Are There Patient Disparities When Electronic Health Records Are Adopted?

Transcription

1 Part II: Original Paper Are There Patient Disparities When Electronic Health Records Are Adopted? Esther Hing, MPH Catharine W. Burt, EdD Abstract: Using nationally representative samples of visits from the National Ambulatory Medical Care Surveys and the National Hospital Ambulatory Medical Care Surveys (N539,343), this study examines whether electronic health record (EHR) systems have been adopted by primary care physicians or providers (PCPs) for poor minority patients at the same rate as by the PCPs for wealthier non-minority patients. Although we found that electronic health record adoption rates varied primarily by type of practice of the PCP, we also found that uninsured Black and Hispanic or Latino patients, as well as Hispanic or Latino Medicaid patients were less likely to have PCPs using EHRs, compared with privately-insured White patients, after controlling for PCPs practice type and location, as well as patient characteristics. This finding reflects a mixture of high and low EHR adopters among PCPs for poor minority patients. Key words: Primary care providers, electronic health records, poor, minorities. patient s primary care provider (PCP) often serves as his or her first and most A frequent contact with the health care system. The PCP is responsible for providing comprehensive health care services to the patient, including acute, chronic, and preventive services. The PCP also manages information about the health of the patient and coordinates care with other health care providers. Adoption of clinical health information technology (HIT) by PCPs has the potential to improve patient care through enhanced clinical decision support, reduced adverse outcomes, and better coordination of care and information between health care providers. 1 4 For example, adoption of clinical decision support systems (CDSS) and computerized physician order entry (CPOE) reduced frequency or duration of inappropriate antibiotic use for common pediatric illnesses, and improved completeness and uniformity in clinical documentation. 5 Based on evidence that tools such as CDSS and CPOE can improve patient outcomes, the Bush administration formed the Office of the National Coordinator in 2004 with the objective of providing electronic health records (EHR) for most Americans by ,2 Uniform adoption of clinical HIT by health care providers may be effective in reducing adverse outcomes leading to health care disparities. 6 8 However, if adoption Ms. Hing is in the Ambulatory Hospital Care Statistics Branch, Division of Health Care Statistics, National Center for Health Statistics, at the Centers for Disease Control and Prevention, 3311 Toledo Road, Hyattsville, MD 20782; (301) ; ehing@cdc.gov. Dr. Burt was formerly affiliated with the same office. Journal of Health Care for the Poor and Underserved 20 (2009):

2 474 Disparities in EHR adoption? of clinical HIT is uneven, the benefits of this new technology may not be available to the underserved; in general, safety-net providers are slower to adopt new technologies than non-safety-net providers Between 2005 and 2006, use of EHR systems among office-based physicians did not change; however, use of these systems increased among physicians with a larger percentage (20% or more) of revenues from Medicaid, from 5.5% in 2005 to 13.6% in ,12 This finding may be one indicator of adoption becoming more uniform among office-based physicians. However, another study found that among federallyqualified health centers (FQHCs), who are chartered to serve poor and uninsured patients, the odds of EHR adoption was 47% lower among FQHCs serving a large proportion (above the median) of uninsured patients compared with FQHCs serving fewer uninsured patients. 13 In general, little is known about the diffusion of clinical HIT among providers to the underserved, or about the impact of HIT adoption on underserved patients. 9,14 16 To examine further diffusion of EHRs among providers of the underserved, this study examines adoption of EHRs among primary care providers (PCP) in physician offices and hospital outpatient departments. To examine whether EHR adoption is equitable, the paper focuses on the patient panels of PCPs, especially those of PCPs for poor and minority patients. Methods Data sources. Data from the 2005 and 2006 National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) were used to estimate adoption and impact of EHRs on patients. Together, the NAMCS and NHAMCS are annual probability surveys representative of ambulatory care in the 50 states and the District of Columbia. The NAMCS and NHAMCS are components of the National Health Care Surveys, a family of provider-based surveys conducted by the CDC s National Center for Health Statistics. The NAMCS is a survey of non-federal office-based physicians, excluding radiologists, anesthesiologists, and pathologists. The NHAMCS is a survey of emergency and outpatient departments (OPDs) in non-federal, general and short-stay hospitals, including children s general hospitals. For the NAMCS, a sample of 3,000 office-based physicians who report they are in direct patient care is taken from the master files of the American Medical Association and the American Osteopathic Association each year. Starting in the 2006 data year, the NAMCS also includes a separate stratum of community health centers (CHCs) with an additional sample of up to 250 physicians within CHCs. For the NHAMCS, a sample of 600 hospitals was selected from the Verispan Hospital Market Database, with an additional 25 children s hospitals included in The multi-stage sample design selects 112 geographic primary sampling units (PSUs) and then samples hospitals and physicians within PSUs. Physicians are first stratified by their specialty within PSUs before sampling. Sampled physicians are randomly assigned to one of 52 reporting periods throughout the year and hospitals are randomly assigned to one of 16, rotating four-week reporting periods, with only 13 panels used in any one year. The surveys involve a face-to-face induction interview to verify eligibility and to ask questions about the practice or facility characteristics. Providers are then asked to

