Abstract
Electronic health record (EHR) implementation may affect patient-clinician communication for diverse safety-net populations. We conducted a cross-sectional survey of English-, Spanish-, and Cantonese-speaking patients in a public hospital clinic with a basic EHR. We examined multivariate associations of patient race/ethnicity, language, and education with perceptions of primary-care provider (PCP) computer use. Among 399 respondents, 25% had less than a high school education, 22% preferred Spanish, and 17% Cantonese. Asian (AOR 3.1), non-English-speakers (AOR 3.6) were more likely to report that PCPs used the computer half or more of the visit. Asians were more likely to report that computers helped PCPs remember patient concerns (AOR 5.6). Non-English-speakers had lower odds of reporting that PCPs listened less carefully to them because of computers (AOR 0.3). Patients at risk for communication barriers may perceive advantages of PCP computer use. Safety-net clinics should consider EHR impact on communication disparities.
Keywords: Health information technology, electronic health records, limited English proficiency, health communication, health care disparities
Safety-net clinics in the U.S. – publicly-funded facilities providing care for socioeconomically disadvantaged populations – disproportionately serve patients with limited English proficiency (LEP) and limited health literacy (LHL). Patient-provider communication barriers may contribute to health inequities disfavoring these patients in access to care, receipt of preventive services, treatment adherence, adverse safety events, and health outcomes.1–11
Incentives from the 2009 Health Information Technology for Economic and Clinical Health Act have triggered widespread implementation of electronic health record systems (EHRs) in U.S. safety-net clinics previously reliant on paper charting.12,13 Prior research suggests that EHR use may both facilitate and inhibit communication depending on clinicians’ baseline communication skills, their clinical expertise, and the style of computer use.14–16 Meanwhile, computers can actively shape the opening of encounters, triggering the clinician with clinical reminders and modifying the visit agenda.17 However, studies to date have not included diverse patient populations.14–17 Given the complexities of triadic patient-clinician-computer communication,18 it is important to consider the positive and negative ways safety-net EHR implementation may affect communication with LEP and LHL patients.
We conducted this study to elicit patient attitudes toward computer use in a safety-net clinic and to examine whether clinician computer use is experienced differently across patient race/ethnicity, language, and education.
Methods
We conducted this study at an internal medicine clinic in an academically-affiliated public hospital in San Francisco, California. The clinic population is divided almost equally across Latinos, African Americans, Whites, and Asian Pacific Islanders, and 40% have a primary language other than English. The clinic uses a basic EHR,19 which includes patient demographic characteristics; medications and allergy lists; some hospital and outpatient specialty notes; laboratory and diagnostic reports / images; and electronic prescribing via fax to regional pharmacies. Primary care providers (PCPs) – faculty and resident physicians and nurse practitioners – electronically document all visit notes and submit computerized referrals for specialty care and radiologic imaging.20–22 The EHR is not fully functional, lacking computerized ordering of lab testing and alerts for guideline-based interventions or screening tests.19
In June–August 2011, we conducted a brief cross-sectional, anonymous, self-administered written questionnaire using convenience sampling of English-, Spanish-, and Cantonese-speaking adults receiving primary care. We trained bilingual research assistants to recruit patients consecutively in the waiting room and administer the written questionnaires. Because our population includes patients with limited literacy, research assistants were trained to ask and assess whether patients preferred or needed assistance and to administer the survey to patients who could not read or complete questionnaires independently. Research assistants translated questionnaires into Spanish and Cantonese, and other research assistants independently verified the accuracy and clarity of translation. Six items assessed patients’ perceptions of computer use by their “main doctor or primary care provider.” First, patients answered, “When we are together in the room, my provider spends ___ on the computer,” with responses of “no,” “a little,” “half,” “most,” or “all of the time.” Then, patients rated agreement (“strongly agree” to “strongly disagree”) about whether the computer: “helped my provider understand my health issues,” “remember my concerns,” or “take better care of me” or made the provider “listen to me less carefully” or “look less at me.” The 5 response options ranged from “strongly agree” to “strongly disagree.”
