can have a water birth, or at least you can be in the water before you give birth. At least at [Hospital X] what I can say is they have very nice showers, and so, I don't think it's standard amongst all the hospitals. Each hospital has its own area that is slightly better than the other, depending on what you like. I like the showers, it was very helpful. Also, some hospitals have more advanced equipment that allow you to not always be hooked up to the machine, so you're free to walk around if you need, if you like. That's supposed to be very important for the process anyways, to be able to walk around, and so you're not just stuck in bed the whole time.” (Hispanic respondent)
Facilities	14.2% (n=38)	This includes patient impressions on the physical facilities, including the size of the hospital and/or individual rooms, the age of the hospital, and security on hospital grounds.	“Some hospitals are bigger, so that means they have more room for rooms or specialty stuff.” (Filipina respondent) — — — — — — — — — — “The post delivery is different in different hospitals. For [Hospital Name], everything is on the same floor, and the rooms are big—delivery and post-delivery, recovery rooms—and you get to stay in the same room. [Hospital Name], you have to move around a lot, and it really depends on how many people give birth, no matter where you are, to how much to move. But, the rooms at [Hospital Name] are smaller.” (Chinese respondent)
Specific clinical outcome	2.2% (n=6)	Patients consider specific outcomes, such as rates of cesarean sections, which are commonly reported to the public as quality measures.	“Some support women in their decisions for natural birth than others. And that's simply based on the results of cesarean rates; women who go in intending to have a natural birth and end up having a C-section, or end up having inductions etc., or interventions.” (Chinese respondent)

^*Others, don't know, or vague descriptions” are not listed in this table. Forty-seven individuals made a statement that was coded into this category.

Another major qualitative finding was that patients were very cognizant of quality trade-offs across different domains. For instance, as shown by this respondent's quote, hospitals known for high-tech levels of care may operate efficiently at the expense of patient-centeredness: “From what I hear from my friends who delivered at [HOSPITAL NAME], they definitely focus more on the laboring to make sure that it's a very good experience. So, they take the time to teach you how to breastfeed, they take the time to labor with you, they really try to follow your birth plan without endangering you or the baby… [HOSPITAL NAME], on the other hand, does better with high risk pregnancies because, like I mentioned before, they do see a higher population of pregnant women, but sometimes I feel like it is like a baby factory. It's almost like you have a time limit. You have to have your birth before this time or else we might have to C-section you…but…I like [HOSPITAL NAME] because they see more complicated pregnancies, I feel they do so much better handling them. They have access to the NICU.”

Similarly, another respondent stated: “I think they provide care in different type of ways that some maybe have more higher experience level or higher specialty level, while others are more likely to listen to the individual and treat the individual as a person instead of just using general policy.”

Discussion

As public reporting of hospital quality grows, it is critical to understand what factors patients value in their decisions around healthcare facilities.^8,13,14 Childbirth is a particularly relevant moment in which patients often compare facilities.^8,13,14 Yet over 30% of women did not endorse our question of whether they believed hospitals varied in patient care. Some respondents, especially LEP Chinese, and both English proficient and LEP Marshallese were less likely than other groups to endorse this variation. Thus, some AAPI ethnic and linguistic subgroups may be less aware of quality variation across hospitals. Developers of hospital quality reports should consider these issues to engage diverse audiences in effective comparison of healthcare quality information. Even the premise that hospitals do vary in quality in meaningful ways might need to be deliberately established.

Our study showed that commonly-used obstetric hospital quality metrics, such as cesarean delivery rates, are likely relevant to women, but the publicly-reported metrics around this topic were not commonly used by women in decision-making about hospital preference for childbirth. While many women mentioned a strong preference to not have a cesarean delivery, only two respondents specifically noted variation across hospitals on cesarean delivery rates. This is despite the fact that this term is one of the most commonly reported metrics in public reports of hospital quality and is generally understood by women.^14,44

At the same time, women had interesting and sophisticated understanding of healthcare factors, many of which may be relevant for quality comparisons, and their trade-offs. Many women with a recent delivery characterized hospital quality in terms of patient-focused experience (43.9%), general impressions (39.2%), and the general desire for high-tech levels of care (25.8%). Patient-reported factors may be challenging to measure systematically by an objective source. However, some experiences that women described as varying across hospitals, such as availability of medical specialties at the hospital and staff to patient ratio, are distinctly recognizable as factors that would come up in conversations. Recent research on the value of Yelp to hospital quality has shown the value of the narrative description of quality.^2,45 Research shows consumers feel increasingly comfortable giving detailed personal histories online and most of us are now very familiar with using such anecdotal information to make decisions across many consumer domains.^2,45 There is a need to determine how to include this familiar type of information into public reports of hospital quality. This need is also congruent with the relevance of personalized information from family and friends among some AAPI groups, especially those with LEP, to make decisions.¹⁹

Given the complexity of measuring, explaining, and providing data that may measure quality for consumers, it is important to mention that not all metrics are of equal value to the healthcare system. One individual reported she appreciated one hospital for “better service, better doctors, better food.” These factors are not necessarily of equal importance. Although patient satisfaction is clearly critical to high quality care, clinicians still hold a responsibility to the patients that is beyond “maximizing satisfaction.”¹

Health literacy is also important to consider in this domain. Innovative solutions to present easy-to-interpret healthcare information are critical, particularly for individuals with low health literacy and/or educational attainment.^5–7 Diverse dissemination and engagement strategies, such as clinical-based programs,⁸ may be important for these populations, as well as for non-English speakers.

As we move towards greater patient-centered care, it is important to understand the perspectives of Asian American and Pacific Islander populations, two of the fastest growing racial groups in the United States.²⁷ Our sample included substantial numbers of AAPI participants as well as many respondents with LEP and provides quotes and details from these respondents. Our study highlights the importance of considering differences by race/ethnicity and by LEP.

Strengths and Limitations

This study has many strengths and some limitations. We allowed participants to provide answers around factors they believed varied by hospitals, allowing for diversity in patient-reported outcomes that were coded in our qualitative analyses. Closed-ended questions could have provided different insights. The study was performed in Hawai‘i, did not provide a one-to-one representation of the population, and focused on only three of the more than 60 Asian and Pacific Islander languages, targeting some particularly relevant languages in Hawai‘i.⁴⁶ Our findings may not apply to other locations, cannot provide comprehensive data to represent the state population, and/or for speakers of other languages. This study focused on LEP, rather than language of preference, which can be distinct.³⁹ We focused on healthcare quality information during childbirth, an event when consumers often have the luxury of time to decide on a preferred hospital, which is not always the case in healthcare decisions. Use of healthcare quality information may be lower generally in times of greater stress and urgency.⁴⁷ This study was a convenience sample. Due to our large sample size, we did not have verbatim transcripts for all participants and did not calculate concordance across targeted transcripts and interview notes using traditional metrics.

Conclusions

Patient conceptualization of factors relevant to variation in patient care may not align with available public reports of healthcare quality data. Some AAPI ethnic and linguistic subgroups may be less aware of quality variation across hospitals. Efforts to engage diverse AAPI populations in healthcare quality information should consider: (1) more languages, (2) better explanation of the value of existing quality metrics, and (3) integration of quality metrics with factors valued by women in healthcare decisions.

List of Abbreviations

AAPI

Asian American/Pacific Islander

LEP

Limited English proficiency

AHRQ

Agency for Healthcare Research and Quality

IRB

Institutional Review Board

Conflict of Interest

None of the authors identify any conflict of interest.