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The American Journal of Managed Care February 2015
A Multidisciplinary Intervention for Reducing Readmissions Among Older Adults in a Patient-Centered Medical Home
Paul M. Stranges, PharmD; Vincent D. Marshall, MS; Paul C. Walker, PharmD; Karen E. Hall, MD, PhD; Diane K. Griffith, LMSW, ACSW; and Tami Remington, PharmD
Quality’s Quarter-Century
Margaret E. O'Kane, MHA, President, National Committee for Quality Assurance
How Pooling Fragmented Healthcare Encounter Data Affects Hospital Profiling
Amresh D. Hanchate, PhD; Arlene S. Ash, PhD; Ann Borzecki, MD, MPH; Hassen Abdulkerim, MS; Kelly L. Stolzmann, MS; Amy K. Rosen, PhD; Aaron S. Fink, MD; Mary Jo V. Pugh, PhD; Priti Shokeen, MS; and Michael Shwartz, PhD
Did Medicare Part D Reduce Disparities?
Julie Zissimopoulos, PhD; Geoffrey F. Joyce, PhD; Lauren M. Scarpati, MA; and Dana P. Goldman, PhD
Health Literacy and Cardiovascular Disease Risk Factors Among the Elderly: A Study From a Patient-Centered Medical Home
Anil Aranha, PhD; Pragnesh Patel, MD; Sidakpal Panaich, MD; and Lavoisier Cardozo, MD
Employers Should Disband Employee Weight Control Programs
Alfred Lewis, JD; Vikram Khanna, MHS; and Shana Montrose, MPH
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Race/Ethnicity, Personal Health Record Access, and Quality of Care
Terhilda Garrido, MPH; Michael Kanter, MD; Di Meng, PhD; Marianne Turley, PhD; Jian Wang, MS; Valerie Sue, PhD; Luther Scott, MS
Decision Aids for Benign Prostatic Hyperplasia and Prostate Cancer
David Arterburn, MD, MPH; Robert Wellman, MS; Emily O. Westbrook, MHA; Tyler R. Ross, MA; David McCulloch, MD; Matt Handley, MD; Marc Lowe, MD; Chris Cable, MD; Steven B. Zeliadt, PhD; and Richard M. Hoffman, MD, MPH
Faster by a Power of 10: A PLAN for Accelerating National Adoption of Evidence-Based Practices
Natalie D. Erb, MPH; Maulik S. Joshi, DrPH; and Jonathan B. Perlin, MD, PhD, MSHA, FACP, FACMI
Differences in Emergency Colorectal Surgery in Medicaid and Uninsured Patients by Hospital Safety Net Status
Cathy J. Bradley, PhD; Bassam Dahman, PhD; and Lindsay M. Sabik, PhD
The Role of Behavioral Health Services in Accountable Care Organizations
Roger G. Kathol, MD; Kavita Patel, MD, MS; Lee Sacks, MD; Susan Sargent, MBA; and Stephen P. Melek, FSA, MAAA
Patients Who Self-Monitor Blood Glucose and Their Unused Testing Results
Richard W. Grant, MD, MPH; Elbert S. Huang, MD, MPH; Deborah J. Wexler, MD, MSc; Neda Laiteerapong, MD, MS; E. Margaret Warton, MPH; Howard H. Moffet, MPH; and Andrew J. Karter, PhD
The Use of Claims Data Algorithms to Recruit Eligible Participants Into Clinical Trials
Leonardo Tamariz, MD, MPH; Ana Palacio, MD, MPH; Jennifer Denizard, RN; Yvonne Schulman, MD; and Gabriel Contreras, MD, MPH
A Systematic Review of Measurement Properties of Instruments Assessing Presenteeism
Maria B. Ospina, PhD; Liz Dennett, MLIS; Arianna Waye, PhD; Philip Jacobs, DPhil; and Angus H. Thompson, PhD
Emergency Department Use: A Reflection of Poor Primary Care Access?
Daniel Weisz, MD, MPA; Michael K. Gusmano, PhD; Grace Wong, MBA, MPH; and John Trombley II, MPP

Race/Ethnicity, Personal Health Record Access, and Quality of Care

Terhilda Garrido, MPH; Michael Kanter, MD; Di Meng, PhD; Marianne Turley, PhD; Jian Wang, MS; Valerie Sue, PhD; Luther Scott, MS
Quality benefits were equal across racial/ethnic groups with equal personal health record (PHR) use, but nonwhite status and a preference for Spanish language predicted lower PHR registration.
ABSTRACT
Objectives
To estimate the impact of race/ethnicity and written language preference on registration for a personal health record (PHR) that included emailing providers, viewing lab results, refilling prescriptions, and other functionalities, and the impact of PHR use on quality across racial/ethnic groups with comparable access.

