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Journal of Medical Systems

, 37:9907 | Cite as

Health Information Technology Adoption in U.S. Acute Care Hospitals

  • Ning Jackie Zhang
  • Binyam Seblega
  • Thomas Wan
  • Lynn Unruh
  • Abiy Agiro
  • Li Miao
Original Paper

Abstract

Previous studies show that the healthcare industry lags behind many other economic sectors in the adoption of information technology. The purpose of this study is to understand differences in structural characteristics between providers that do and that do not adopt Health Information Technology (HIT) applications. Publicly available secondary data were used from three sources: American Hospital Association (AHA) annual survey, Healthcare Information and Management Systems Society (HIMSS) analytics annual survey, and Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) databases. Fifty-two information technologies were grouped into three clusters: clinical, administrative, and strategic decision making ITs. Negative binomial regression was applied with adoption of technology as the dependent variables and eight organizational and contextual factors as the independent variables. Hospitals adopt a relatively larger proportion of administrative information technology as compared to clinical and strategic IT. Large size, urban location and HMO penetration were found to be the most influential hospital characteristics that positively affect information technology adoption. There are still considerable variations in the adoption of information technology across hospitals and in the type of technology adopted. Organizational factors appear to be more influential than market factors when it comes to information technology adoption. The future research may examine whether the Electronic Health Record (EHR) Incentive Program in 2011 would increase the information technology uses in hospitals as it provides financial incentives for HER adoptions and uses among providers.

Keywords

Health information technology Healthcare systems Innovation adoption Clinical decision support systems Strategic information systems Chronic disease hospitals 

Introduction

Several problems with the U.S. healthcare system were identified in previous studies. Although the U.S. healthcare system is the largest in the world, standing at $2.2 trillion or about 17.3 % of the total GDP in 2009 and projected to increase to $4.5 trillion or about 19.3 % of the GDP by 2019 [1], the system remains expensive, unsafe, and inefficient as compared to other developed countries [2, 3, 4]. For instance, medical errors are estimated to be chief causes of death anywhere between 44,000 and 98,000 patients every year [5], while adverse drug events injure or kill an estimated 770,000 people annually [6]. Consumer surveys also revealed that a significant portion of the public is dissatisfied with the safety and quality of care they received at US hospitals [7]. Moreover, the ever increasing complexity in healthcare services is accompanied by a substantial increase in costs [8]. Taylor and colleagues [9] predicted that the recent trend of aging population and healthcare cost inflation will ultimately produce unsustainable federal deficits.

Policy makers, healthcare providers, and consumer groups as well as concerned organizations such as Institute of Medicine (IOM), Agency for Healthcare Research and Quality (AHRQ), and The Leapfrog Group have advocated that the adoption of healthcare information technology (HIT). HIT could play a key role in addressing the growing crisis in the healthcare industry [10]. The adoption of one or more HIT applications is shown to improve healthcare provision through: reducing errors of omission [11]; diminishing number of adverse drug effects [6, 12]; decreasing number of prescription errors [13]; promoting efficient physician time spent with patients [14]; increasing nurse time on direct patient care [15]; providing better surveillance [16]; encouraging adherence to regimented and evidence-based guidelines [17]; reducing inpatient days [18]; enhancing integrated data review [19]; and positively affecting medication and non-medication quality of care measures [20].

From the vantage point of cost, Hillestad et al. [4] estimated that a 90 % national adoption rate of Electronic Medical Record (EMR) in hospitals could cost up to $98 billion, while the efficiency savings from patient care could potentially top more than $77 billion per annum. A national study on hospital level estimates found that clinical IT applications were estimated to bring an average of $0.5 to $3 million cost saving per hospital annually [21].

In spite of the aforementioned evidence supporting the benefits of HIT applications, the adoption of IT systems in the healthcare industry has been modest in comparison with other industries [4]. Instead of focusing on clinical IT systems, the healthcare industry has primarily focused on acquiring technological applications that are related to administration and financial transactions [3]. Consequently, a considerable amount of patient records are still kept on paper. At best, only 20–25 % of hospitals in the United States are estimated to keep medical records electronically [4]. Poon and colleagues (2006) estimated the proportion of general physicians using electronic record systems in the United States to be 17 %, in contrast to 88 % in the Netherlands [22].

Despite such low levels of adoption, the current trend is indicating an increased recognition by various stakeholders on the benefits of healthcare information systems. In fact, HIT adoption is noted as one of the relatively few areas in the current healthcare debate where a general agreement exists among the diverse groups of healthcare providers, consumers, and policy makers [3]. Cognizant of the aforementioned arguments, the purpose of this paper is to identify organizational and contextual determinants of HIT adoption in US acute care hospitals.

