Recognition of the importance of health information technology (IT) developments, such as Electronic Medical Records (EMRs) and computerized practitioner order entry (CPOE), is increasing, and there is higher investment in health IT than ever before [1]. The United States federal government has made increasing investments in health IT a central part of its healthcare reform initiative to decrease health care costs. In 2009, the Health Information Technology for Economic and Clinical Health (HITECH) Act was signed as a part of the American Recovery and Reinvestment Act to promote the adoption and meaningful use of health IT [2]. The HITECH Act allocated almost US$20 billion to subsidize the adoption of health IT and could potentially result in an IT subsidy of US$2-10 million per hospital [2]. It has already been used to support health IT adoption for eligible providers since 2011.

To deliver healthcare to patients, hospitals require many resources. The complex set of administrative activities and patient treatment processes in the hospital setting makes procedures complicated. Thus, it is hard to track all patient records seamlessly. The complexity of the many processes that occur around a patient increases the likelihood of ordering duplicate or redundant tests, resulting in longer waiting times for providers and patients as well as higher cost. However, health IT may solve such problems by making patient information available where it is needed, supporting better health care decision making and better coordinated care, increasing processing speed, and reducing the likelihood of redundant testing [3].

The benefits of health IT adoption have been demonstrated by many studies [4-8]. Health IT may reduce hospital cost in many ways, through reduction of hospitals stays, redundant tests, medical errors, and administrative expenses. These studies have found a negative relationship between health IT adoption and cost. However, most previous studies on health IT adoption have been based on single leading institutions. This implies that, even if they have correctly measured the effects of health IT, the results are limited to the leading institutions that were involved in the studies. Thus, it is hard to generalize the results to all hospitals. Recently, researchers have examined the relationship between IT adoption and hospital performance using large samples of hospitals [9-20]. They found that health IT adoption could lead to significant benefits for hospitals. However, the results of these studies were inconsistent, which may result from their use of different data sets and different IT applications in each study.

Moreover, most of the previous studies have employed discrete values (0 or 1) of EMR or CPOE adoption as health IT measures [4-8,10-12,14,15,19]. While focusing on these IT measures may clarify the effects of specific IT practices, doing so ignores the fact that hospitals use many different types of IT inputs with different units having different impacts on output. Therefore, to measure the effect of health IT, there needs to be an aggregation across IT inputs [21]. Unlike previous studies focusing on specific IT applications, such as CPOE or EMR, we measure IT capital and IT labor as the dollar amount given on a hospital's balance sheet. This measure captures the overall spending on IT, not a specific application. Moreover, we considered data at the level of individual patients. Using this data, we could also examine the relationship between IT expenditure and cost across patient and disease characteristics. Findings on the effects of health IT expenditure on individual patient-level cost may help guide the development of policies and interventions to ensure the use of health IT. To this end, this study investigates the effect of health IT expenditure on cost at the individual patient level by using California Office of Statewide Health Planning and Development (OSHPD) data obtained from 2000 to 2007 [22]. A traditional cost function was used with hospital fixed effect by introducing health IT explicitly into the cost function and clustered error.

Table 1 shows the characteristics of patient, payer source, DRG weight, and IT capital expenditure. Forty-three percent of patients belonged to the 35-64 age group, 54% were female, almost 60% were white, and 36% had medicare as a payer source. The average DRG weight was 1.127. The average patient cost was US$8,466 per stay. The average IT capital per bed was US$20,300, the average IT labor per bed was US$5,964, and sum of IT capital per bed and IT labor per bed was US$26,264.

Table 2 presents the regression results. For the analysis, we applied hospital fixed effect with clustered error. To represent the possible non-linear relationship between patient-level cost and IT expenditure, a quadratic cost function was employed to fit the hospital cost function by adding linear and quadratic terms of IT capital per bed and labor per bed in Equation (3). The regression results in Table 2 show that the quadratic function of IT capital per bed and labor per bed adequately explained changes in patient cost. IT capital per bed and IT labor per bed were associated with cost in Model 1 in Table 2. This suggests a conventional U-shaped average cost curve. Figure 1 clearly shows the quadratic relationship between patient-level cost and IT expenditure. The dashed line of Figure 1 shows the quadratic relationship between patient-level cost and IT labor per bed. The average cost falls until the IT labor per bed of US$1,550 (log of 7.34) is reached, and it rise thereafter. The thin line in Figure 1 represents the quadratic relationship between patient-level cost and IT capital per bed. The average cost decreases until the IT capital per bed of US$27,909 (log of 10.239) is reached, and it rises thereafter. However, hospital's IT capital and IT labor expenditure account for a small percentage of revenue [1]. Thus, to capture the whole effect of IT expenditure, we summed these two values to obtain the total IT expenditure. Model 2 in Table 2 shows that all IT expenditure was significantly associated with cost. The thick line of Figure 1 also shows that patient-level cost decreased as all IT expenditure per bed increase up to US$28,695 (log of 10.264), and it increased thereafter.

