Factors Affecting Electronic Medical Record System Adoption in Small Korean Hospitals
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초록
Objectives: The objective of this paper is to investigate the factors affecting adoption of an Electronic Medical Record (EMR) system in small Korean hospitals. Methods: This study used survey data on adoption of EMR systems; data included that from various hospital organizational structures. The survey was conducted from April 10 to August 3, 2009. The response rate was 33.5% and the total number of small general hospitals was 144. Data were analyzed using the generalized estimating equation method to adjust for environmental clustering effects. Results: The adoption rate of EMR systems was 40.2% for all responding small hospitals. The study results indicate that IT infrastructure (OR, 1.48; 95% CI, 1.23 to 1.80) and organic hospital structure (OR, 1.86; 95% CI, 1.07 to 3.23) rather than mechanistic hospital structure or the number of hospitals within a county (OR, 1.08; 95% CI, 1.01 to 1.17) were critical factors for EMR adoption after controlling for various hospital covariates. Conclusions: This study found that several managerial features of hospitals and one environmental factor were related to the adoption of EMR systems in small Korean hospitals. Considering that health information technology produces many positive health outcomes and that an ‘adoption gap’ regarding information technology exists in small clinical settings, healthcare policy makers should understand which organizational and environmental factors affect adoption of EMR systems and take action to financially support small hospitals during this transition.
Abstract
Objectives: The objective of this paper is to investigate the factors affecting adoption of an Electronic Medical Record (EMR) system in small Korean hospitals. Methods: This study used survey data on adoption of EMR systems; data included that from various hospital organizational structures. The survey was conducted from April 10 to August 3, 2009. The response rate was 33.5% and the total number of small general hospitals was 144. Data were analyzed using the generalized estimating equation method to adjust for environmental clustering effects. Results: The adoption rate of EMR systems was 40.2% for all responding small hospitals. The study results indicate that IT infrastructure (OR, 1.48; 95% CI, 1.23 to 1.80) and organic hospital structure (OR, 1.86; 95% CI, 1.07 to 3.23) rather than mechanistic hospital structure or the number of hospitals within a county (OR, 1.08; 95% CI, 1.01 to 1.17) were critical factors for EMR adoption after controlling for various hospital covariates. Conclusions: This study found that several managerial features of hospitals and one environmental factor were related to the adoption of EMR systems in small Korean hospitals. Considering that health information technology produces many positive health outcomes and that an ‘adoption gap’ regarding information technology exists in small clinical settings, healthcare policy makers should understand which organizational and environmental factors affect adoption of EMR systems and take action to financially support small hospitals during this transition.
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