Methods

On the bases of Austin and Boxerman’s taxonomy [23], this study groups HIT applications into three clusters. Clinical IT, administrative IT, and strategic decision-support IT make up the clusters. Clinical IT refers to technologies that are directly associated with patient diagnosis, treatment, and evaluation of outcomes [23]. A comprehensive list of the technologies is depicted in Table 1. The core purpose of these technologies is to improve patient care. Administrative IT applications are not directly related to patient care activities. Instead, they are used in the human resource departments and include “financial information systems, payroll, purchasing and inventory control, outpatient clinic scheduling, office automation, and many other” [23] support functions (p. 5). Similar to administrative IT applications, strategic IT applications are not directly related to patient care. They are primarily used by management teams in the hospitals to make strategic-planning and revenue-generating decisions as well as monitoring and performance evaluations. A list of 52 IT applications was selected for analysis [24, 25]. The list included; 25 clinical, 18 administrative, and 9 strategic IT applications.
Table 1

Health Information Technology (HIT) applications by technology clusters

Clinical IT

Administrative IT

Strategic IT

1. Abstracting

1. Accounts payable

1. Budgeting

2. ADM

2. ADT/Registration

2. Case mix management

3. Ambulatory EMR

3. Benefits administration

3. Contract management

4. Ambulatory PACS

4. Browser

4. Cost accounting

5. BCMA

5. Credit/collections

5. Data warehousing/mining–financial

6. BCMD

6. DBMS

6. Enterprise resource planning

7. Cardiology information system

7. Eligibility

7. Executive information system

8. Chart deficiency

8. Email

8. Nurse staffing/scheduling

9. Chart tracking/locator

9. Encoder

9. Outcomes and quality management

10. Clinical data repository

10. Enterprise master person index (EMPI)

11. Clinical decision support

11. General ledger

12. Computerized physician order entry (CPOE)

12. Materials management

13. Electronic medication administration record (EMAR)

13. Patient billing

14. In-house transcription

14. Patient scheduling

15. Laboratory information system

15. Payroll

16. Nursing documentation

16. Personnel management

17. Operating room (surgery)–peri-operative

17. RFID–supply tracking

18. Operating room (surgery)–post-operative

18. Time and attendance

19. Operating room (surgery)–pre-operative

20. OR scheduling

21. Order entry (includes order communications)

22. Pharmacy management system

23. Radiology information system

24. ROBOT

25. Telemedicine–radiology

(Adapted from Burke & Menachemi, 2004 [24]; Used With Permission)

Data were assembled from several sources. Information on the type of technologies and the level of adoption is obtained from the Healthcare Information and Management Systems Society (HIMSS) analytics annual survey of 2006 (N = 5,082 hospitals). The year 2006 was selected as it was the latest year with the most complete information available from all three data sources. American Hospital Association (AHA) annual survey (N = 6,346 hospitals) provided organizational factors (size, ownership, and HMO penetration) and contextual factors (market competition, and payer mix) for the study. Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) data set (N = 1,045 hospitals) was used to identify information on the three size categories; teaching status, urban/rural, and regional location of hospitals.

The unit of analysis is individual acute care hospitals. The data sets were merged through the Medicare Identification Number and other common variables such as hospital name, street address, city, and zip code. Hospitals with missing or duplicate values for any of the common variables were excluded from the analysis. In addition, some states (GA, IN, KS, MI, NE, OH, OK, SC, SD, TN, and TX) prohibit the release of information that could identify hospitals on the HCUP NIS data set. Therefore, hospitals from these states were excluded from the analysis. Eventually, 586 hospitals were analyzed to identify factors that affect the adoption of HIT.

The explanatory variables used in this study were categorized into two groups: (1) organizational factors (size, ownership, teaching status, and HMO penetration); and (2) contextual factors (urban/rural location, regional location, market competition, and payer mix). The operational definitions of the variables are provided in Table 2. Both the predictor and response variables had observations with missing values which were subsequently excluded from analysis. Ultimately, this study aimed to identify the organizational and contextual factors that predicted the adoption of HIT as measured by the number of technologies adopted. Since the response variable was count data, the prediction of the adoption of HIT in the three clusters was achieved by developing negative binomial regression models represented by the following equation:
Table 2

Operational definitions of response and predictor variables

Variable

Description

Attributes

Data source

Response variables

 HIT adoption

The number of ‘Live and Operational’ technologies in the hospital; measured in terms of clinical, administrative, and strategic decision-support IT.

