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Technical and scale efficiency of public hospitals in Papua New Guinea
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The objective of the study was to estimate the relative technical efficiency of public hospitals in PNG and estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient using Data Envelopment Analysis for 20 hospitals in PNG using the data for 2017. The study is further extended using the Tobit regression analysis to investigate the institutional and contextual factors on hospital efficiencies.
The results of the output-oriented DEA BCC Model indicated that the average constant returns to scale technical efficiency score was 79%; the average variable returns to scale technical efficiency score was 92%; and the average scale efficiency score was 86%. The 7 inefficient hospitals need to increase the overall outpatient visits by 31%, and inpatient discharges by 27% to be efficient. Alternatively, inefficient hospitals could also have improved their relative efficiency by reducing their overall beds by 19%, doctors by 34%, nurses by 13%, and other staff by 31%. The pattern of scale efficiency showed that out of 7 inefficient hospitals, 2 hospitals operated under increasing returns to scale while 5 hospitals operating under decreasing return to scale.
For this study, the focus is on the technical and scale efficiency score, which could be used for hospital planning and management policies. The Tobit regression model result indicated that the ratio of bed to nurses (RoBTN) is significant and positively correlated with inefficiency scores. In other words, a unit increase in RoBTN will increase the inefficiency of hospitals by 19%. It is noted that additional nurses for the available beds to effectively minimize the inefficiency. Additionally, the ratio of bed to doctors (RoBTD) is significant and negatively correlated meaning available doctors need to be reduced to minimize the inefficiency. A better approach and policies are required for hospital management to allocate and shift doctors within the hospitals for better efficiency. Hospitals with more beds are significant and are negatively correlated with inefficiency scores. Additional beds are required to minimize the inefficiency for better and effective health services.
Public hospital improvement policies can have various links to resource allocation within and between hospitals, some reflected in demand for health personnel, and some in the types and skills available on the supply side. The study results illustrate part of the range of possibilities for selected components of the hospital management plan. Many effects, whether the direct effects that define the policy, indirect side effects, or conditionalities imposed directly or indirectly, will vary with the components and details of the plans. Therefore, there are no general statements about links to hospital inefficiency improvement based only on theoretical analyses of the technical and scale efficiency score. The interpretation of the findings for policy recommendations should be made with caution.
Title: Technical and scale efficiency of public hospitals in Papua New Guinea
Description:
The objective of the study was to estimate the relative technical efficiency of public hospitals in PNG and estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient using Data Envelopment Analysis for 20 hospitals in PNG using the data for 2017.
The study is further extended using the Tobit regression analysis to investigate the institutional and contextual factors on hospital efficiencies.
The results of the output-oriented DEA BCC Model indicated that the average constant returns to scale technical efficiency score was 79%; the average variable returns to scale technical efficiency score was 92%; and the average scale efficiency score was 86%.
The 7 inefficient hospitals need to increase the overall outpatient visits by 31%, and inpatient discharges by 27% to be efficient.
Alternatively, inefficient hospitals could also have improved their relative efficiency by reducing their overall beds by 19%, doctors by 34%, nurses by 13%, and other staff by 31%.
The pattern of scale efficiency showed that out of 7 inefficient hospitals, 2 hospitals operated under increasing returns to scale while 5 hospitals operating under decreasing return to scale.
For this study, the focus is on the technical and scale efficiency score, which could be used for hospital planning and management policies.
The Tobit regression model result indicated that the ratio of bed to nurses (RoBTN) is significant and positively correlated with inefficiency scores.
In other words, a unit increase in RoBTN will increase the inefficiency of hospitals by 19%.
It is noted that additional nurses for the available beds to effectively minimize the inefficiency.
Additionally, the ratio of bed to doctors (RoBTD) is significant and negatively correlated meaning available doctors need to be reduced to minimize the inefficiency.
A better approach and policies are required for hospital management to allocate and shift doctors within the hospitals for better efficiency.
Hospitals with more beds are significant and are negatively correlated with inefficiency scores.
Additional beds are required to minimize the inefficiency for better and effective health services.
Public hospital improvement policies can have various links to resource allocation within and between hospitals, some reflected in demand for health personnel, and some in the types and skills available on the supply side.
The study results illustrate part of the range of possibilities for selected components of the hospital management plan.
Many effects, whether the direct effects that define the policy, indirect side effects, or conditionalities imposed directly or indirectly, will vary with the components and details of the plans.
Therefore, there are no general statements about links to hospital inefficiency improvement based only on theoretical analyses of the technical and scale efficiency score.
The interpretation of the findings for policy recommendations should be made with caution.
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