Over the last decade, healthcare supply chain (SC) costs have increased by 40 percent in the United States. A typical hospital’s SC costs account for 38 percent of the total, compared to less than 10 percent for most industries. Supply chain costs are the healthcare organizations’ second biggest expense. Healthcare centers are, therefore, becoming supply chain-sensitive organizations, leading to inefficiency and limited access to quality patient healthcare. This problem is exacerbated by the fact that healthcare SC cost drivers are almost unknown, which makes the work of healthcare SC managers more difficult. This study focuses on uncovering SC cost drivers and provides appropriate cost-reduction strategies tailored to confront each identified driver. Primary data were collected from health centers and secondary data was collected from databases such as Health Care Cost Institute (HCCI), Data Resources Agency for Healthcare Research & Quality, National Health Expenditure Data, and Centers for Medicare & Medicaid. The study looked at the attributes that explain the most variation in each contributing factor. A multiple regression was developed to predict the costs along with F tests and Student t-tests to determine the model goodness-of-fit and each factor’s contribution significance. The results of the study might lead to improved efficiency in healthcare organizations and increased access to quality healthcare for the population.
|Advisor:||Eggstaff, Justin, Islam, Muhammad F.|
|Commitee:||Eggstaff, Justin, Etemadi, Amir, Hardie, Daniel D., Islam, Muhammad F., Murphree, E. Lile|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 80/02(E), Dissertation Abstracts International|
|Subjects:||Engineering, Health care management, Operations research|
|Keywords:||Cost drivers, Healthcare supply chain cost drivers, Primary care supply chain, Regression analysis, Supply chain management|
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