Numerical:

HIMSS

 • 0 to 25 for clinical IT

 • 0 to 18 for administrative IT

 • 0 to 9 for strategic IT

Predictor variables

Organizational

 Size

The number of staffed and setup beds in the hospital.

Continuous (regression analysis)

AHA

Categorical (descriptive analysis):

HCUP

1 = Small

2 = Medium

3 = Large

 Ownership

Ownership status of hospitals.

Dichotomous:

AHA

0 = Not-for-profit

1 = For-profit

 Teaching status

Teaching status of hospitals.

Dichotomous:

AHA

0 = Non-teaching hospital

1 = Teaching hospital

 HMO penetration

The existence of a contract with an HMO.

Dichotomous:

AHA

0 = No HMO contract

1 = HMO contract

Contextual

 Urban/rural location

Urban vs. rural location of hospitals.

Dichotomous:

AHA

0 = Rural location; and

1 = Urban location.

 Region

Geographic locations of hospitals.

Categorical:

HCUP

1 = Northeast (CT, MA, ME, NH, NJ, NY, PA, RI, and VT);

2 = Midwest (IA, IL, IN, KS, MI, MN, MO, ND, NE, OH, SD, and WI)

3 = South (AL, AR, DE, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA, and WV)

4 = West (AZ, CA, CO, ID, MT, NM, NV, OR, UT, WA, and WY)

 Market competition

Herfindahl-Hirschman Index (HHI) of market concentration, small HHI indicates more market competition.

Numerical

AHA

 Payer mix

The ratio of Medicare and Medicaid patients to total hospital patients.

Numerical

AHA

$$ \mathrm{Adoption}\;\mathrm{of}\;\mathrm{IT}=\mathrm{f}\left( {\mathrm{size},\;\mathrm{ownership},\;\mathrm{teaching}\;\mathrm{status},\;\mathrm{HMO},\;\mathrm{urban}\;\mathrm{location},\;\mathrm{region},\;\mathrm{market}\;\mathrm{competition},\ \mathrm{payer}\;\mathrm{mix}} \right) $$

Results

Results of descriptive statistics

Table 3 shows the results of the descriptive statistics for the final data set (N = 586). Accordingly, the minimum number of set up and staffed beds in the data set was 6 while the maximum was 1,834, with mean and median values of 179.61 and 116.50 beds respectively. Market competition (HHI) varied among the hospitals from 1 (where the only hospital beds in the county exist in that specific hospital, indicating a complete absence of competition) to close to zero (indicating that a large number of hospitals compete within the same market area). The average HHI was 0.33 while the median was 0.06. Two hospitals in the data set reported zero payer mix (i.e., they did not treat any Medicare or Medicaid patients in that specific year), while the maximum, the mean, and median values were 0.99, 0.69, and 0.70 respectively.
Table 3

Descriptive statistics of acute care hospitals

Variable

N

Mean (or %)

Median

SD

Health information technology

 Clinical IT

586

10.87

12.00

6.33

 Administrative IT

586

10.44

12.00

5.22

 Strategic decision-support IT

586

4.22

5.00

2.83

Organizational factors

 Size

586

179.61

116.50

196.68

 Small

242

41.3 %

_

_

 Medium

149

25.4 %

_

_

 Large

195

33.3 %

_

_

 Ownership

 Not-for-profit

516

88.0 %

_

_

 For-profit

70

12.0 %

_

_

 Teaching status

 Non-teaching hospital

452

77.1 %

_

_

 Teaching hospital

134

22.9 %

_

_

 HMO penetration

 Without HMO contract

168

28.6 %

_

_

 With HMO contract

418

71.3 %

_

_

Contextual factors

 Urban/rural location

 Rural hospitals

227

38.7 %

_

_

 Urban hospitals

359

61.3 %

_

_

 Region

 Northeast

120

20.5 %

_

_

 Midwest

150

25.6 %

_

_

 South

152

25.9 %

_

_

 West

164

28.0 %

_

_

 HHI

586

0.33

0.06

0.42

 Payer mix

586

0.69

0.70

0.14

Out of the 586 hospitals included in the data set, 89 hospitals did not report having “Live and Operational” technologies under any of the clinical, administrative, or strategic categories. The maximum numbers reported were 23 out of 25 for clinical technologies, 16 out of 18 for administrative technologies, and 9 out of 9 for administrative technologies. The mean and median values were 10.87 and 12 for clinical IT, 10.44 and 12 for administrative IT, and 4.22 and 5 for strategic IT. In general, the findings indicated that hospitals adopt a relatively larger proportion of administrative information technology as compared to clinical and strategic IT.

