The practice of medical care in the United States varies in relation to patient race and ethnicity [1]. There a multiple roots of these healthcare disparities, ranging from differences in physician and patient perceptions and communication during clinical encounters, to differences in the ways that different racial and ethnic groups relate to the institutions and systems that finance and provide care [2]. How information is presented, managed, and exchanged contributes to healthcare disparities. Therefore, the use of Electronic Medical Records (EMRs) could potentially reduce disparities.

The purpose of EMR systems is to retain, organize, and communicate medical information in a uniform manner. EMRs have the potential to make healthcare providers' decisions more consistent and objective, while ensuring that best practices are pursued [3]. EMRs provide evidence-based decision support to the healthcare provider at the point of service and can lead to timely and informed medical decision making. Such standardization has the potential to provide benefits to members of racial and ethnic minority populations that have disproportionately experienced sub-standard care.

While a growing number of studies have examined the use of EMRs to improve the efficiency of medical care delivery, surprisingly few studies have explored the impact of EMRs on reducing health disparities between racial and ethnic groups. Thus, the aim of this study was to investigate the differential impact of EMR adoption on the outcomes of care among members of minority groups. Our hypothesis was that the adoption of an EMR system by a hospital or hospital system would provide benefits to members of disadvantaged minority groups who had historically experienced lower-quality medical outcomes compared to non-Hispanic white patients.

The hospital EMR adoption rate increased from 9.4% in 2008 to 59.4% in 2013 [4]. The Healthcare Information and Management Systems Society (HIMSS) has tracked changes during the implementation of EMR systems. Using their collected information, we investigated whether the adoption of EMR systems reduced racial and ethnic disparities in health outcome measurements by comparing EMR-adopting hospitals to hospitals that have yet to implement an EMR system.

These care quality measures were calculated using linked Medicare claims data for patients receiving care in each hospital for two years before and after the adoption of an EMR system, compared to patient data from hospitals that had not adopted an EMR over the same time period. Thus, our main methodological approach was to use a difference-indifference design to examine changes in outcome disparities. Analyses were adjusted for the characteristics of patients and hospitals using data from the Medicare Beneficiary Summary File and the HIMSS data.

Poor access to care among African Americans and Hispanics is reflected in substantially lower rates of employment-based insurance coverage, lack of a regular source of medical care, and ethnic/cultural mismatches with care providers. These factors may also reduce the continuity of care among these disadvantaged populations relative to non-Hispanic whites and may diminish the engagement and trust between patient and provider, reducing the quality of medical decision-making. The Institute of Medicine found racial disparities across a wide range of disease areas, clinical services, and clinical settings, even after clinical factors, including age, disease progression, comorbid conditions, and severity of illness, were taken into account [2]. They concluded that prejudices, biases, and negative racial stereotypes may be a potential source of the disparity and proposed "evidence based guidelines" among the solutions. In addition, there are abundant research findings that racial and ethnic disparities in health care can be explained in part by minorities' disproportionate receipt of care from certain institutions where all patients, irrespective of race, tend to experience worse outcomes [5]. There is some concern that regulatory policies aimed to reduce poor outcomes may disproportionately impact minority-serving institutions, perhaps increasing racial disparities [6].

Previous studies have demonstrated disparities in both the outcomes and process of care. Compared with white patients with diabetes, African Americans with diabetes were less likely to have received the recommended processes of care metrics, including glycated hemoglobin (HbA1c) and lipid measurements [78]. Another study [9] found that compared with white patients, black and Hispanic patients experienced substantially longer wait times from the start of dialysis to being added to the kidney transplant waiting list.

Furthermore, previous health services research suggests that there are racial disparities in health outcomes. Some studies have found that black patients experienced a higher 30-day readmission rate (24.8%) compared to white patients (22.6%) according to Medicare beneficiary data [510]. Other studies found that black patients admitted with pneumonia had a longer length of stay than white patients (incidence rate ratio: 1.19). Moreover, black patients had a higher inpatient mortality rate than white patients after cardiovascular procedures and cancer resections; the odds ratios of inpatient mortality for black patients relative to white patients are 1.21 (carotid endarterectomy), 1.21 (aortic valve replacement), 1.16 (coronary artery bypass graft), 1.32 (abdominal aortic aneurysm repair), 1.08 (resection for lung cancer), 1.32 (cystectomy of the bladder), 1.57 (esophagectomy), and 1.27 (pancreatic resection).

Despite the projection that the minority population is expected to rise to 56% of the total US population, prior research on racial and ethnic disparities in healthcare has failed to identify methods of reducing these disparities [11]. This study was an expansion of the work by Lee et al. [12], which investigated the relationship between EMR adoption and health outcomes of the general population; however, they did not focus on the outcome disparities among minorities. Our study investigated the association between EMR adoption and the health outcomes of minorities, including length of stay, inpatient mortality rate, 30-day mortality rate, and 30-day readmission rate. This study was approved by the Institutional Review Board (IRB) at the University of Texas Medical Branch (IRB No. 09-054).

