title: Identification of High Utilization Inpatients on Internal Medicine Services.
abstract: As healthcare reform moves toward value based care, hospitals must reduce costs. As a first step, here we developed a predictive model to identify high-cost patients on admission.
  
  
  
  We performed a retrospective observational study of 7,571 adults admitted to internal medicine services from July 1, 2013 to June 30, 2014. We compared the top 10% highest cost patients to other patients (controls) and identified clinical variables associated with high inpatient costs. Using logistic regression analyses, we developed a predictive model that could be used on admission to identify potential high utilization patients.
  
  
  
  In the 757 high utilizer patients, the median total hospital cost was $53,430 ± 60,679 compared to $8,431 ± 7,245 in the control group (P < 0.0001). The median length of stay for high utilization patients was 19.5 ± 32.5 days compared to 3.8 ± 3.9 days in the control group (P < 0.001). Variables associated with high utilization included transfer from an outside hospital (odds ratio [OR] = 1.6), admission to the pulmonary or medical intensive care unit (OR = 2.4), admission to cardiology (OR = 1.8), coagulopathy (OR = 2.6) and fluid and electrolyte disorders (OR = 2.1). A multivariate logistic regression model was used to fit a predictive model for high utilizers. The receiver operating characteristics curve of this prediction model yielded an area under the curve of 0.80.
  
  
  
  High resource utilization patients appear to have a specific phenotype that can be predicted with commonly available clinical variables. Our predictive formula holds promise as a tool that may help ultimately reduce hospital costs.
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Marc
authors.initials: M
authors.lastname: Heincelman
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Samuel O
authors.initials: SO
authors.lastname: Schumann
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Jenny
authors.initials: J
authors.lastname: Riley
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Jingwen
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Justin E
authors.initials: JE
authors.lastname: Marsden
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina.
authors.forename: Patrick D
authors.initials: PD
authors.lastname: Mauldin
authors.affiliation: Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina. Electronic address: rockey@musc.edu.
authors.forename: Don C
authors.initials: DC
authors.lastname: Rockey
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006779:Hospitals, Public; D006784:Hospitals, Teaching; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D013022:South Carolina; D055815:Young Adult
keywords: Accountable care; Cost; Hospital; Length of stay; Predictive modeling; Value