Based on the number of beds, location, and teaching status as criteria; hospitals were divided into three groups: small, medium, and large. In the final data set, a large proportion of the hospitals (N = 242 or 41.30 %) were small, while only 149 hospitals (25.42 %) were medium, and 195 hospitals (33.28 %) were large in size. A significant proportion of the hospitals (N = 516 or 88.05 %) were not-for-profit while only 70 hospitals (11.95 %) were for-profit. Similarly, a very large proportion of the hospitals (N = 452 or 77.13 %) were non-teaching while 134 hospitals (22.87 %) were teaching. In terms of an HMO contract, 168 hospitals (28.67 %) did not have a contract while 418 hospitals (71.33 %) did have a contract. Provider location largely favored urban designations as 359 hospitals (61.26 %) were located in urban areas while 227 hospitals (38.74 %) were in rural areas. The hospitals were fairly equally distributed among the four regions as defined by US Census Bureau: 120 hospitals (20.48 %) were located in the Northeast, 150 hospitals (25.60 %) were from the Midwest, 152 hospitals (25.93 %) were from the South, and 164 hospitals (27.99 %) were from the West.

The relationship between the hospitals’ adoption of the three HIT groups and the predictor variables is depicted in Fig. 1. The vertical bars represent the average number of the three technology groups adopted by the hospitals.
Fig. 1

Clinical, administrative, and strategic IT applications adopted by the hospitals (mean)

Results of regression analyses

Table 4 shows the results of the negative binomial regression analysis. The adoption of Clinical IT in acute care hospitals was positively affected by size (p < .001), urban location (p < .001), for-profit ownership type (p < .01), and HMO penetration (p < .05). The adoption of Clinical IT was negatively affected by payer mix (p < .05). In addition, compared to a Northeast location, being located in the West had had a negative effect on the adoption of clinical IT (p < .001), while the difference from the other two regions was not statistically significant. Moreover, teaching status and market competition were not significantly associated with the adoption of clinical IT in acute care hospitals. Table 4 also reveals factors affecting adoption of administrative IT in the hospitals. Similar to clinical IT, the adoption of administrative IT was positively affected by size (p < .001), urban location (p < .01), and HMO penetration (p < .05). Adoption of administrative IT was negatively affected by payer mix (p < .05). Hospitals in the West had had a negative association with adoption of administrative IT compared to hospitals in the Northeast. Ownership type, teaching status, and market competition did not appear to affect the adoption of administrative IT. Finally, Table 4 indicates that the adoption of strategic IT was positively affected significantly by size (p < .001), urban location (p < .001), ownership (p < .01), and HMO penetration (p < .01). Teaching status, regional location, HHI, and payer mix did not significantly affect the adoption of strategic IT.
Table 4

Negative binomial regression model for adoption of health information technology (N = 582)

Variable

Clinical IT

Administrative IT

Strategic IT

β

95 % CI

β

95 % CI

β

95 % CI

Intercept

2.2704ǂ

(1.9223, 2.6186)

2.2943ǂ

(2.0057, 2.5829)

1.0867ǂ

(0.6988, 1.4747)

Size

0.0010ǂ

(0.0007, 0.0013)

0.0005ǂ

(0.0003, 0.0007)

0.0008ǂ

(0.0005, 0.0011)

Ownership

0.1999Ɨ

(0.0763, 0.3234)

0.0987

(−0.0277, 0.2252)

0.2057 Ɨ

(0.0581, 0.3532)

Teaching status

−0.019

(−0.1092, 0.0712)

0.0425

(−0.0238, 0.1089)

−0.1032

(−0.2108, 0.0044)

HMO

0.1566*

(0.0218, 0.2913)

0.1414*

(0.0200, 0.2627)

0.2441 Ɨ

(0.0910, 0.3972)

Urban location

0.2840ǂ

(0.1427, 0.4253)

0.1812 Ɨ

(0.0562, 0.3062)

0.4228ǂ

(0.2597, 0.5859)

Midwest

−0.1039

(−0.2409, 0.0331)

0.0206

(−0.0980, 0.1392)

−0.0325

(−0.1834, 0.1184)

South

0.0179

(−0.1144, 0.1501)

