Abstract
BackgroundGeneral hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17.MethodsWe undertook input-oriented data envelopment analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1.ResultsThe average constant returns to scale, variable returns to scale and scale efficiency of general hospitals for 2016/17 were 49% (95% CI, 44–54%), 69% (95% CI, 65–74%) and 70% (95% CI, 65–75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.ConclusionThe 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3439) less staff and 31% (3539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.
Highlights
General hospitals provide a wide range of primary and secondary healthcare services
Descriptive statistics is provided for all inputs, outputs and predictor variables for financial year (FY) 2016/ 17 as this is the focus year of comprehensive analysis
The study provides empirical evidence on the level, trends and determinants of efficiency among 78 general hospitals that routinely submitted data of acceptable quality on the study variables. It shows that average Constant Returns to Scale (CRS) Technical efficiency (TE), Variable Returns to Scale (VRS) TE and Scale efficiency (SE) scores among all the hospitals investigated were only 49, 69 and 70% respectively and that Private Not for Profit (PNFP) hospitals had higher technical efficiency scores than public hospitals, though the differences were not statistically significant
Summary
General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. Scale efficiency (SE) if achieved when a DMU operates at its optimal size [11]. Changing the operational size of an efficient DMU either results in increasing returns to scale (IRS) whereby an increase in inputs results in a proportionally bigger increase in outputs (economies of scale) or a decreasing return to scale (DRS) in which increase in inputs creates a proportionally smaller volume of outputs (diseconomies of scale) [12]. Data envelopment analysis (DEA) and Stochastic frontier analysis (SFA) are the most commonly used approaches for conducting non-parametric and parametric TE analysis [13, 14]
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