Patients answered three items from the Consumer Assessment of Healthcare Providers and Systems (CAHPS), assessing in the last 12 months how often providers explained things in a way that was easy to understand, listened carefully, and showed respect for what patients had to say.23
Respondents reported their age, gender, highest educational attainment, race/ethnicity, and type of primary care provider (nurse practitioner, resident physician, or faculty physician). They also responded to a question that we used for this analysis as a marker for patient computer use: “Where do you normally go to use the Internet?” (responses: “home,” “relative or friend’s home,” “library,” “school,” “other place,” or “do not use the Internet”). We classified as non-users those who chose “do not use the Internet.”
Data analyses
The dependent variables were patient perceptions of high computer use, agreement with other attitudinal items about clinician use, and the summary CAHPS score. For time “my provider spends on the computer,” we categorized “high clinician computer use” as “half,” “most,” or “all of the time.” We dichotomized patients’ agreement with the other statements, combining “strongly agree” and “agree” vs. “strongly disagree,” “disagree,” or “neither agree nor disagree.” We calculated a summary CAHPS score by linearly transforming each of these three items, then averaging and converting to a percent.24–26 We dichotomized the CAHPS score as better (vs. poorer) communication for patients, with the maximum score of 100.
The primary independent variables were race/ethnicity, non-English language, and educational attainment less than high school. We used logistic regression to examine separately the associations between each independent variable and each dependent variable. We then conducted multivariate logistic regression analyses adjusting for patient age, gender, and Internet use and provider type.15 The indicator for patients requiring assistance with questionnaires was collinear with patient language and not included in final models. Because patients were not asked to identify their specific PCP on the survey, analyses did not account for clustering by PCP. All analyses were conducted using Stata 11.0 (College Station, TX: StataCorp LP, 2009).
Results
Among 399 patients (Table 1), 31% were Latino, 29% Asian, 17% African American, and 18% White. The average age was 54.5 years, 56% were women, 22% preferred Spanish and 17% Cantonese. For education, 25% reported less than high school, and 37% required assistance to complete questionnaires. Half (51%) reported Internet use, and this differed by race/ethnicity, language, and educational attainment (79% White vs. 31–49% for non-White; 71% English vs. 23% for non-English speakers; 62% high school graduates vs. 16% patients with less than high school education, all p<0.01).
Table 1.
Characteristics of patients surveyed about primary care clinician computer use at an urban, hospital-based safety-net clinic (n=399)
Characteristic | ||
---|---|---|
| ||
Age, years (SD) | 54.5 (12.5) | |
| ||
Female, n (%) | 195 (55.7) | |
| ||
Race/Ethnicity, n (%) | ||
White | 60 (18.0) | |
African American | 57 (17.1) | |
Latino | 105 (31.4) | |
Asian | 96 (28.7) | |
Native American, Hawaiian / Pacific-Islander, or Other | 16 (4.8) | |
| ||
Preferred language, n (%) | ||
English | 243 (60.9) | |
Spanish | 87 (21.8) | |
Cantonese | 69 (17.3) | |
| ||
Educational attainment, n (%) | ||
Less than high school | 83 (25.2) | |
Some high school | 34 (10.3) | |
High school graduate or GED | 86 (26.1) | |
Some college or 2-year degree | 73 (22.2) | |
4-year college graduate | 29 (8.8) | |
More than 4-year college degree | 24 (7.3) | |
| ||
Internet use, n (%) | ||
User | 160 (50.8) | |
Non-user | 155 (49.2) | |
| ||
Primary care provider, n (%) | ||
Faculty physician | 95 (23.8) | |
Nurse practitioner | 56 (14.0) | |
Resident physician | 134 (33.6) | |
Unsure / no response | 114 (28.6) |
When asked how much time their PCPs spend on the computer during a visit, 58% of patients reported little or no time, 24% half the time, 11% most of the time, and 7% all the time.
