The objective of this study was to investigate the relationship between the level of Electronic Medical Record (EMR) system adoption and healthcare information technology (IT) infrastructure.
Both survey and various healthcare administrative datasets in Korea were used. The survey was conducted during the period from June 13 to September 25, 2017. The chief information officers of hospitals were respondents. Among them, 257 general hospitals and 273 small hospitals were analyzed. A logistic regression analysis was conducted using the SAS program.
The odds of having full EMR systems in general hospitals statistically significantly increased as the number of IT department staff members increased (odds ratio [OR] = 1.058, confidence interval [CI], 1.003–1.115;
This study verified that full EMR adoption was closely associated with IT infrastructure, such as organizational structure, human resources, and various IT subsystems. This finding suggests that political support related to these areas is indeed necessary for the fast dispersion of EMR systems into the healthcare industry.
Many healthcare organizations (HCOs) have been adopting Electronic Medical Record (EMR) systems worldwide. It is known that more than 85% of hospitals have adopted EMR systems in both the United States and European Union countries [
One previous study empirically described levels of EMR adoption as ‘no EMR system’, ‘minimal’, ‘basic’, and ‘fully functional EMR system’ based on frequently used functionalities. This study found that the breast examination rates of providers increase as the EMR adoption levels increase from no or minimal EMR systems to full EMR systems [
Many studies have considered the factors affecting EMR system adoption. For example, in one study, internal features, such as IT infrastructure, location of hospitals, organic organizational culture, and environmental factors influencing competition were associated with EMR adoption [
For the rest of hospital covariates, this study suggests that full EMR adoption is closely associated with healthcare IT infrastructure. IT infrastructure generally refers to IT components, such as computer hardware, software, etc.; human and technical IT capabilities, such as knowledge, skills, etc.; and shared IT services, such as networks, data management, communication technology, etc. [
Theoretically, structural contingency theory may fit well in this situation: H1 & H2. There are many perspectives on contingency theory. However, the main explanation of the theory is that organizations are affected by internal and external factors, but there is no best way to achieve better performance or organizational effectiveness or to organize a corporation [
Regarding H1, we argue that EMR systems, generally speaking, bring many advantages of better performance in HCOs, such as quality of care, managerial efficiency, and electronic healthcare information exchange. Employees working in IT areas of HCOs see and experience various improvements related to EMR systems in the healthcare industry. EMR system adoption is widespread in HCOs. Thus, they would be likely to express their opinions to top-level managers or decision-makers of hospitals suggesting the advantages of introducing full EMR systems. Those voices would increase as the number of those staff members increases. In contrast, when there are few IT employees, there would be a low probability of full EMR adoption because reports or feedback to top-level managers would be weaker. Similarly, if there are IT departments in HCOs, then their influence would be greater in comparison to HCO that do not have IT departments. Thus, these kinds of internal pressure would affect the level of full EMR adoption.
According to a previous study, hospitals with partial EMR adoption had greater difficulties in recruiting ICT staff members than hospitals with full EMR adoption [
Regarding H2, EMR systems generally have a dominant role that links various internal and external IT systems. Thus, hospitals with high levels of IT subsystems are more likely to install full EMR systems because those hospitals could easily make their various systems connect with EMR systems. They would also have lower marginal costs and high marginal utility by investing financial resources in EMR systems. However, hospitals with lower levels of subsystems do not have these motivations because of low marginal utility or efficiency. Thus, the level of EMR system adoption would increase when the level of IT infrastructure increases.
Several empirical studies support this argument. One previous study showed that there was a direct association between IT infrastructure and EMR adoption [
Thus, the purpose of this study was to investigate the relationship between full EMR system adoption and healthcare IT infrastructure, which was measured by considering the number of IT staff members (general hospitals), the establishment of an IT department (hospitals), and various IT systems in both HCOs. Findings from this study would contribute to areas related to EMR system adoption, such as policy making, EMR dispersion support, monitoring of EMR system sophistication, and academic EMR adoption studies.
This study had a cross-sectional design, and the unit of analysis was HCOs, or hospitals. In Korea, the medical law categorizes hospitals into five types, namely, general hospitals, small hospitals, dental hospitals, oriental-medicine hospitals, and long-term care hospitals. According to the law, a ‘general hospital’ is a medical accommodation facility with 100 or more beds and at least 7 medical specialty departments, whereas a ‘small hospital’ is a medical facility accommodating 30 or more beds. This study only considered these two types of hospitals as the main study subject for the generalization of study findings. According to previous studies, the size of hospitals, teaching status, location, multi-hospital systems, and affiliation were related to EMR system adoption [
Finally, regarding the ethical issue of human study subjects, approval of the Institutional Review Board was not obtained because the study did not directly consider human study subjects, rather it focused on HCOs.