Study Design and Methods
Retrospective observational design among 3,173,774 adults. Factors affecting registration were assessed using logistic regression, and propensity score matching techniques assessed the impact of language preference on registration and PHR use on quality of care. Difference-in-differences methods assessed the significance of between-group changes in Healthcare Effectiveness Data and Information Set (HEDIS) scores, such as glycated hemoglobin and lipid screening and control.

Results
Race/ethnicity most strongly predicted PHR registration. After adjusting for multiple factors, Asian American, Latino American, and African American members remained 23%, 55%, and 62% less likely to register, respectively, than non-Hispanic white members. Preference for Spanish as a written language predicted poor PHR adoption. The probability of registration was 0.451 (95% CI, 0.449-0.453) for English language–preferring Latinos and 0.174 (95% CI, 0.173-0.176) for Spanish language–preferring Latinos. For non- Hispanic whites, Latinos, and African Americans using the PHR, HEDIS scores increased after PHR use by 1.3 to 12.7 percentage points, compared with differences of –1.1 to 8.1 percentage points among nonusers. All but 2 difference-in-differences between PHR users and nonusers were statistically significant.

Conclusions
Nonwhite race/ethnicity and Spanish language preference independently predict poor PHR adoption. PHR use is associated with higher quality healthcare, and when PHR use is equivalent across racial/ethnic groups, so is quality of care.

Am J Manag Care. 2015;21(2):e103-e113
Although race/ethnicity and language preference disparities exist in personal health record (PHR) registration, the quality benefits of PHR use are equivalent when racial/ ethnic groups have comparable access.
  • After adjusting for multiple factors, Asian Americans, Latino Americans, and African Americans remained 23%, 55%, and 62% less likely to register, respectively, for the PHR than non-Hispanic whites.
  • The probability of registration was 0.451 (95% CI, 0.449-0.453) for English language– preferring Latinos and 0.174 (95% CI, 0.173-0.176) for Spanish language– preferring Latinos.
  • PHR use is associated with higher quality healthcare; when PHR use is equivalent across racial/ethnic groups, so are quality gains.
Robust Internet-based patient portals and personal health records (PHRs) provide patients with access to personal health information and supporting patient education materials, provide secure messaging with providers, and offer functions to schedule appointments and refill prescriptions.1 In 2010, 7% of Americans used a PHR, although much higher registration rates occurred in integrated systems with robust and well-established PHRs, such as the Veterans Health Administration and Kaiser Permanente (KP).2-4 PHRs hold promise to enhance care quality, improve patient-provider relationships, and encourage healthy lifestyle behaviors.5,6 For instance, we previously demonstrated that among more than 35,000 patients with diabetes and/or hypertension, the use of secure e-mail with providers through a PHR was associated with improved Healthcare Effectiveness Data and Information Set (HEDIS) scores.7,8 The relationship between patient portal use and improved glycemic control and other quality measures among patients with diabetes has also been documented.9-12

Racial/ethnic disparities in PHR registration and use exist1,13-21; however, few recent studies designated Latinos or Hispanics as a racial/ethnic category and included patients whose preferred language was not English.1,13,21 Race/ethnicity is one factor of many that likely influences PHR registration; recent studies also uniformly examined age and gender. However, healthcare utilization was only measured in an underserved population,21 and health status was measured by the presence of a limited number of chronic conditions.1,21 To the best of our knowledge, no evidence exists as to the impact of written language preference on registration rates, which is logically more germane to using a PHR than is spoken language.21 Finally, no published studies document the quality impact of PHR use across racial/ethnic groups. An important question is, if patients with differing race/ethnicity have equivalent PHR use, do they experience similar clinical benefits?22

We had several hypotheses:
1. Registration rates among non-Hispanic whites would be higher than among members of any other racial/ethnic group. Other factors would affect registration rates, such as age, gender, disease burden, healthcare utilization, length of membership, and distance to the nearest medical office, but race/ethnicity would have the strongest influence on registration rates. To test this hypothesis, we examined registration rates and the impact of race/ethnicity relative to other factors.

2. Non-English language preference would reduce the likelihood of PHR registration, regardless of race/ethnicity. To test this, we assessed the impact of English and Spanish language preference among Latinos on registration.

3. Once barriers to access were removed, quality benefits of PHR use would accrue equally across racial/ethnic groups. To test this, we assessed the effect of PHR use on healthcare quality across racial/ethnic groups with comparable PHR access.


METHODS

We used a retrospective observational design to investigate the impact of race/ethnicity on PHR registration relative to other factors, and a propensity score–matched pair cohort design to investigate the impact of written language preference on registration and the effect of PHR use on quality of care in racial/ethnic groups with comparable PHR access.

Personal Health Record

Kaiser Permanente (KP) is the largest not-for-profit integrated healthcare delivery system in the United States, serving 9 million members. KP’s PHR, My Health Manager, is integrated with the electronic health record (EHR), KP HealthConnect. Features include access to portions of the medical record, test results, patient education, prescription refills, appointment scheduling, and the ability to securely e-mail providers. Patients must register for and activate an account to sign on to use these features. Access is free to all members who receive marketing information about the PHR, regardless of their race/ethnicity.