Table 2 depicts respondents’ perceptions of clinician computer use. Most patients agreed that the computer helped their providers understand the patients’ health concerns (81%), remember the patients’ concerns (76%), or take better care of them (74%). However, 20% stated that their providers listened less carefully because of the computer, and 13% agreed that their providers looked at them less because of the computer.
Table 2.
Patient perceptions of primary care clinician computer use at an urban, hospital-based safety-net clinic (n=399)
No time | A little time | Half of the time | Most of the time | All of the time | |
---|---|---|---|---|---|
When we are together in the room, my provider spends ___on the computer. | 10% | 48% | 24% | 11% | 7% |
Strongly agree | Agree | Neither | Disagree | Strongly disagree | |
The computer in the room helped my provider understand my health issues. | 44% | 37% | 9% | 1% | 1% |
The computer in the room helped my provider remember my concerns. | 41% | 36% | 10% | 3% | 2% |
My provider listened to me less carefully because of the computer in the room. | 9% | 11% | 14% | 31% | 26% |
My provider looked less at me because of the computer in the room. | 4% | 10% | 16% | 32% | 29% |
The computer in the room helped my doctor take better care of me. | 39% | 35% | 11% | 3% | 3% |
In unadjusted analyses (Table 3), patients who were Latino (OR 2.2, 95% CI 1.0 – 4.7), Asian (OR 10.8, 95% CI 4.9 – 23.8), non-English-speaking (OR 4.4, 95% CI 2.8 – 7.0), or had less than high school education (OR 5.0, 95% CI 2.9 – 8.9) were more likely to report that PCPs used the computer half or more of the time. Non-English-speaking patients were more likely to report that the computer helped PCPs understand their health issues (OR 3.4, 95% CI 1.7 – 6.9), and Asian patients were more likely to report computers helped their PCPs remember their concerns (OR 2.5, 95% CI 1.1 – 5.8). Non-English-speaking patients (OR 0.4, 95% CI 0.2 – 0.7) and patients with less than high school education (OR 0.5, 95% CI 0.2 – 0.9) had lower odds of reporting PCPs listened less carefully. Those requiring assistance with completing the survey were more likely to report high clinician computer use (OR 6.5, 95% CI 4.0 – 10.5) and that the computer helped PCPs take better care of them (OR 1.7, 95% CI 1.1 – 2.8), while having lower odds of reporting that their PCPs listened less to them (OR 0.4, 95% CI 0.2 – 0.7) (data not shown).
Table 3.
Associations of race/ethnicity, language, and education with patient perceptions of primary care clinician computer use at an urban, hospital-based safety-net clinic (n=399)*
When we are together in the room, my provider spends ___ the time on the computer? (half or more) |
The computer in the room helped my provider understand my health issues. (agree or strongly agree) |
The computer in the room helped my provider remember my concerns. (agree or strongly agree) |
My provider listened to me less carefully because of the computer in the room. (agree or strongly agree) |
My provider looked less at me because of the computer in the room. (agree or strongly agree) |
The computer in the room helped my provider take better care of me. (agree or strongly agree) |
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
OR (95% CI) |
AOR† (95% CI) |