3 Hing and Burt 475 abstract information about the patient and encounter for a systematic sample of patient visits during their reporting period (approximately 30 encounters per physician, and 150 encounters per OPD). In , the overall (unweighted) visit response rate was 60% for the NAMCS and 73% for the OPD component of the NHAMCS. The combined survey data yield average annual national estimates based on responses from 25,665 physician office visits, and 29,975 hospital OPD visits in 2005, and 29,392 physician office visits, and 35,105 hospital OPD visits in The data collection agent for both the NAMCS and NHAMCS is the U.S. Census Bureau, and the data are centrally processed by Constella Group, Inc. There is 100% independent keying of the induction forms, with a quality control error rate of 0.1%. More information about the data collection procedures and survey background has been published. 21,22 Data from the internal NAMCS and NHAMCS files were used for this analysis. The survey protocols were approved by the NCHS Ethics Review Board. The NAMCS and NHAMCS have been monitoring use of clinical health information technology, including electronic medical records (EMRs) since ,12,23,24 In 2005 and 2006, the NAMCS and NHAMCS expanded information gathered on EMR use. Physician and hospital OPD staff respondents reported whether their EMR was fully electronic, or partly paper and partly electronic. Respondents reporting use of either type of EMR were further asked about functions included in their systems (patient demographic information, computerized orders for prescriptions, computerized orders for tests, test results (lab or imaging), physician clinical notes, reminders for guidelinebased interventions and/or screening tests, and public health reporting). To standardize measurement of EMRs in use, as well as to define EMR systems that approximate the type of EHR system envisioned by the federal initiative, an expert panel defined EHRs as EMR systems with all of the following minimal functions: health information and data, results management, order entry management, and decision support Based on items collected in the NAMCS and NHAMCS, the expert panel considered minimally functional EMR systems (those that permit electronic ordering of prescriptions and tests, as well as electronic viewing of test results and clinical notes) as equivalent to EHRs For the remainder of this paper, minimally functional EMR systems will be referred to as EHRs. We report use of EHRs by office-based physicians and by hospital OPDs, augmented by information from patient visits indicating whether the provider served as a PCP. Information on PCP use of EMRs was missing for 1% of PCPs and patients. In this study, cases missing information on EMR use were included with cases reporting no EMR. Although the resulting estimates are conservative, the percentage missing was small and did not affect results of the multivariate model simulated under two scenarios. That is, model results were identical when missing data were omitted from the model, as well as when the model was run assuming cases missing EMR data actually had EMRs. Adjusting visit sampling weights to yield patient estimates. In the NAMCS and NHAMCS, a sample weight is computed for each sample visit record that takes all stages of the design into account. The weight includes four basic components: inflation by the reciprocal of the probability of selection at the provider and visit level, adjustment for non-response, a calibration ratio adjustment, and weight smoothing. The sum of the

