| Title |
Health Information Technology Adoption in U.S. Acute Care Hospitals
|
|---|---|
| Published in |
Journal of Medical Systems, January 2013
|
| DOI | 10.1007/s10916-012-9907-2 |
| Pubmed ID | |
| Authors |
Ning Jackie Zhang, Binyam Seblega, Thomas Wan, Lynn Unruh, Abiy Agiro, Li Miao |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Argentina | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Canada | 1 | <1% |
| Unknown | 141 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 42 | 29% |
| Student > Master | 21 | 15% |
| Student > Doctoral Student | 13 | 9% |
| Researcher | 8 | 6% |
| Student > Bachelor | 8 | 6% |
| Other | 25 | 17% |
| Unknown | 27 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 25 | 17% |
| Computer Science | 22 | 15% |
| Business, Management and Accounting | 21 | 15% |
| Nursing and Health Professions | 14 | 10% |
| Engineering | 8 | 6% |
| Other | 23 | 16% |
| Unknown | 31 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 January 2015.
All research outputs
#18,327,422
of 22,694,633 outputs
Outputs from Journal of Medical Systems
#804
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Outputs of similar age
#217,359
of 280,485 outputs
Outputs of similar age from Journal of Medical Systems
#9
of 12 outputs
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