OR† (95% CI) |
AOR† (95% CI) |
OR (95% CI) |
AOR† (95% CI) |
OR (95% CI) |
AOR† (95% CI) |
OR (95% CI) |
AOR† (95% CI) |
OR (95% CI) |
AOR † (95% CI) |
|
African American | 1.3 (0.5, 3.0) | 1.1 (0.4, 3.2) | 1.2 (0.5, 2.8) | 1.7 (0.6, 4.9) | 1.2 (0.5, 2.8) | 1.5 (0.6, 3.9) | 1.3 (0.6, 2.9) | 1.8 (0.7, 5.1) | 1.8 (0.7, 5.1) | 2.9 (0.8, 10.0) | 0.6 (0.3, 1.4) | 1.0 (0.4, 2.7) |
Latino | 2.2 (1.0, 4.7) ‡ | 0.5 (0.1, 1.6) | 1.3 (0.6, 3.0) | 3.4 (0.9, 13.0) | 1.1 (0.6, 2.3) | 2.7 (0.8, 9.0) | 0.5 (0.2, 1.0) | 0.9 (0.3, 2.9) | 0.9 (0.3, 2.4) | 0.9 (0.3, 3.7) | 0.7 (0.3, 1.4) | 0.8 (0.3, 2.2) |
Asian | 10.8 (4.9, 23.8) ‡ | 3.1 (1.1, 6.1) ‡ | 1.9 (0.8, 4.6) | 3.7 (1.0, 14.4) | 2.5 (1.1, 5.8) ‡ | 5.6 (1.6, 19.4) ‡ | 0.6 (0.3, 1.4) | 0.9 (0.3, 2.8) | 1.6 (0.6, 4.2) | 1.5 (0.4, 5.8) | 1.3 (0.6, 2.8) | 1.5 (0.5, 4.3) |
Other | 1.3 (0.4, 4.8) | 1.5 (0.3, 6.2) | 1.9 (0.4, 9.6) | 4.8 (0.5, 42.7) | 1.8 (0.4, 6.8) | 2.8 (0.5, 14.4) | 1.0 (0.3, 3.6) | 1.0 (0.2, 4.2) | 0.5 (0.1, 4.4) | 0.6 (0.3, 4.1) | 2.1 (0.4, 10.5) | 5.3 (0.6, 46.6) |
Limited English Proficiency | 4.4 (2.8, 7.0) ‡ | 3.6 (1.2, 10.6) ‡ | 3.4 (1.7, 6.9) ‡ | 1.1 (0.3, 2.4) | 1.0 (0.6, 1.7) | 0.8 (0.3, 2.9) | 0.4 (0.2, 0.7) ‡ | 0.3 (0.1, 0.9) ‡ | 0.8 (0.4, 1.4) | 0.4 (0.1, 1.7) | 1.1 (0.7, 1.8) | 1.9 (0.7, 5.0) |
Less than high school education | 5.0 (2.9, 8.9) ‡ | 2.2 (0.9, 5.1) | 1.2 (0.6, 2.4) | 0.8 (0.3, 2.2) | 0.9 (0.5, 1.6) | 0.6 (0.3, 1.6) | 0.5 (0.2, 0.9) ‡ | 1.1 (0.3, 3.5) | 0.5 (0.2, 1.2) | 0.4 (0.1, 1.7) | 1.1 (0.6, 2.0) | 0.9 (0.4, 2.2) |
References are White, English, and high school or higher educational attainment for analyses, respectively.
Logistic regression controlling for patient age, gender, and Internet use and type of primary care provider (faculty physician, resident physician, or nurse practitioner).
p-value statistically significant (<0.05).
In adjusted analyses (Table 2), patients who were Asian (AOR 3.1, 95% CI 1.1 – 6.1), non-English-speaking (AOR 3.6, 95% CI 1.2 – 10.6) had higher odds of reporting that PCPs used the computer half or more of the time. Asian patients had higher odds of reporting computers helped their PCPs remember their concerns (AOR 5.6, 95% CI 1.6 – 19.4), and non-English-speaking patients had lower odds of reporting that their PCPs listened less carefully to them because of the computer. (AOR 0.3, 95% CI 0.1 – 0.9).
On the CAHPS score, 70% had the maximum score of 100, and this was not associated with patient perceptions of amount or effects of PCP computer use (not shown).
Discussion
Prior research has found that patients feel computers improve quality of care while remaining satisfied with their PCPs’ communication.15,27,28 Our study adds to this emerging literature by examining differences across an ethnically and linguistically diverse population. Although our findings suggested that some minority and LEP patients are more likely to perceive high PCP computer use, this was not associated with poorer perceptions of communication. Rather, Asian patients perceived computers helped their PCPs remember their concerns, and non-English-speaking patients were less likely to feel computers made their PCPs listen less carefully to them. Thus, patients at high risk for communication barriers may actually recognize advantages of their clinicians’ computer use.