4 476 Disparities in EHR adoption? visit weights is an unbiased estimate of the annual number of visits. Detailed information on estimation for NAMCS and NHAMCS are described elsewhere. 25,26 In this paper, number of patients, rather than number of visits, was estimated from NAMCS and NHAMCS/OPD encounter data using a multiplicity estimator. 27 This was performed by adjusting the visit sample weight by the inverse of the multiplicity indicator (number of visits to the sample provider during the last 12 months, including the sample visit) to account for the increased likelihood of selection for patients with multiple visits. This re-weighting counts an individual patient only once during the last 12 months for each sampled provider, and represents the annual number of patients making office or hospital OPD visits. The re-weighting, however, only adjusts for multiple visits to a single provider. In order to exclude patients visiting multiple providers during the past year, analysis was limited to visits to the patient s primary care provider (PCP) since patients typically have only one principal provider. In , 49.6% of office visits and 40.9% of OPD visits were to the patient s PCP. Because 92.2% of PCP visits occurred at physician offices, the overall percentage of visits to PCPs was 48.8% across these two settings. Analysis. Bivariate and multivariate analyses of the probability that the patient s PCP used an EHR were examined by provider and patient characteristics. Provider characteristics include practice organization (private solo or partner practice, private group practice with three or more physicians, community health center (CHC), other office setting, hospital OPD), geographic region (Northeast, Midwest, South, and West), and urban/rural status as indicated by metropolitan statistical area status. Patient characteristics examined include patient age (younger than 18, 18 64, 65 years and older), sex, race or ethnicity (non-hispanic White, non-hispanic Black, Hispanic or Latino, other), and expected payment sources. In 2005 and 2006, multiple expected payment sources were recorded. In order to count patients only once, payment source was prioritized and categorized as follows: private insurance, Medicare (including patients dually eligible for Medicaid), Medicaid-only patients, uninsured (only self-pay, charity, or no charge), and all other sources. Finally, the median household income, a contextual socioeconomic characteristic of the patients neighborhood, was examined. This characteristic was derived by matching the NAMCS/NHAMCS visit file to 2000 Census files by the patient s ZIP code. Because estimates presented are based on complex sample surveys rather than the universe of office-based physicians, hospitals, and patients, they are subject to compound sampling weights and sampling variability. The standard errors are calculated using Taylor series approximations using SUDAAN software, 28 which take into account the complex sample design of the NAMCS and NHAMCS. Estimates whose standard error represents more than 30% of the estimate are marked with an asterisk to indicate that they do not meet the reliability standard set by NCHS. Statements of differences in estimates are based on statistical tests (e.g., chi-square tests of independence, Student s-t, or weighted linear regression) with significance at the.05 level.

5 Hing and Burt 477 Results During in the United States, an estimated 155,605,000 patients made 502,460,000 visits to their PCP each year (on average, 3.2 visits per patient per year) Electronic health record adoption rates varied by type of PCP seen. Figure 1 shows that PCPs in private solo or partner practice have the lowest adoption rate (5.7%), whereas PCPs in other office settings (including HMOs, faculty practice plans, and urgent care centers) have the highest adoption rate (38.3%). The adoption rates for hospital OPDs exceeded the rate for solo and partner practices. Although the CHC adoption rate is higher than the rate for solo and partner practices, the rate is unstable due to its high variability (standard error is more than 30% of the estimate). The distribution of patients and the extent of provider adoption is shown in Table 1. The vast majority of patients had PCPs who worked alone or in partnership (43.6%) or were in group practices (39.7%). Another 7.5% of patients PCPs were in other office settings. By combining 2005 and 2006 NAMCS data, it is possible to present estimates of CHC patients for the first time. On average, 2.7 million patients had PCPs in CHCs in (Table 1). Overall, 9.2% of patients with PCPs saw them in safety-net settings 29 (7.4% in hospital OPDs and 1.8% in CHCs) (Table 1). The extent of EHR adoption by PCPs practices (Figure 1) is mirrored in the percentage of patients with PCPs who used EHRs (Table 1). Primary care providers in solo or partner practices were less likely to use EHRs (6.6%) than PCPs in group practice (14.8%), other office settings (29.2%), or hospital OPDs (18.2%). The estimated percentage of CHC patients with PCPs using EHRs was unreliable. Urban patients were more likely to have PCPs using EHRs (14.3%) than patients with PCPs in nonmetropolitan areas. Between 2005 and 2006, the percentage of patients with PCPs using EHRs was 10.8% in 2005 and 14.4% in 2006; the difference, however, was not statistically significant. Electronic health record adoption rates by PCPs practices are also reflected in the demographic/socioeconomic makeup of the PCP s patient population. In , EHR adoption among PCPs for privately-insured patients was higher (13.2%) than among PCPs for Medicaid patients (8.3%). Electronic health records adoption by PCPs, however, did not vary among patient distributions by age, race/ethnicity, or known median household income in the patient s ZIP code area (Table 1). Bivariate findings of EHR adoption by practice setting may be correlated with the PCP s patient load of poor (uninsured or Medicaid patients) and minority patients. Medicaid and uninsured patients constituted over half of PCPs patient load (62.6%) in CHCs, while 41.8% of PCPs patients load in hospital OPDs relied on the same payment sources (Figure 2). In contrast, the comparable percentage of Medicaid or uninsured patients among PCPs patient load in solo or partner, group, and other office settings ranged from 13.1 to 27.9%. The distribution of patients race or ethnicity also varied by PCPs practice setting. Nearly half (44.2%) of PCPs patients in CHCs were either Hispanic or Latino (Figure 3), compared with significantly lower percentages in solo or partner practice (12.2%), group practice (12.0%), other office settings (18.0%), and hospital OPDs (16.2%). Primary care providers in hospital OPDs saw a higher percentage

