The existing literature contains scant information on the diffusion of EMRs among physicians who serve disproportionately underserved populations (Blumenthal et al. 2006
; Jha et al. 2006
;). We provide important insights into this question by exploring the NAMCS, the only public-use database that has nationally representative information on EMR adoption by physicians and the capacity to link patient records to treating physicians, enabling direct estimates of the share of underserved populations in physicians' panels. We found a negative association between the proportion of Hispanic patients treated by a physician and the likelihood of adopting EMRs with all four minimally required functionalities after controlling for other patient-panel characteristics and characteristics of physicians and their practices.
Our results demonstrate the importance of a functionality-based definition for EMR adoption. The lack of agreement regarding the definition of an EMR remains a major challenge in assessing EMR adoption in the United States (Blumenthal et al. 2006
). Evaluating the general use of EMRs has yielded a wide range of adoption rates (Jha et al. 2006
; DesRoches et al. 2008
;). Variations in the perception of what constitutes an EMR may explain a substantial part of the differences (Blumenthal et al. 2006
; Jha et al. 2006
;). To facilitate comparison across different surveys and studies, a definition based on multiple functionalities of an EMR was recommended (Blumenthal et al. 2006
). We used a definition that approximates the four core functionalities specified by an expert panel for any system to be called an EMR (Hing, Burt, and Woodwell 2007
). By distinguishing EMRs with all of the essential functionalities from those with limited functionalities, we were able to detect a negative association between the proportion of Hispanic patients treated by a physician and adoption of EMRs with “comprehensive” functionalities, but not those with limited functionalities. The logistic regression analysis of general EMR adoption did not find a statistically significant association, masking the lower EMR adoption rate among physicians treating a disproportionate share of Hispanic patients.
Previous studies that examined the associations between patient panels of racial/ethnic minorities and physicians' adoption of EMRs or similar IT technology were limited and generally inconclusive. DesRoches et al. (2008)
found insignificant associations and Grossman and Reed (2006)
found either similar or better access to health IT for the clinical activities examined among physicians treating larger proportions of minority patients. Both were physician-level studies, the same as ours. However, they relied on physician self-reported patient-panel profiles. From the patient perspective, Hing and Burt (2009)
found that uninsured black or Hispanic patients, and Hispanic patients with Medicaid coverage, were less likely than privately insured white patients to have primary care physicians using EMRs with all four minimally required functionalities. Consistent with their findings, we found a negative association between the proportion of black patients who self-paid or received charity care/no charge and adoption of EMRs with all four minimally required functionalities. Burt, Hing, and Woodwell (2006)
linked the zip code-level population characteristics to physicians who practice in that zip code and found no association between neighborhood characteristics, including the proportions of minority or Hispanic populations, and EMR adoption.
We did not find an overall significant association between EMR adoption and the proportion of Medicaid patients treated by a physician. However, compared with physicians with 25 percent or less Medicaid patients, physicians with >25–50 percent Medicaid patients were less likely to adopt EMRs with “comprehensive” functionalities. Previous studies used physician-reported Medicaid revenue as a proxy for patient panels of Medicaid recipients (Burt, Hing, and Woodwell 2006
; Grossman and Reed 2006
; DesRoches et al. 2008
;). Using the 2005 NAMCS, Burt and colleagues found a significant relationship between the percent of Medicaid revenue reported and physicians' having EMRs with all four minimally required functionalities (Burt, Hing, and Woodwell 2006
). As a sensitivity analysis, we substituted physician-reported revenues from Medicaid for patient panels of Medicaid recipients. No statistically significant association was found after we adjusted for other patient-panel characteristics and characteristics of physicians and their practices.
We did not find a statistically significant difference in EMR adoption between physicians who practice in a CHC setting and others. However, the number of physicians in a CHC setting was small (n
=171), and the resulting estimates may not be generalizable. A much larger survey, the first national survey of federally funded CHCs (n
=725) found that nationally 26 percent of CHCs reported some EMR use but only 13 percent had the minimally required EMR functionalities in 2006 (Shields et al. 2007
). These estimates are comparable with our estimates of the overall adoption rates among office-based physicians (27 percent adopted some EMRs and 11 percent had EMRs with “comprehensive” functionalities).
The major limitation of this study was the small number of patient records used to measure the proportions of underserved populations in each physician's panel. We conducted a series of sensitivity analyses with alternative definitions of the dependent and key independent variables and a different model specification. The results of these analyses give us some confidence in our estimates. However, more rigorous assessment was not feasible with the data. For instance, according to the NAMCS documentations, 210 physicians (90 in 2005 and 120 in 2006) only participated minimally (i.e., fewer than half of the expected number of patient report forms were submitted) and patient-panel characteristics calculated for these physicians are likely to be less accurate. Although excluding these physicians may increase the accuracy of our estimates, these physicians are not identifiable from public-use data. Therefore, caution should be exercised when extrapolating our findings.
Other limitations include the lack of information on physicians' demographic characteristics and practice size. Physician age or gender may impact their willingness to adopt new health technology (Fuji, Galt, and Serocca 2008
; Simon 2008
; Pagan, Pratt, and Sun 2009
;). However, a previous study found only age to be significantly associated with EMR adoption (Hing, Burt, and Woodwell 2007
), and the effect became statistically insignificant after controlling for practice size (Burt and Sisk 2005
). Practice size was the most important factor affecting the EMR adoption, with a uniformly increasing trend in the adoption rate as the number of physicians rises (Burt and Sisk 2005
; Hing, Burt, and Woodwell 2007
;). We controlled for the practice size to some extent through an indicator for solo practices and found that physicians in solo practices had a lower adoption of both EMRs with limited and with “comprehensive” functionalities. Additionally, one of the assumptions underlying the multiplicity method used in generating patient-panel characteristics is that “the characteristics of the sample visit were the same for all visits made [by the same patient] during the year” (Burt and Hing 2007
). This assumption is applicable to demographic characteristics such as race and ethnicity, but may not hold for insurance status because patients' insurance coverage may change within a year.
In summary, a higher proportion of Hispanics in a physician's panel was found to be associated with a lower likelihood of adopting EMRs with essential functionalities by the physician. To the extend that a fully functional EMR may increase efficiency and quality of health care services, this finding suggests that existing disparity in health care may have been further exacerbated. Adopting an EMR requires large up-front costs, which is the main barrier to EMR adoption (Miller and Sim 2004
) but may be particularly challenging for physicians treating disproportionately minority patients (Reschovsky and O'Malley 2008
). The recently passed stimulus package includes provision to promote EMR adoption among physicians by providing up to “U.S.$40,000 to U.S.$65,000 per eligible physician” over 5 years through Medicare or Medicaid incentive payments (Steinbrook 2009
). This may provide some relief for those who can act fast and demonstrate “meaningful use” of EMRs. However, the amount that could be received by a physician practice depends on the percentage of Medicare and Medicaid patients at the practice (Neclerio et al. 2009
). This may be less helpful among physicians treating large shares of Hispanics as the uninsurance rate among Hispanics is two to three times that of non-Hispanic whites (Doty 2003
). Apart from the initial implementation costs, to maintain a fully functional EMR requires long-term technical support and maintenance costs, which may remain a challenge among physicians in high-minority practices after implementation, as majority of minority patients (43 percent) were treated by physicians in solo or partner practices (Hing and Burt 2009
). Additional financial support or partnership with large hospital systems for continuing support may be developed (Dolan 2009