Patients from low-income, minority, and non-English-speaking backgrounds face disadvantages in communication, reporting lower comprehension of medical information and less patient-centered communication and decision-making.29 Clinicians may use computers to overcome traditional communication barriers faced by LEP patients, e.g., educating patients about tests or medications given by providers in other medical settings or counseling patients using images. Patients in safety-net settings may perceive that their clinicians are using computers to help overcome their communication gaps. Although this study cannot address this question, videorecorded interaction analysis could elucidate whether patient-centered clinicians do have communication behaviors that allow them to use computers as a tool to reduce communication disparities for LEP patients.14, 30
Although we did not directly measure health literacy, lower educational attainment and assistance in questionnaire completion were associated with perceptions of high PCP computer use and reduced odds of PCPs listening less in unadjusted analyses. Given LHL patients’ documented communication challenges and health risks,5, 31–35 future studies should examine the impact of clinician EHR use with this vulnerable population.9, 36
The study limitations should be noted. First, half of the clinic’s interpreted patient encounters use video medical interpretation, and positive attitudes towards this form of interpretation may have affected our results if perceived as a “computer.”37 Second, the item to quantify amount of clinician computer use and the categorization of “high computer use” as half or more of the visit are not validated against objective measures of clinician computer use or with other communication outcomes; a mixed methods study could provide this validation and help elucidate the importance of perceptions of computer use intensity in diverse populations. Third, we cannot calculate a response rate or assess differences from those who declined participation, although measured demographics are similar the overall clinic population. Fourth, unmeasured patient, provider, or relationship characteristics could have confounded measured associations. Although we controlled for patient Internet use, this item may not capture adequately patient comfort with computer use, which could have confounded associations with race/ethnicity or language. Fifth, analyses were unable to account for clustering by PCP, and our patients may have difficulty accurately reporting their type of PCP. Sixth, the surveys were self-administered for some patients, while others received assistance. Although this was done to increase the representativeness of our sample population, we could not assess the effect of the different administration methods, since this was collinear with language. Seventh, this cross-sectional study cannot be used to determine causality. Lastly, these results from an urban safety-net clinic may not be generalizable to other patient populations.
In summary, patients at high risk for communication barriers perceived both high levels of clinician computer use as well as advantages to computer use. Future mixed-methods studies throughout the phases of EHR implementation can explore how patient-clinician-computer interactions affect care differentially within diverse populations. Safety-net clinics adopting EHRs should measure the potential impacts on disparities in communication and care for their diverse populations.38
Acknowledgments
This research was supported by Grant #5U79TI020296-04 from the Center for Substance Abuse Treatment of the Substance Abuse and Mental Health Services Administration (SAMHSA). Drs. Ratanawongsa and Barton are fellows supported by the Pfizer Medical Academic Partnership Fellowship in Health Literacy, under the mentorship of Drs. Schillinger and Yelin. “Dr. Ratanawongsa is supported by the Hellman Faculty Award, UCSF Research Evaluation and Allocation Committee, and the Health Delivery Systems Center for Diabetes Translational Research (CDTR) [NIDDK grant 1P30-DK092924].
Footnotes
SAMHSA and Pfizer had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.
The authors have no conflicts of interest to disclose.
Contributor Information
Dr. Neda Ratanawongsa, Assistant Professor in the Department of Medicine at the University of California, San Francisco and the UCSF Center for Vulnerable Populations at San Francisco General Hospital.
Dr. Jennifer L. Barton, Assistant Professor in the Department of Medicine at the University of California, San Francisco.
Dr. Dean Schillinger, Professor in the Department of Medicine at the University of California, San Francisco and the UCSF Center for Vulnerable Populations at San Francisco General Hospital.
Dr. Edward H. Yelin, Professor in the Department of Medicine and Institute for Health Policy Studies at the University of California, San Francisco.
Dr. Jennifer E. Hettema, Assistant Professor in the Department of Psychiatry and Neurobehavioral Sciences at the University of Virginia.
Dr. Paula J. Lum, Associate Professor in the Department of Medicine at the University of California, San Francisco.
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