Levels, trends and determinants of technical efficiency of general hospitals in Uganda: data envelopment analysis and Tobit regression analysis

Abstract

Background 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. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17. Methods We 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. Results The 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. Conclusion The 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.

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019

“…Kohl et al included 18 studies of hospital operations in their systematic review of the literature in which the Tobit regression model was applied in the second phase using transformed DEA scores as dependent variables [ 15 ]. A search of the Medline database on the 9th of November, revealed an additional 16 such papers that have been published since that systematic review was conducted [ 10 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Among the published papers, studies focused on European hospital systems were well-represented with two studies from Turkey and one study each from Ukraine, Greece, Poland, and the Netherlands [ 29 , 41 , 42 , 43 , 44 ].…”

“…Raising bed occupancy seems to increase efficiency, but only to a certain threshold, after which point it is correlated with inefficiencies, becomes a threat to the safety of patients, and jeopardizes the quality of care [ 45 , 46 , 47 ]. In every study except one, average length of hospital stay is correlated with lower level of efficiency, whereas the ratio between outpatient episodes and inpatient days has the opposite effect [ 30 , 34 ]. On the other hand, the effects of factors such as hospital competition, hospital size as measured by the number of beds, hospital type, the percentage of elderly patients, and the number of specific health workers per hospital bed, are contradictory and vary across studies.…”

Configurational Paths to Higher Efficiency in County Hospital: Evidence From Qualitative Comparative Analysis

“…In terms of hospital organizational structure, ownership type, hospital scale, and human resource structure were often used. In addition, as a healthcare service delivery unit, hospital service behavior also impacts its efficiency, including service capacity, bed utilization status, and patient structure (32,40).…”

“…The non-parametric approach, represented by the DEA method, was widely used because of the multi-input and multi-output nature of the healthcare system (26), which originated from Farell's concept of technical efficiency and was proposed by Charnes et al (27). So far, the method has been widely used in measuring the efficiency of healthcare delivery systems in both developed and developing countries (28)(29)(30)(31)(32). Since the DEA approach creates an efficiency frontier based on available data, the efficiency values calculated using this method are inherently biased in a positive and or at least non-negative direction (13).…”

Determinants of Health Service Efficiency in Hospi-tal: A Systematic Review

“…Availability of hospital beds and efficiency in the management of health resources play an important role in determining the maneuvering room for health facilities in the event of an emergency [9]. Various studies identify factors to look at efficiency, such as hospital size, own-ership, bed occupancy rate, outpatient to inpatient ratio, average income of the population, technology, financing, and hospi-tal teaching status as determinants of hospital efficiency [10].…”