6 478 Disparities in EHR adoption? Table 1. NUMBER AND PERCENTAGE DISTRIBUTION OF PATIENTS WITH PRIMARY CARE PROVIDERS, AND PERCENTAGE OF PATIENTS PRIMARY CARE PROVIDERS USING EHR SYSTEM, UNITED STATES, Patients with primary care providers (n539,343) % of patients primary care providers number in % using EHRs Selected characteristics thousands Distribution (standard error) All patients 155, (1.6) 2005 annual estimate 158,728 NA 10.8 (2.0) 2006 annual estimate 152,483 NA 14.4 (2.3) Provider characteristics Type of setting Private solo or partner practice 67, (1.5) Private group practice 61, (2.8) Community health center 2, * (5.7) Other office setting a 11, (8.1) Hospital outpatient department (OPD) 11, (3.9) Region Northeast 30, (1.9) Midwest 39, (2.2) South 56, (3.2) West 28, (4.2) Metropolitan Statistical Area (MSA) status MSA 130, (1.8) Non-MSA 24, d (1.6) Patient characteristics Age Under 18 years 48, (2.1) years 81, (1.9) 65 years and over 25, (2.1) Gender Male 69, (1.6) Female 85, (1.7) Race or ethnicity Non-Hispanic White 110, (1.7) Non-Hispanic Black 15, (2.9) Hispanic or Latino 20, (2.3) Other 8, (3.4) (Continued on p. 479)

7 Hing and Burt 479 Table 1. (continued) Patients with primary care providers (n539,343) % of patients primary care providers number in % using EHRs Selected characteristics thousands Distribution (standard error) Expected source of payment Private insurance 90, (1.8) Medicare b 24, (2.1) Medicaid 22, (1.7) Self pay, no charge, or charity 8, (2.9) Other payment source 10, (2.9) Median household income of patients neighborhood c Under $33,000 32, (2.6) $33,000 to 60,000 83, (1.8) More than $60,000 28, (2.8) Unknown 10, (3.5) a Includes HMOs, faculty practices, urgent care centers, and other office settings. b Includes patients eligible for both Medicare and Medicaid. c Contextual characteristic is the U.S. Census Bureau estimate of characteristic at the zip code level of the patients neighborhood. Unknown category for contextual characteristic represents patients with non-matching ZIP codes. *Figure does not meet standards of reliability or precision. PCP 5 primary care physician or provider EMR 5 electronic medical record EHR 5 systems analyzed include EMRs with all four of the following features: computerized prescription order entry, computerized test order entry, test results, and physician notes NA 5 not applicable Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys. of non-hispanic Black patients (22.5%) than PCPs in solo or partner practice (10.2%), group practice (7.2%), and other office settings (12.7%). To examine the extent of PCP adoption of EHRs among patient populations, a multivariate model of EHR adoption was estimated, taking into account provider and patient characteristics, as well as an interaction term for patient payment source and race or ethnicity (Table 2). The model found that overall use of EHRs by PCPs did not change between 2005 and Electronic health record adoption, however, was significantly associated with type and location of the ambulatory setting where PCPs practiced. Overall, PCPs in group practice, CHCs, other office settings, and hospital OPD clinics were each more likely to use EHRs than PCPs in private solo or partner practice, all else remaining constant. In urban areas, PCPs were more likely to use EHRs than PCPs in rural areas, all else remaining constant.

