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Identification of high resource utilizing patients on internal medicine hospital services
  1. David W Walsh1,
  2. Molly C McVey2,
  3. Abigal Gass2,
  4. Jingwen Zhang2,3,
  5. Patrick D Mauldin2,3,
  6. Don C Rockey2
  1. 1Department of Internal Medicine, Division of General Internal Medicine, Vanderbilt University, Nashville, Tennessee, USA
  2. 2Department of Internal Medicine, Medical University of South Carolina University, Charleston, South Carolina, USA
  3. 3Division of General Internal Medicine, Medical University of South Carolina University, Charleston, South Carolina, USA
  1. Correspondence to Dr Don C Rockey, Department of Internal Medicine, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 803, MSC 623, Charleston SC 29425, USA; rockey{at}


In order to provide high quality, cost-efficient care, it is critical to understand drivers of the cost of care. Therefore, we sought to identify clinical variables associated with high utilization (cost) in patients admitted to medical services and to develop a robust model to identify high utilization patients. In this case–control analysis, cases were identified as the 200 most costly patients admitted to internal medicine/internal medicine subspecialty services using our institution's computerized clinical data warehouse over a 7-month time period (November 1, 2012–May 31, 2013). 400 patients admitted in the same time period were randomly selected to serve as controls. The mean cost for the highest utilization patients was $126,343, while that for randomly matched patients was $15,575. In a multivariable regression model, the following variables were associated with high utilization of resources: African American race, age 35–44, admission through the emergency department, primary service of hematology–oncology, a history of heart failure or paralysis, a diagnosis of HIV, cancer, collagen vascular diseases and/or coagulopathy, a reduced albumin, and/or an elevated creatinine. The in hospital mortality rate for high utilization patients was 19%, compared to 8% for controls (p=0.0002). A predictive model using 14 different readily available clinical variables predicted high utilization with an area under the curve of 0.85. The data suggest that high utilization patients share similar demographic and clinical features. We speculate that a predictive model using commonly known patient characteristics should be able to predict high utilization patients.

  • Academic Medical Centers
  • Inpatients
  • Intensive Care

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