OBJECTIVES: The efficiency of a hospital's operating room (OR) management can affect its overall profitability. However, existing indicators that assess OR management efficiency do not take into account differences in hospital size, manpower and functional characteristics, thereby rendering them unsuitable for multi-institutional comparisons. The aim of this study was to develop indicators of OR management efficiency that would take into account differences in hospital size and manpower, which may then be applied to multi-institutional comparisons. METHODS: Using administrative data from 224 hospitals in Japan from 2008 to 2010, we performed four multiple linear regression analyses at the hospital level, in which the dependent variables were the number of operations per OR per month, procedural fees per OR per month, total utilization times per OR per month and total fees per OR per month for each of the models. RESULTS: The expected values of these four indicators were produced using multiple regression analysis results, adjusting for differences in hospital size and manpower, which are beyond the control of process owners' management. However, more than half of the variations in three of these four indicators were shown to be explained by differences in hospital size and manpower. CONCLUSION: Using the ratio of observed to expected values (OE ratio), as well as the difference between the two values (OE difference) allows hospitals to identify weaknesses in efficiency with more validity when compared to unadjusted indicators. The new indicators may support the improvement and sustainment of a high-quality health care system.
OBJECTIVES: The efficiency of a hospital's operating room (OR) management can affect its overall profitability. However, existing indicators that assess OR management efficiency do not take into account differences in hospital size, manpower and functional characteristics, thereby rendering them unsuitable for multi-institutional comparisons. The aim of this study was to develop indicators of OR management efficiency that would take into account differences in hospital size and manpower, which may then be applied to multi-institutional comparisons. METHODS: Using administrative data from 224 hospitals in Japan from 2008 to 2010, we performed four multiple linear regression analyses at the hospital level, in which the dependent variables were the number of operations per OR per month, procedural fees per OR per month, total utilization times per OR per month and total fees per OR per month for each of the models. RESULTS: The expected values of these four indicators were produced using multiple regression analysis results, adjusting for differences in hospital size and manpower, which are beyond the control of process owners' management. However, more than half of the variations in three of these four indicators were shown to be explained by differences in hospital size and manpower. CONCLUSION: Using the ratio of observed to expected values (OE ratio), as well as the difference between the two values (OE difference) allows hospitals to identify weaknesses in efficiency with more validity when compared to unadjusted indicators. The new indicators may support the improvement and sustainment of a high-quality health care system.