8 480 Disparities in EHR adoption? Figure 1. Percent of primary care providers using electronic health record systems by type of setting. *Figure does not meet standards of reliability or precision. 1/ Difference with other office setting is statistically significant (p,0.05). Notes: Electronic health records systems are electronic medical record systems that, at a minimum, permit electronic ordering of tests and prescriptions; and electronic viewing of test results and clinical notes. Other office setting includes health maintenance organizations, faculty practices, urgent care centers, and other office settings. Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys. Figure 2. Percent distribution of patients visiting their primary care provider by patient expected payment source, according to type of setting: United States Notes: Other office setting includes health maintenance organizations, faculty practices, urgent care centers, and other office settings. OPD is outpatient department. Uninsured patients use only self pay or charity for payment. Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys.

9 Hing and Burt 481 The model shows that PCP use of EHRs varied according to their patients demographic and socioeconomic characteristics. Uninsured (self-pay, charity, or no charge) non-hispanic Black patients were significantly less likely than privately insured non- Hispanic White patients to have PCPs who used EHRs, after controlling for the provider and other patient characteristics (adjusted odds ratio 0.12 (confidence interval [CI] 5 (0.04, 0.32)). Similarly, uninsured (self-pay, charity, or no charge) Hispanic or Latino patients were less likely than privately insured non-hispanic White patients to have PCPs who used EHRs, after controlling for provider and patient characteristics (adjusted odds ratio 0.21 (CI5(0.07, 0.65)). Finally, Hispanic or Latino patients relying on Medicaid for payment were less likely than privately insured non-hispanic White patients to have PCPs who used EHRs, after controlling for provider and patient characteristics (adjusted odds ratio 0.36 (CI5(0.18, 0.73)). These findings are illustrated more concretely in the model s predicted percentage of patients with PCPs using EHRs in (Table 2). The predicted percentage of Hispanic or Latino Medicaid patients with PCPs using EHRs was 5% compared with 14% for privately-insured non-hispanic White patients. Similarly, only 4% of uninsured Hispanic or Latino patients and 3% of uninsured non-hispanic Black patients were served by PCPs using EHRs. Although these findings are striking, the model does not specifically identify where these patients saw their PCP. As shown in Figure 4, PCPs for poor minority patients were a mixture of high and low EHR adopters. Among poor Hispanic and Latino patients, more had PCPs in solo or partner practice than in CHCs or hospital OPDs. A similar pattern was observed among PCPs for non-hispanic Black Medicaid patients. Uninsured non-hispanic Black patients, however, tended to be served by PCPs in hospital OPD more often than in solo or partner practices (although the difference was not statistically significant). In contrast to poor minority patients, PCPs for privately-insured non-hispanic White patients were likely in group practice; in addition, more privately-insured non-hispanic White patients saw their PCPs in other office settings (4.6 million) than poor non-hispanic Black and Hispanic patients combined (1.7 million). Discussion This study examined PCP adoption of EHR systems proposed by a federal initiative and the extent of EHR adoption reflected in their patient panels in The study found that in , EHR adoption by PCPs was not widespread. Overall, only one of every eight patients had PCPs who were using EHRs. The study found that EHR adoption rates varied primarily by the type of practice. Adoption was lowest among PCPs in private solo or partner practices, and was highest among PCPs in other office settings, a group that includes some of the early adopters of EHRs (HMOs and faculty practices). 2,30 31 Among PCPs working alone or in partnerships, EHR adoption (5.7%) was less than half as frequent as among PCPs in group practice, other office and hospital settings. The reasons for non-adoption of EHRs by physicians in solo or partner practices are likely the same as for all physicians (high start-up cost and technical support costs after

