Ownership and technical efficiency of hospitals: evidence from Ghana using data envelopment analysis

Abstract

BackgroundIn order to measure and analyse the technical efficiency of district hospitals in Ghana, the specific objectives of this study were to (a) estimate the relative technical and scale efficiency of government, mission, private and quasi-government district hospitals in Ghana in 2005; (b) estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient; and (c) use Tobit regression analysis to estimate the impact of ownership on hospital efficiency.MethodsIn the first stage, we used data envelopment analysis (DEA) to estimate the efficiency of 128 hospitals comprising of 73 government hospitals, 42 mission hospitals, 7 quasi-government hospitals and 6 private hospitals. In the second stage, the estimated DEA efficiency scores are regressed against hospital ownership variable using a Tobit model. This was a retrospective study.ResultsIn our DEA analysis, using the variable returns to scale model, out of 128 district hospitals, 31 (24.0%) were 100% efficient, 25 (19.5%) were very close to being efficient with efficiency scores ranging from 70% to 99.9% and 71 (56.2%) had efficiency scores below 50%. The lowest-performing hospitals had efficiency scores ranging from 21% to 30%.Quasi-government hospitals had the highest mean efficiency score (83.9%) followed by public hospitals (70.4%), mission hospitals (68.6%) and private hospitals (55.8%). However, public hospitals also got the lowest mean technical efficiency scores (27.4%), implying they have some of the most inefficient hospitals.Regarding regional performance, Northern region hospitals had the highest mean efficiency score (83.0%) and Volta Region hospitals had the lowest mean score (43.0%).From our Tobit regression, we found out that while quasi-government ownership is positively associated with hospital technical efficiency, private ownership negatively affects hospital efficiency.ConclusionsIt would be prudent for policy-makers to examine the least efficient hospitals to correct widespread inefficiency. This would include reconsidering the number of hospitals and their distribution, improving efficiency and reducing duplication by closing or scaling down hospitals with efficiency scores below a certain threshold. For private hospitals with inefficiency related to large size, there is a need to break down such hospitals into manageable sizes.

Predictors of public and private healthcare utilization and associated health system responsiveness among older adults in Ghana

“…Private healthcare consists of private for-profit facilities owned by private individuals or companies which are privately funded through payments for medical services, and private not-for-profit services such as mission or faith-based facilities involved in the direct delivery of health services [19]. The availability and ownership of healthcare facilities across the country have been reported to include 1607 governmental facilities, 1277 private for-profit facilities, 245 mission and 91 quasi-governmental facilities [20]. …”

How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach

“…Since 2000 studies have been undertaken in a number of countries, including Angola [2], Namibia [3], Botswana [4], Zambia [5] Kenya [6, 26], Benin [27], Burkina Faso [28], Ethiopia [29], Ghana [30–32], Nigeria [33], Seychelles [34], Sierra Leone [35, 36], South Africa [7, 37], Uganda [18] and Eritrea [38]. These studies demonstrate that DEA is an important tool for policy advice.…”

Measuring Technical Efficiency of Hospitals Using Data Envelopment Analysis: A Review

“…In the USA a study conducted to calculate technical efficiency for 322 federal hospitals was used BCC, VRS, input-oriented, and the total combination number of inputs and outputs was 6 (J. P. Harrison & Meyer, 2014). A study conducted in Ghana to examine technical efficiency for 128 public, mission, quasi-government, and private hospitals was applied BCC, VRS, output-oriented, and the total combination number of inputs and outputs was 8 (Jehu-Appiah et al, 2014). In Saudi Arabia a study conducted to assess technical efficiency for 20 public hospitals was used CCR and BCC, CRS and VRS, Input-oriented and International Journal of Public Health and Clinical Sciences e-ISSN : 2289-7577.…”

“…In regard to return to scale, there were studies used VRS these studies were (Ali et al, 2017), (Kakeman et al, 2016), (Kalhor et al, 2016), (Mujasi et al, 2016), (H. Li & Dong, 2015), (Applanaidu et al, 2014), (J. P. Harrison & Meyer, 2014), (Jehu-Appiah et al, 2014), (Jat & Sebastian, 2013), (Yusefzadeh, Ghaderi, Bagherzade, & Barouni, 2013). For CRS model, the studies were shared in this model type were (N. Li, Wang, Ni, & Wang, 2017), , (Cheng et al, 2016).…”