RT Journal Article
SR Electronic
T1 Identification of high resource utilizing patients on internal medicine hospital services
JF Journal of Investigative Medicine
JO J Investig Med
FD BMJ Publishing Group Ltd
SP 1172
OP 1178
DO 10.1136/jim-2016-000118
VO 64
IS 7
A1 David W Walsh
A1 Molly C McVey
A1 Abigal Gass
A1 Jingwen Zhang
A1 Patrick D Mauldin
A1 Don C Rockey
YR 2016
UL http://hw-f5-jim.highwire.org/content/64/7/1172.abstract
AB 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.