This study used a nationwide healthcare IT survey and various healthcare administrative data obtained from the Korea Health Industry Development Institute (KHIDI). The survey was conducted by the KHIDI and the Health Insurance Review and Assessment Service (HIRA) to assess the current IT status and to support healthcare IT of the healthcare industry. The survey was conducted from June 13 to September 25, 2017. Based on the population of the study hospitals, the KHIDI and HIRA randomly selected a group of hospitals based on their prescheduled methodological guidelines. The survey tool was a structured questionnaire. A total of 275 general hospitals (response rate [RR] = 83.1%) and 298 small hospitals (RR = 32.7%) participated in the survey. The data obtained by this survey were merged with KHIDI's health-related administrative data. During this process, this study excluded 18 and 25 study subjects of general hospitals and small hospitals, respectively, due to missing values regarding the number of beds, number of doctors, IT staff members, and the Herfindahl–Hirschman Index (HHI). Thus, the final results obtained from 257 general hospitals and 273 small hospitals were analyzed.
This study had one outcome variable, namely, level of EMR system adoption: full versus partial EMR system adoption. This study descriptively defined it as the degree of digitalization of patients' demographic and clinical information. HCOs were simply considered as having full EMR systems when they were storing and pulling patients clinical information electronically without using paper-based charts and as having partial EMR systems when they were using both electronic medical charts and paper-based charts. This scale of measurement is almost the same as those used in previous studies [
Regarding major independent variables, this study descriptively defined IT infrastructure as computer hardware, software, human resources, and shared knowledge related with healthcare IT resources following the general definition used in previous studies [
The location of HCOs was measured as Seoul and mega metro cities or others. If the population of the local administrative district area was greater than one million including Seoul, then they were considered Seoul and mega metro cities; otherwise, they were considered as belonging to the other group. Regarding the type of foundation, if HCOs were for-profit organizations, such as private foundations, corporate foundations, medical foundations, or privately owned hospitals, then they were considered private; otherwise, they were considered public. The number of beds was measured according to the number of operating beds, and the number of physicians was the number of full-time equivalent physicians. The HHI was calculated based on the sum of the squares of the total running beds of the hospitals within each local area; thus, it was the sum of the squares of the bed portion of the specific hospitals in a local area.
This study first looked at the descriptive statistics targeting the main independent variables and an outcome variable showing full EMR system adoption. For the main statistical analysis, which is the association of the main dependent variable, level of EMR system adoption, with two independent variables, a logistic regression analysis was conducted using the logistic procedure of the SAS program version 9.4 (SAS Institute Inc., Cary, NC, USA).
There has been little study investigating factors related with level of EMR system adoption. This study investigated the relationship between level of EMR system adoption and the internal features of general hospitals and smaller hospitals based on the prediction of the structural contingency theory. Level of EMR system adoption was measured in terms of full EMR system or partial EMR system adoption. In the former case, patients' clinical data is electronically stored and pulled out when it is needed. In the latter case, patients' clinical data are electronically stored or kept in paper-based medical charts.
This study proposed two hypotheses. First, the number of IT staff members and the existence of an IT department is related to the level of EMR systems adoption. Second, healthcare IT subsystems are positively associated with level of EMR system adoption. The two hypotheses were statistically significantly supported except the relationship between full EMR system adoption and IT subsystems in general hospitals. Although this relationship in general hospitals was not statistically supported, the study results show the same relationship direction as the proposed hypotheses, which was positive association.
More specifically, the number of IT department staff members and the existence of IT departments were significantly associated with full EMR system adoption regarding H1. The study results show that the number of IT staff members and the existence of IT departments are equally associated with full EMR system adoption, although there are different regulating guidelines, such as the number of beds and the number of medical specialty departments according to Korean medical law. The results of this study show agreement with the results of previous international studies [
Another study result regarding H2, in contrast, was somewhat surprising. Contrary to our expectation, the full EMR adoption of larger hospitals, such as general hospitals, was not related with IT subsystems, but the level of EMR system adoption was statistically significantly associated with IT subsystems in small hospitals. This means that IT infrastructure has a critical role in full EMR system adoption in small hospitals. This finding also agrees with the results of a previous study [
Regarding the other hospital covariates other than the two main independent variables, this study found that full adoption rates of EMR system in general hospitals and small hospitals were 70.0% (180/257) and 59.7% (163/273), respectively. The location of small hospitals was not associated with full EMR adoption contrary to our expectations and the results of previous studies [
Although this study produced several meaningful findings, there were some limitations. First, a simple measure of EMR system adoption, such as full or partial EMR system adoption, would be weak from a methodological point of view because the current measure, by itself, might not fully detect the variation coming from more sophisticated EMR functionalities. Future studies are necessary to incorporate various healthcare IT standards, functionalities, and usability into full EMR system adoption measures [
In conclusion, full EMR system adoption has crucial relationships with IT staff members, IT departments, and IT subsystems of general hospitals and smaller hospitals. Prediction based on organizational theory well explained the relationship and supported our two hypotheses. Widespread dispersion of sophisticated EMR systems is important because there is a high probability that sophisticated EMR systems are related to the provision of high quality of care. These days, most HCOs are adopting EMR systems. We expect that our study results will provide some research insights to those who are interested in levels of EMR system adoption.
Values are presented as mean ± standard deviation (max–min). EMR: Electronic Medical Record, IT: information technology.
aincludes tertiary hospitals.
EMR: Electronic Medical Record, IT: information technology, OR: odds ratio, CI: confidence interval.
EMR: Electronic Medical Record, IT: information technology, OR: odds ratio, CI: confidence interval.