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ECONOMICS, EDUCATION, AND POLICY

Tactical Increases in Operating Room Block Time Based on Financial Data and Market Growth Estimates from Data Envelopment Analysis

Liam O’Neill, PhD^*, and Franklin Dexter, MD, PhD

From the *Department of Health Management and Policy, University of North Texas, Fort Worth, Texas; and Division of Management Consulting, Departments of Anesthesia and Health Management and Policy, University of Iowa, Iowa City, Iowa.

Address correspondence and reprint requests to Franklin Dexter, Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, IA 52242. Address e-mail to Franklin-Dexter{at}UIowa.edu.

Abstract

BACKGROUND: Data envelopment analysis (DEA) is an established technique that hospitals and anesthesia groups can use to understand their potential to grow different specialties of inpatient surgery. Often related decisions such as recruitment of new physicians are made promptly. A practical challenge in using DEA in practice for this application has been the time to obtain access to and preprocess discharge data from states.

METHODS: A case study is presented to show how results of DEA are linked to financial analysis for purposes of deciding which surgical specialties should be provided more resources and institutional support, including the allocation of additional operating room (OR) block time on a tactical (1 yr) time course. State discharge abstract databases were used to study how to perform and present the DEA using data from websites of the United States’ (US) Healthcare Cost and Utilization Project (HCUPNet) and Census Bureau (American FactFinder).

RESULTS: DEA was performed without state discharge data by using census data with federal surgical rates adjusted for age and gender. Validity was assessed based on multiple criteria, including: satisfaction of statistical assumptions, face validity of results for hospitals, differentiation between efficient and inefficient hospitals on other measures of how much surgery is done, and correlation of estimates of each hospital’s potential to grow the workload of each of eight specialties with estimates obtained using unrelated statistical methods.

CONCLUSIONS: A hospital can choose specialties to target for expanded OR capacity based on its financial data, its caseloads for specific specialties, the caseloads from hospitals previously examined, and surgical rates from federal census data.

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