0.0578

(−0.0622, 0.1779)

0.0897

(−0.0594, 0.2389)

West

−0.2677ǂ

(−0.4059, −0.1296)

−0.1258*

(−0.2512, −0.0004)

−0.1292

(−0.2843, 0.0260)

HHI

−0.1281

(−0.2887, 0.0325)

−0.1304

(−0.2792, 0.0184)

−0.1345

(−0.3064, 0.0373)

Payer mix

−0.4014*

(−0.7674, −0.0355)

−0.3468*

(−0.6506, −0.0430)

−0.3189

(−0.7262, 0.0884)

* p < .05; Ɨ p < .01; ǂ p < .001

Discussion

Technology adoption

In terms of health information technology adoption, the results in this study support earlier findings. Even though clinical IT applications were more directly related to hospitals’ primary goal of delivering higher quality of care, the evidence in this study indicated that more emphasis is given to administrative and strategic IT applications. This finding is line with Poon et al. [22]. In their finding, administrative technologies, such as claims and eligibility checking applications, had larger diffusion rates than technologies with clinical applications. Poon et al concluded that the adoption of information technology is mainly motivated by financial functionality rather than the improvement of service quality and safety. In a different study, Chaudry et al. [3] reported that the healthcare industry has primarily focused on acquiring technological applications that are related to administration and financial transactions.

Organizational factors

From an organizational perspective, the empirical analysis suggests that hospital size was the most important predictor of health information technology adoption. Large hospitals consistently adopted the largest number of clinical, administrative, and strategic IT applications compared to small- and medium-size hospitals. These results were similar to the findings of a number of authors. Burke et al. [26] and Wang et al. [25] found positive associations between adoption of clinical, administrative, and strategic IT and hospital size. Kazley and Ozcan [27], Furukawa et al. [28] and Parente and Van Horn [29] also reported significant relationships between large hospital size and adoption of some clinical IT applications.

Coming to ownership, for-profit hospitals were more likely to adopt clinical and strategic IT but were not related to administrative IT adoption. Taylor et al. [9], Furukawa et al. [28] and Amarasingham et al. [30] all reported positive effects of for-profit ownership on some clinical IT applications. However, Kazley and Ozcan [27] national study reported no association between profit ownership and the adoption of EMR, a key clinical IT application. Strategic IT adoption was also reported to be positively related to for-profit ownership by Burke et al. [26] and Wang et al. [25]. Given the pressure on for-profit hospitals to deliver high returns on investments, it would be logical for them to acquire strategic IT as that would enhance market interaction and enable strategic decisions.

Teaching status of a hospital was not associated with any of the three IT application clusters. Wang et al. [25] also found no relationship between teaching status and adoption of technologies. However, unlike the study by Wang and colleagues, this study found a significant positive association between HMO penetration and all three technology clusters. The findings of this study and Wang et al. [25] stand in contrast to the several other studies. Kazley and Ozcan [27], Furukawa et al. [28] and Amarasingham et al. [30] reported positive relationship between teaching status and clinical IT adoption.

Contextual factors

From a contextual perspective, this analysis suggests that urban location was the most important predictor of the adoption of the three categories of HIT. Several other studies corroborated the finding [26, 27, 28]. One reason could relate to the relative higher demand and expectation for advanced technologies by urban patients as compared to rural counterparts. Another reason could emanate from the better opportunities urban hospitals have to partner with various industries, government agencies, and foundations. The relative ease of finding partners enables urban hospitals to secure external financial resources and acquire insider information about the technologies. Consequently, urban hospitals have a relative advantage in effectively adopting better technologies in terms of quality and quantity.

Regional location is another significant determinant of HIT adoption. Hospitals in the West are negatively associated with adoption of clinical and administrative IT as compared to hospitals located in the Northeast. This finding supported Furukawa et al. [28]. They reported that hospitals on the east coast had higher IT adoption rates compared to those on the west coast. In terms of HIT adoption, there were no differences between hospitals in the Northeast, the Midwest, and the South.

Market competition, in this study, was not associated with the adoption of any of the three technology clusters. This finding appears to suggest that market pressure was not detrimental to hospital adoption of the technologies. This finding also supports Wang et al. [25] as they also reported no relationship between market competition and the adoption of all three information technology types. In contrast, Burke et al. [26] found a significant association between market competition and all three technology clusters.