The PHR is currently available primarily in English. Although many patient education materials are available in Spanish, English is currently the only language option for complete navigation and for access to the health record, including test results, prescription refills, appointment scheduling, and secure e-mail with providers. As of March 2013, 65% of all age-eligible KP members were PHR-registered. The PHR has become a significant mode of care delivery; 28% of contacts between patients and primary care providers occur by secure e-mail.

Study Cohorts

To investigate the impact of race/ethnicity on PHR registration relative to other factors, we included all patients 18 years and older with active KP memberships on December 31, 2010, and with complete data for all variables of interest. To investigate the impact of written language preference on registration, we limited the population to Latino patients—the only group composed of enough individuals with varying self-reported language preferences (English and Spanish). To assess the impact of PHR use on quality, we included Latino, non-Hispanic white, and African American members with diabetes or hypertension from 1 region (KP Southern California, KPSC) who registered for the PHR and activated their accounts between January 1, 2006, and October 31, 2010, and who had active memberships between January 1, 2009, and December 31, 2010.

Outcome Measures, Covariates, and Data Sources

A main outcome measure was PHR registration. We defined members as registered if they had ever signed up for PHR access. Quality outcome measures were scores on 7 HEDIS effectiveness-of-care measures: 6 comprehensive diabetes control measures (lipid and glycemic screening and control, and retinopathy and nephropathy screening) and 1 control measure for diagnosed hypertension.23 Baseline HEDIS scores came from the calendar year before the one in which members registered for the PHR and follow-up scores came from the subsequent calendar year. Quality outcomes were examined in relationship to PHR use, which we defined as registration for and activation of an account.7

We included covariates that may have also affected registration and were used to create propensity score-matched pairs: race/ethnicity, age, gender, language preference, distance to the nearest medical office building, number of annual office visits, illness burden (measured by current DxCG risk scores derived from proprietary [Verisk Health, Inc] software forecasting risk-adjusted utilization24), and KP membership tenure. Data on age, gender, diagnoses, membership tenure, distance to the nearest medical office, illness burden, utilization of office visits, PHR registration, HEDIS effectiveness-of-care measures, and self-reported race/ethnicity and language preference were all obtained from the EHR.

Propensity Score Matching

Impact of language preference on registration. We calculated a propensity score for each Latino member with a self-reported preference for English or Spanish as a written language, using SAS 9.2 (SAS Institute, Inc, Cary, North Carolina). The propensity score included the independent variables of age, gender, DxCG score, tenure, number of annual office visits, distance from nearest medical office building, and region. We used the greedy matching algorithm without replacement to create 209,873 matched pairs out of 211,995 Latino members preferring Spanish and 521,235 Latino members preferring English.25 Propensity scores were matched to the second decimal place (Table 1).

Impact of PHR use on quality across racial/ethnic groups with comparable access. We used propensity score matching within Latino, non-Hispanic white, and African American populations because they yielded sufficient sample sizes. For each HEDIS measure, we calculated a propensity score for each member with diabetes or hypertension. The propensity score included the independent variables of age, gender, illness burden, tenure, number of annual office visits, distance from nearest medical office building, and baseline HEDIS score. We used greedy matching without replacement to pair each member who used the PHR to a member of the same race/ethnicity who had not used it.20 The number of propensity score-matched pairs within racial/ethnic groups varied by measure, from 4481 (A1C control among African Americans with diabetes) to 28,612 (blood pressure control among non-Hispanic whites with hypertension). Propensity scores were matched to the second decimal place, and no difference in baseline periods was allowed. Table 2 provides an example of the pre- and post matching balance for 1 measure (A1C screening) among Latinos.25

Statistical Analysis

Logistic regression was used to assess factors affecting PHR registration for the entire population. Registration was the dependent variable, and race/ethnicity, age, gender, illness burden, number of annual office visits, distance from the nearest medical office, region, and tenure were independent variables. Modeling was performed with SAS 9.2.

To get the propensity score–matched data for the impact of language preference on registration, we calculated probabilities from the proportions of registered and unregistered members and bootstrapped the confidence intervals (CIs).26 Similarly, we used bootstrapping methods to compute CIs for HEDIS scores, and compared outcomes for propensity score-matched African American, Latino, and non-Hispanic white members with diabetes and/or hypertension who had and had not used the PHR. We assessed both the absolute value of changes before and after PHR use, and the difference-in-differences. We received an institutional review board waiver for this quality assessment project.

RESULTS

Impact of race/ethnicity and other factors on PHR registration. Among 3,173,774 members meeting our inclusion criteria, 1,764,121 (56%) were registered to use the PHR (Table 3). Women, people 30 years or older, and non-Hispanic white members were more likely to register than men, younger individuals, and members of any other race/ethnicity. Of registered members, 57% were female, and 59% were aged 20 to 64 years. Among registered members, 10% made no office visits during the year, compared with 18% of unregistered members.

 
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