Factors Influencing Health Information System Adoption in American Hospitals

Wang, Bill B; Wan, Thomas T. H.; Burke, Darrell; Bazzoli, Gloria J.; Lin, Blossom Yen-Ju

doi:10.1097/00004010-200501000-00007

Cited by 102 publications

(109 citation statements)

References 25 publications

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“…25 In addition to the risk adjustment, we also controlled for hospital-level variables, such as bed size, ownership type, teaching affiliation, system membership, network participation, the number of staffed beds per full time equivalent registered nurses, percentage Medicare patients, percentage Medicaid patients, capitation-based reimbursement, market competitiveness, rural or urban hospital, and hospital region. 26,27 Given that there were more than 40 independent variables to be modeled, we used the preliminary regression analysis for detecting multicollinearity among independent variables in the multivariable models. 28 For example, teaching hospital status was not included in the multivariable model because it was highly correlated to bed size; percentage of Medicare patients was not included due to its high correlation with percentage of Medicaid patients.…”

Section: Discussionmentioning

confidence: 99%

Racial and Insurance Status Disparities in Patient Safety Indicators among Hospitalized Patients

et al. 2016

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Objective: To examine the association between patient race/ethnicity, insurance status, and their interaction with patient safety indicators among hospitalized patients.Methods: Cross-sectional study was conducted. Data were extracted from the 2009 National Inpatient Sample. A total of 3,052,268 patient safety indicator-related discharges were identified. Dependent variables were 11 patient safety indicators (PSI) whereas independent variables included race/ethnicity and insurance status. Results:As compared with White patients, African American patients were more likely to experience pressure ulcer, postoperative hemorrhage or hematoma, and post-operative pulmonary embolism (PE) or deep vein thrombosis (DVE); Asian/Pacific Islander patients were more likely to experience pressure ulcer, post-operative PE or DVT, and two obstetric care PSIs; whereas Hispanic/Latino patients were more likely to experience post-operative physiometabolic derangement and accidental puncture/ laceration. As compared with patients with private insurance, Medicaid patients were more likely to experience pressure ulcer, post-operative physiological metabolic derangement, post-operative PE or DVT, post-operative respiratory failure, post-operative wound dehiscence, and death among surgeries. However, both obstetric care PSIs showed that African Americans, Hispanics, and uninsured patients were less likely to incur them in comparison with their respective counterparts. Furthermore, strong interactive effects between African American and Medicaid on PSIs were detected. Conclusions:Although mixed findings in disparities in PSIs were observed in our study, Asian/Pacific Islander patients and Medicaid patients seem to be the most vulnerable. Further, interactive effects between African American and Medicaid indicate that poverty may be a key factor related to disparities in health care. Future research is merited to identify underlying factors that are related to PSIs among Asian/Pacific Islander patients. Strategies are needed to improve PSIs among Medicaid patients, especially during the current Medicaid program expansion due to the implementation of the Affordable Care Act.

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Section: Discussionmentioning

confidence: 99%

Racial and Insurance Status Disparities in Patient Safety Indicators among Hospitalized Patients

et al. 2016

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“…Many factors may influence the adoption and acceptance of these systems. Several studies have investigated these factors [1,11,12]. According to Anderson et al [13] since physicians are the main front line users of HIS, their resistance is one of the important factors affecting implementation of this system.…”

Section: Introductionmentioning

confidence: 99%

“…According to Anderson et al [13] since physicians are the main front line users of HIS, their resistance is one of the important factors affecting implementation of this system. Other researches [12,10,14,15] addressing failure factors showed that obstacles such as time constraints, security and privacy risk, lack of an adequate policy regarding medical IT, non-applicability with regard to patient characteristics, and complex clinical settings are among culprits. Similarly, lack of financial resources and high costs; poor management and bureaucracy; poor staff IT competency; lack of qualified IT personnel and lack of awareness of HIT (health information technology) values, are barriers to the adoption of health information technology in Arab countries' hospitals [16].…”

Section: Introductionmentioning

confidence: 99%

Prioritizing Barriers to Successful Implementation of Hospital Information Systems

et al. 2014

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Hospital information systems (HIS) are often implemented to enhance the quality of care, as well as to improve the efficiency and safety of health care services. However, there are various barriers for their successful implementation. The aim of this paper is to prioritize these barriers. This research is a cross sectional analytic-descriptive study. The study populations were hospital managers, IT department administrators, and clinical supervisors at the academic and non-academic hospitals of two cities in Iran. The data was collected by a questionnaire that its content validity was confirmed by three specialists. Its reliability was confirmed using Cronbach's alpha (α = 0.78). Questionnaire contained five dimensions and 39 implementation barriers. The collected data was analyzed by descriptive and analytical statistics using the Kendall Rank Correlation Coefficient and Chi2 tests. The findings of the study revealed that lack of powerful information networks, error in data entry, technical problems related to system design, lack of organizational training, lack of users' knowledge about system and working with it, and negative attitudes of providers and patients toward systems are the most important barriers of HIS implementation. Prioritizing of these barriers helps policy makers to decide what to do when planning for HIS utilization.

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“…Hospital characteristics, such as economies of scale, payer mix, ownership, urban or rural location, financial performance, and teaching status are found to be strong predictors of technology adoption [13][14][15][16][17][18][19]. Investigated environmental factors include competition, reimbursement policies [20,21], managed care penetration [22,23], insurance market characteristics [24], and the technology adoption behavior of neighboring hospitals [12].…”

Section: Introductionmentioning

confidence: 99%

U.S. hospital efficiency and adoption of health information technology

Zhivan

Diana

2011

Health Care Manag Sci

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This study empirically examines the association between hospital inefficiency and the decision to introduce electronic medical records (EMR) and computerized physician order entry (CPOE) in a national sample of U.S. general hospitals in urban areas in 2006. The main research question is whether the presence of hospital cost inefficiency or other factors driving inefficiency in the production process of a hospital explain low adoption rates of health information technology (HIT) in a hospital setting. We estimated a logistic regression of HIT adoption as a function of hospital cost inefficiency scores obtained using a stochastic frontier analysis. The results demonstrate that hospitals with a greater degree of cost inefficiency were more likely to introduce EMR, suggesting that the benefits of EMR implementation in terms of improved efficiency were likely to outweigh the costs of adoption compared to hospitals that are more efficient. The results showed no association between cost inefficiency and the CPOE adoption decision.

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Factors Influencing Health Information System Adoption in American Hospitals

Abstract: The study concludes that hospital organizational and financial factors influence on hospitals' strategic adoption of clinical, administrative, and managerial information systems.

Cited by 102 publications

References 25 publications

Racial and Insurance Status Disparities in Patient Safety Indicators among Hospitalized Patients

Racial and Insurance Status Disparities in Patient Safety Indicators among Hospitalized Patients

Prioritizing Barriers to Successful Implementation of Hospital Information Systems

U.S. hospital efficiency and adoption of health information technology

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