The proportion of Medicare and Medicaid patients was found to be significantly negatively affected by the adoption of clinical and administrative IT but not by strategic IT. This finding also supports Furukawa and colleagues [28] as they reported a negative association between proportion of Medicare patients and adoption of some clinical IT applications. Since clinical IT applications are directly related to the treatment of patients, further investigation is needed to understand the relationship between the proportion of Medicare and Medicaid patients and clinical IT adoption.

Limitations

This study has several limitations. First, it is based on typical administrative data that may have questionable coding accuracy, variation, and timing of events [31]. Second, since this study is not based on a randomly assigned design model and excluded hospitals with missing data, its generalizability may be limited. Third, the study may not account for extraneous factors that affect the adoption of technologies in hospitals. Lastly, no individual technology was specifically examined within administrative, clinical and strategic information technology groups. However, the impact of these limitations is expected to be minimal because: (1) the data sets have been repeatedly tested and used in the past; (2) the national sample of hospitals collected by AHRQ was a fair representation of all U.S. hospitals; (3) the study was based on the most recent data that should be able to detect the latest trend; and (4) barring possible confirmation biases, the similarities of the findings to previous researches provides validity to the methodologies and the data in this study.

Conclusions and recommendations

More often than not, hospitals acquired commercially available information technologies that typically require substantial investment for installation, training, operation, and maintenance. As such, hospitals with better financial resources had a greater likelihood of adopting these costly technologies whereas smaller hospitals did not. Moreover, the current reimbursement reduction trends have forced hospitals to focus not only on providing high quality of care but also on cost containment. Previous studies demonstrated that investments in HIT applications were associated with eventual cost savings in hospitals [4, 9, 21]. Hence, hospitals with fewer resources to invest in IT may be at a big disadvantage in terms of not only providing higher quality of care but also in the area of cost containment. Table 5 presents a summary of main findings.
Table 5

Summary points

What have already known before this article

 • More emphasis is given to administrative and strategic IT applications than clinical IT applications

 • Large hospitals consistently adopted the largest number of clinical, administrative, and strategic IT applications

 • Urban location was the most important predictor of the adoption of clinical, administrative, and strategic IT applications

What this article added

 • It examined the predictors of HIT in three perspectives including clinical, administrative and strategic applications

 • It included a broad range of technologies (52 HIT applications) that offered a better investigation of predictors of the HIT adoption

 • HMO penetration was accompanied by increased adoption of all three technology clusters (clinical, administrative, and strategic IT applications)

 • For-profit hospitals exhibited higher-level clinical and strategic IT adoptions

This is a time of unprecedented change in the U.S. healthcare system. The new healthcare reform bills, the Patient Protection and Affordable Care Act (PPACA) of 2010 and the Health Care and Education Reconciliation Act of 2010, have been signed into law by President Obama. In addition, the Electronic Health Record (EHR) Incentive Program in 2011 established by the Health Information for Clinical and Economic Health Act of 2009 (HITECH) provides incentives for hospitals and other providers to improve care through EHR. The findings in this study indicated that there are still considerable variations in the adoption of information technology across hospitals and in the type of technology adopted. Thus, it is important to evaluate the uses of information technology particularly EHR after the implementation of HITECH.

Yet, the findings in this study and many other studies warrant further aggressive policy interventions from the government that will particularly speed up the adoption of technologies with clinical applications.

Future studies should investigate the comparative effectiveness of individual HIT in term of their cost-benefits and efficiencies. In addition, the meaningful uses of HIT in healthcare deliveries among different stakeholders need further investigation, such as the benefits of providing complete and accurate information, better access to information and patient empowerment. Moreover, the long term impacts and sustainability of HIT applications on healthcare outcomes is another area of research that deserves attention. Finally, the effect of region-specific characteristics such as state regulations on the adoption of technologies should also be examined.

Notes

Conflicts of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Ning Jackie Zhang
    • 1
  • Binyam Seblega
    • 2
  • Thomas Wan
    • 3
  • Lynn Unruh
    • 4
  • Abiy Agiro
    • 5
  • Li Miao
    • 3
  1. 1.Doctoral Program in Public Affairs and Department of Health Management and Informatics, College of Health and Public AffairsUniversity of Central FloridaOrlandoUSA
  2. 2.UnitedHealth GroupTrumbullUSA
  3. 3.Doctoral Program in Public Affairs, College of Health and Public AffairsUniversity of Central FloridaOrlandoUSA
  4. 4.Department of Health and Public Affairs, College of Health and Public AffairsUniversity of Central FloridaOrlandoUSA
  5. 5.HealthCoreWilmingtonUSA

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