10 482 Disparities in EHR adoption? Table 2. ADJUSTED ODDS RATIOS AND PREDICTED PERCENTAGE ON LIKELIHOOD OF PATIENTS PRIMARY CARE PROVIDERS USING ELECTRONIC HEALTH RECORD SYSTEMS. LOGISTIC REGRESSION MODEL adjusted 95% Predicted odds ratio confidence marginal Selected characteristics (n539,343) interval percent Survey year 2005 Reference (0.77, 2.38) 14 Provider characteristics Type of setting Private solo or partner practice Reference 6 Private group practice 2.58 (1.34, 4.97) 15 Community health center 3.12 (1.12, 8.71) 17 Other office setting a 5.60 (2.26, 13.88) 26 Hospital outpatient department (OPD) 4.67 (2.19, 9.98) 23 Region Northeast 0.70 (0.31, 1.58) 9 Midwest 0.80 (0.35, 1.84) 10 South Reference 12 West 2.26 (0.99, 5.13) 22 Metropolitan Statistical Areas (MSA) status MSA 3.92 (1.36, 11.34) 14 Non-MSA Reference 4 Patient characteristics Age (year) Under 18 Reference (0.88, 2.39) and over 1.65 (0.92, 2.97) 15 Gender Male Reference 12 Female 1.21 (1.04, 1.40) 13 Race or ethnicity Non-Hispanic White Reference 13 Non-Hispanic Black 1.40 (0.82, 2.38) 15 Hispanic or Latino 0.94 (0.58, 1.51) 11 Other 0.74 (0.42, 1.31) 11 Expected source of payment Private insurance Reference 14 Medicare b 0.88 (0.59, 1.33) 12 Medicaid 0.85 (0.52, 1.37) 10 Self pay, no charge, or charity 1.17 (0.69, 2.01) 12 Other payment source 1.21 (0.77, 1.90) 15 (Continued on p. 483)

11 Hing and Burt 483 Table 2. (continued) adjusted 95% Predicted odds ratio confidence marginal Selected characteristics (n539,343) interval percent Median household income of patients neighborhood c Under $33,000 Reference 13 $33,000 to $60, (0.51, 1.46) 12 More than $60, (0.55, 2.07) 14 Unknown 1.00 (0.50, 2.00) 13 Race or ethnicity-expected source of payment interaction Non-Hispanic White, private insurance Reference 14 Non-Hispanic White, Medicare b Reference 12 Non-Hispanic White, Medicaid Reference 12 Non-Hispanic White, self pay, no charge or charity Reference 15 Non-Hispanic White, other payment source Reference 16 Non-Hispanic Black, private insurance Reference 18 Non-Hispanic Black, Medicare b 0.58 (0.26, 1.28) 10 Non-Hispanic Black, Medicaid 0.60 (0.24, 1.55) 10 Non-Hispanic Black, self pay, no charge, or charity 0.12 (0.04, 0.32) 3 Non-Hispanic Black, other payment source 0.81 (0.37, 1.77) 17 Hispanic or Latino, private insurance Reference 13 Hispanic or Latino, Medicare b 1.17 (0.65, 2.08) 13 Hispanic or Latino, Medicaid 0.36 (0.18, 0.73) 5 Hispanic or Latino, self pay, no charge or charity 0.21 (0.07, 0.65) 4 Hispanic or Latino, other payment source 0.36 (0.13, 1.02) 7 Other, private insurance Reference 11 Other, Medicare b 0.91 (0.41, 2.05) 9 Other, Medicaid 0.72 (0.24, 2.13) 7 Other, self pay, no charge or charity 1.41 (0.37, 5.34) 16 Other, other payment source 1.93 (0.77, 4.87) 21 Note: Electronic health record (EHR) systems are electronic medical records that include, at a minimum: computerized prescription order entry, computerized test order entry, test results, and clinical notes. a Includes HMOs, faculty practices, urgent care centers, and other office settings. b Includes patients eligible for both Medicare and Medicaid. c Contextual characteristic is the U.S. Census Bureau estimate of characteristic at the zip code level of the patients neighborhood. Unknown category for contextual characteristic represents patients with non-matching ZIP codes. Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys.

