title: Data envelopment analysis to determine by how much hospitals can increase elective inpatient surgical workload for each specialty.
abstract: We apply data envelopment analysis to discharge data from the 115 hospitals in the rural state of a study hospital to answer three questions. We use a case study to investigate the usefulness and limitations of data envelopment analysis for assessing three common questions regarding hospital market capture for elective inpatient surgery. (i) The hospital studied in this paper performs 40% of the neurosurgery and 25% of the inpatient urology surgery in its state. Workloads are twice that of the hospitals with the next largest workloads. In contrast, the hospital performs 9% of its state's cardiac surgery and has a workload half that of the largest volume hospital. The cardiac surgeons want more operating room time, faster turnovers, and capital investment for minimally invasive equipment. Controlling for the distance patients would need to travel for care, would increasing capacity likely increase cardiac surgery workload? (ii) The study hospital has fewer hospitalizations for thoracic surgery than for any other specialty. Is thoracic surgery inpatient workload of 121 lung resections large or small compared with those of orthopedics' 213 hip replacements, urology's 132 nephrectomies, and cardiac surgery's 304 coronary artery bypass grafts? (iii) The hospital's busiest specialty by discharges is orthopedics. How sensitive is the hospital's orthopedic workload to changes in decision making at nearby competing hospitals?
authors.affiliation: *Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa; †Department of Policy Analysis and Management, Cornell University, Ithaca, New York.
authors.forename: Franklin
authors.initials: F
authors.lastname: Dexter
authors.forename: Liam
authors.initials: L
authors.lastname: O'Neill
mesh_terms: D003399:Craniotomy / Q000706:statistics & numerical data; D017558:Elective Surgical Procedures / Q000706:statistics & numerical data*; D013502:General Surgery; D006276:Health Facility Size; D006739:Hospital Administration; D006801:Humans; D015233:Models, Statistical; D009873:Operating Rooms / Q000458:organization & administration*; D010351:Patient Discharge / Q000706:statistics & numerical data; D013038:Specialization; D000078329:Workforce; D016526:Workload / Q000706:statistics & numerical data*