We found similar results in age, sex, race, payer source, and DRG weight for Models 1 and 2. Older patients (65 and over) had the lower costs than any other age groups. Females and Asian had higher costs than their counterparts. Patients with private insurance had lower costs than those with other insurance. As expected, higher DRG weight was associated with higher cost.

Next, we investigated the effect of health IT expenditure on costs across DRG types because different diseases may be differently associated with health IT. We classified all DRGs into medical or surgical diseases based on the DRG Expert category [29]. Results show that surgical diseases account for 17.8% of all DRGs, while medical diseases comprise the remainder. Table 3 showed that all IT expenditure was associated with both medical and surgical diseases. While the average cost for medical diseases is lower than that for surgical diseases, the cost slope is steeper in medical diseases than in surgical diseases (-0.076 vs. -0.059) in the falling cost part in Figure 2.

Moreover, we found slightly different results in relation to other independent variables in surgical and medical diseases. Regarding surgical disease, older patients (65 and over) had higher costs than any other age groups. Male and black patients had higher costs than their counterparts. Regarding medical disease, people in the age range of 35 to 64 had higher costs than the youngest age group (1-17). Unlike surgical disease, female patients had higher costs than males in medical disease. Asians also had higher costs than other race/ethnic groups. In both surgical and medical diseases, patients with medical insurance had higher costs than those with other insurance, while patients with self and private insurance had the lowest costs. Also, higher DRG weight was associated with increased cost.

This study examined the effect of health IT expenditure on patient-level cost by analyzing OSHPD data obtained from 2000 to 2007. Unlike previous studies [4-8,10-12,14,15,19], we used a dollar measure of IT capital and labor expenditure. Also, we employed hospital fixed effects regression with clustered errors within hospitals and found the quadratic relationship between health IT expenditure and patient-level cost. The quadratic function in IT expenditure predicted a decrease in cost up to US$1,550 of IT labor per bed, US$27,909 of IT capital per bed, and US$28,695 of all IT expenditure per bed. This quadratic relationship is consistent with the marginal effect theory in economics. It implies that as the hospitals invest more in IT systems, their cost savings will increase. However, after a certain point, as they increase IT investment, their cost reduction will decrease. That is, further cost reduction will be limited without expanding general capital. According to Rosenberg [30], capital statement is an important factor in the diffusion of a technology because it plays a crucial role in the successful implementation of a new technology. Also, one quadratic relationship between IT expenditure and patient-level cost could be lack of enough money for hospitals to invest in health IT. Overall health IT expenditure accounted for only 5.3% of the total revenue in California hospitals [1]. Also, less than 8% of US hospitals have adopted basic EMR systems [31]. This lag in IT investment means that there may not be a significant reduction in cost. Conversely, the widespread of IT adoption may generate large cost savings [1].

Our findings are generally consistent with those of other studies [9,18,20,31] that found decreased costs due to health IT adoption. Some studies have examined the impact of IT on hospitals-level cost. Borzekowski [9] examined the relationship between health IT use and hospital operating costs and found that both financial/administrative and clinical IT systems at most automated hospitals were associated with lower costs. Furukawa et al. [18] also found that EMR adoption reduced licensed vocational nurse cost per hour by 2%-4%. Other studies examined patient cost reduction after IT adoption. Amarasingham et al. [20] measured IT adoption more flexibly as the level of automation based on physicians' daily interaction with the IT system and found that hospitals with automated IT systems had lower costs. Also, Evans et al. [32] found that the use of a database linked to a decision-support program and computer-based patient records significantly reduced total per-patient cost.

Moreover, we found different relationships between IT expenditure and patient cost in relation to different types of disease. We categorized DRGs into surgical and medical diseases and found that IT expenditure reduced cost more significantly in relation to medical diseases than surgical diseases. Medical disease is less complicated than surgical disease. Thus, health IT may capture the cost reduction more easily than for surgical disease.

There were some limitations in this study. First, our health IT expenditure measure may be heterogeneous because it includes all the IT applications including clinical and administrative ITs. Different IT systems may affect hospital cost differently. Because patient cost is aggregated from complex processes, some processes may not be affected by a clinical IT system, such as EMR or CPOE. However, cost management may be influenced by administrative IT systems because they can keep track patient record and improve charge capture. Second, we only used California hospital data. Thus, it may not be possible to generalize our results to other states in which the pattern of IT expenditure is affected by other factors or expenditures may occur at different rates. Third, the returns to IT expenditure may have occurred in ways that we could not observe in the data, such as increased quality or efficiency which is difficult to measure in cost. Fourth, health IT expenditure behavior may differ in some factors that we could not observe in our data. Thus, our estimates may remain biased. Lastly, we used the hospital's cost-to-charge ratio to calculate the cost of hospitals; however, the cost-to-charge ratio can vary widely within the same institutions for different services, and the conversion of cost multiplied by the cost-to-charge ratio is only a general approximation of real cost in hospitals.