12 484 Disparities in EHR adoption? implementation; loss of provider productivity during implementation; misaligned cost and benefits of acquiring EHRs). 30,31 In addition, PCPs working solo or in partnership were further disadvantaged by serving a somewhat larger percentage of patients enrolled in Medicaid (14.3%) than PCPs in group practice (9.7%). Lower Medicaid physician payment rates (relative to private insurance payments) may make purchase of EHR systems unaffordable for these physicians. To our knowledge, this study is the first to examine EHR adoption from the patient s perspective. The study found that the percentage of patients with PCPs using EHRs mirrored the comparable percentage of providers in solo, partner, and group practices. There was variation, however, between percentages of PCPs using EHRs and patients with PCPs using EHRs for the remaining types of practices; this pattern may be affected by the number of patients seen in those settings. After controlling for patient and practice characteristics, the study found that EHR adoption was lower among PCPs serving Hispanic or Latino patients who were uninsured or relied on Medicaid in multivariate analysis. The study also found lower EHR adoption among PCPs for uninsured (self-pay, charity, or no charge) non-hispanic Black patients than for PCPs for privately insured non-hispanic White patients, controlling for provider and patient characteristics. These findings suggest uneven EHR adoption by PCPs of poor minority patients. Uneven EHR adoption by PCPs of poor minority patients, however, is complicated by the mixture of high and low adopting PCPs serving these patients. Overall, PCPs in solo or partner practices served more of the patients (43.6%) than PCPs in CHCs and hospital OPDs (1.8 and 7.4%, respectively). Thus, the impact of higher EHR use in CHCs and hospital OPDs for minority Medicaid and uninsured patients was offset Figure 3. Percent distribution of patients visiting their primary care provider by patient race or ethnicity, according to type of setting: United States Notes: Other office setting includes health maintenance organizations, faculty practices, urgent care centers, and other office settings. OPD is outpatient department. Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys.

13 Hing and Burt 485 by larger numbers of these kinds of patients seeing PCPs in settings less likely to use EHRs. Finally, previous studies found that among CHCs, EHR adoption was lower among those serving larger proportions of uninsured patients. 13,32 The same phenomenon could also occur among hospital OPDs, the location of PCPs for many uninsured Black patients. Lower adoption by PCPs serving poor minority patients makes the improvements of HIT-enhanced clinical care unavailable to these patients. A previous review of the literature found that EHR functions enhanced care and management of chronic disease. That is, order entry focused on specific diseases allowed longitudinal care planning (such as specialist or care manager referrals), while computerized prompts and population-based reporting and feedback (e.g., reporting back unfinished care plan elements) improved patient care outcomes. 33 To the extent that such opportunities are lost, efforts to narrow disparities in heath care are lost. If policymakers are to move toward the goal of an electronic health record for every American by 2014, additional funding may be needed to support acquisition of EHRs by small physician practices and other providers with inadequate financial resources to purchase these systems. This study has certain limitations. Analysis of EHR systems was limited to data items collected in the 2005 and 2006 NAMCS and NHAMCS induction interview forms. For example, information on connectivity (ability of clinicians to access EHR data at the point of care [interoperability]) is not well covered in the NAMCS and NHAMCS. 9 The CHC estimate of EHR adoption is also subject to reporting variability. Figure 4. Location of primary care providers for poor minority patients and privately insured White patients, Notes: Other office setting includes health maintenance organizations, faculty practices, urgent care centers, and other office settings. Sources: National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys.

14 486 Disparities in EHR adoption? Sample size limitations affected certain provider and patient estimates in this study. Since the 2006 NAMCS was the first survey year that a separate stratum of community health centers was included in the survey, the combined data lacked sufficient sample to reliably estimate both the percentage of CHC-PCPs using EHRs and their patients. At least part of this variability is associated with greater variability of estimates derived using a multiplicity estimator. 27 Conclusion This study presents nationally representative estimates of patients in multiple ambulatory care settings whose primary care providers have adopted EHRs. The finding that PCPs for minority patients who were uninsured or relying on Medicaid were less likely to adopt EHRs suggests a gap in potential benefits of this technology Effects of differential adoption on potential disparities in health care utilization should be studied further. Notes 1. Office of the National Coordinator for Health Information Technology (ONC). President s vision for health IT. Washington, DC: U.S. Department of Health and Human Services, Available at 2. Bush GW. Incentives for the use of health information technology and establishing the position of the national health information technology coordinator. Washington, DC: The White House, Available at: newsletter/2008/10october/executive%20order% pdf. 3. Bodenheimer T, Grumbach K. Electronic technology: a spark to revitalize primary care? JAMA Jul 9;290(2): Institute of Medicine, Board on Health Care Services. Key capabilities of an electronic health record system: letter report. Washington, DC: The National Academies Press, Available at: 5. Chaudhry B, Wang J, Wu S, et al. Systematic review: impacts of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med May 16;144(10):742 52, W-169 W Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: The National Academy Press, Institute of Medicine and Board on Health Sciences Policy. Unequal treatment: confronting racial and ethnic disparities in health care. Washington, DC: The National Academy Press, Chang BL, Bakken S, Brown SS, et al. Bridging the digital divide: reaching vulnerable populations. J Am Med Inform Assoc Nov Dec;11(6): Epub 2004 Aug Blumenthal D, DesRoches C, Donelan K, et al. Health information technology in the United States: the information base for progress. Princeton, NJ: Robert Wood Foundation, Jha AK, Ferris TG, Donelan K, et al. How common are electronic health records in the United States? A summary of the evidence. Health Aff (Millwood) Nov Dec; 25(6):w496 w507. Epub 2006 Oct Burt CW, Hing E, Woodwell D. Electronic medical record use by office-based phy-