The study cohort included patients admitted to 425 hospitals adopting EMR systems and 283 hospitals not adopting EMR systems over the study period as indicated in Tables 1 and 2. Table 3 presents patient, disease, and hospital characteristics. A larger proportion of patients were female (59%), aged between 76–85 (43%), and white (90.7%).

White patients accounted for 90.7% of the sample. The mean of the comorbidity index was 2.9. Slightly less than half of the hospitals were teaching hospitals, while not-for-profit hospital ownership accounted for the majority (78.1%). The number of beds in each type of hospital was equally distributed. Table 3 also shows descriptive statistics for the four outcomes: the mean length of stay was 5.62 days, inpatient mortality 4.4%, 30-day mortality 13.5%, and 30-day readmission 16.3%.

Table 4 presents the difference-in-difference GLM regression results of length of stay and inpatient mortality rate. In the first column (length of stay), the variables positively associated with length of stay are black race, older age groups (65–75 and 86 and over), higher comorbidity, and greater number of beds. As seen in the second column (inpatient mortality rate), we found that patients who were black males, had a higher comorbidity rate, and belonged to an older age group (76–85 and 86 and over) had an increased likelihood of experiencing inpatient mortality. In addition, government hospital ownership and number of beds from 261–362 were other factors linked to higher mortality rates.

Table 5 presents GLM regression results of 30-day readmission and 30-day mortality after discharge. As seen in the first column (30-day readmission rate), we found that patients who were black, males, had a higher comorbidity rate, and belonged to an older age group (76–85 and 86 and over) were more likely to have a higher 30-day readmission rate. Also, hospitals of not-for-profit ownership were more likely to have a higher 30-day readmission rate. As seen in the second column (30-day readmission rate), patient characteristics associated with higher 30-day mortality rates were male, older age, and higher comorbidity. The hospital characteristic associated with higher 30-day mortality rates was not-for-profit ownership.

However, we could not find any significant effect of EMR adoption, the years after EMR adoption, the interaction terms of the EMR adopted group, after years of EMR adoption and race in all four outcome regressions.

EMR systems have been reported to support better coordinated care [3121718], thus encouraging appropriate treatment for minority patients by removing potential sources of bias from the providers [19]. Health IT systems could improve clinical care by detecting important clinical and sociodemographic risk factors for various conditions particularly relevant to minority patients. EMR systems may improve the quality of care provided to patients via increased responsiveness to care processes that are required to be more timesensitive and through improved communication [20].

While a growing number of studies have examined the association between EMRs and health outcomes, surprisingly few studies have explored the impact of EMRs on outcome disparities among minorities. Thus, to fill this gap in the current literature, this study investigated the impact of EMR system adoption on disparities in health outcomes of black patients. However, we did not observe any significant changes in health outcomes before and after EMR system implementation. Moreover, we did not find evidence that EMR system implementation was associated with outcomes of black patients.

A possible reason for the lack of any significant association between EMR and healthcare outcomes of black patients could be the short-term effect of EMR adoption. We compared the outcomes two years before and after EMR adoption. Thus, no meaningful changes in health outcomes of black patients were observed after EMR implementation, which might reflect a short-term effect of EMR implementation on patient outcomes. It has been reported that it may take several years for hospitals adopting EMR systems to fully capitalize on the clinical benefits after EMR implementation [18]. In particular, benefits from EMR on health outcomes may take longer than for process of care for minority populations. For example, Lee [21] found that greater health IT investment leads to shorter waiting times, and the waiting time reduction was greater for non-white than for white patients. He concluded that minority populations could benefit from health IT in terms of process of care [21].

Also, eliminating healthcare disparities among minority groups may remain limited, even though the evidence of healthcare disparity among minority groups is substantial [22]. Acevedo-Garcia et al. [22] argued that an effort to eliminate disparities among specific groups is currently underway but will take more time. For example, this effort has focused on training providers to offer appropriate services through the use of EMR systems and to improve coordination of care [22].

There were some limitations in this study. First, for this work, EMR was defined as computerized patient record systems (CPRS) supported by clinical decision-support (CDS) capabilities and a clinical data repository (CDR) [1314]. Thus, the definition of EMR adoption was less restraining than those used in previous studies [232425]. For example, Miller and Tucker [23] employed HIMSS data, but defined EMR adoption as four complete components of adoption including CDR, computerized physician order entry (CPOE), CDSS, and digitized physician documentation. Also, prior studies have used a more limited definition of EMR [2425] that included IT systems, such as electronic medication administration records (eMAR) and nursing documentation. This study used a more expansive definition of EMR systems because HIMSS data was incompatible with health IT systems, such as physician documents.

Second, we did not have access to in-depth data indicating the level of care regarding provider proficiency and reluctance related to the use of EMR systems in hospitals. Hence, if care providers are resistant to using an EMR system or are not IT-savvy for various reasons after EMR adoption, our data may be underestimated.

Lastly, there may have been unobserved confounding factors that might have impacted our findings. For example, organizational and management behavior may be correlated with health IT adoption. Although we controlled for structural characteristics of the hospital and patient clinical and demographic characteristics, EMR adoption behavior may vary in ways we could not observe in the data set, leading to bias in our results.