15 Hing and Burt 487 sicians: United States, Hyattsville, MD: National Center for Health Statistics, Available at: electronic.htm. 12. Hing E, Burt CW, Woodwell DA. Electronic medical records use by office-based physicians and their practices: United States, Hyattsville, MD: National Center for Health Statistics, Shields AE, Shin P, Leu MG, et al. Adoption of health information technology in community health centers: results of a national survey. Health Aff (Millwood) Sep Oct;26(5): Ferris TG, Kuhlthau K, Ausiello J, et al. Are minority children the last to benefit from a new technology? Technology diffusion and inhaled corticosteriods for asthma. Med Care Jan;44(1): Zaman O, Lukins E, Cummings L. America s public hospitals and health systems, 2004 results of the Annual NAPH Hospital Characteristics Survey. Washington, DC: National Association of Public Hospitals and Health Systems, Groeneveld PW, Laufer SB, Garber AM. Technology diffusion, hospital variation, and racial disparities among elderly Medicare beneficiaries Med Care Apr;43(4): Cherry DL, Woodwell DA, Rechtsteiner EA. National ambulatory medical care survey: 2005 summary. Hyattsville, MD: National Center for Health Statistics, Middleton K, Hing E, Xu J. National hospital ambulatory medical care survey: 2005 outpatient department summary. Hyattsville, MD: National Center for Health Statistics, Cherry DL, Hing E, Woodwell DA, et al. National ambulatory medical care survey: 2006 summary. Hyattsville, MD: National Center for Health Statistics, Hing E, Hall MJ, Xu J. National hospital ambulatory medical care survey: 2006 outpatient department summary. Hyattsville, MD: National Center for Health Statistics, National Center for Health Statistics. Public use data file documentation: 2005 National Ambulatory Medical Care Survey. Hyattsville, MD: National Center for Health Statistics, Available at: National Center for Health Statistics. Public use data file documentation: 2005 National Hospital Ambulatory Medical Care Survey. Hyattsville, MD: National Center for Health Statistics, Available at: ahcd1.htm. 23. Burt CW, Hing E. Use of computerized clinical support systems in medical settings: United States, Adv Data Mar;(353): Burt CW, Sisk JE. Which physicians and practices are using electronic medical records? Health Aff (Millwood) Sep Oct;24(5): Bryant E, Shimizu I. Sample design, sampling variance, and estimation procedures for the National Ambulatory Medical Care Survey. Vital Health Stat Sep;(108): McCaig LF, McLemore T. Plan and operation of the National Hospital Ambulatory Medical Care Survey. Series 1: programs and collection procedures. Vital Health Stat Jul;(34): Burt CW, Hing E. Making patient-level estimates from medical encounter records using a multiplicity estimator. Stat Med Apr 15;26(8): Research Triangle Institute. SUDAAN user s manual, release 9.0. Research Triangle Park, NC: Research Triangle Institute, 2001.

16 488 Disparities in EHR adoption? 29. Forrest CB, Whelan EM. Primary care safety-net delivery sites in the United States: a comparison of community health centers, hospital outpatient departments, and physician offices. JAMA Oct 25;284(16): Miller RH, Sim I. Physicians use of electronic medical records: barriers and solutions. Health Aff (Millwood) Mar Apr;23(2): Loomis GA, Ries JS, Saywell RM Jr, et al. If electronic records are so great, why aren t family physicians using them? J Fam Pract Jul;51(7) Fiscella K, Geiger HJ. Health information technology and quality improvement for community health centers. Health Affairs. 2006;25(2): Dorr D, Bonner LM, Cohen AN, et al. Informatics systems to promote improved care for chronic illness: a literature review. J Am Med Inform Assoc Mar Apr; 14(2): Epub 2007 Jan 9.