Analysis of factors influencing technical efficiency of public district hospitals in KwaZulu-Natal province, South Africa

Abstract

Background District hospitals are crucial in supporting primary health care and serve as a gateway to more specialist care through a referral system. Majority of South Africans access health care services through the public sector district health system. Given the enormous task assigned to the public district hospital within the country, this study examined factors influencing their technical efficiency. Method Data were collected for 38 public district hospitals in KwaZulu-Natal province from 2014/15 to 2016/17. Data envelopment analysis (DEA) was used to determine the technical efficiency of the hospitals, adopting both the constant return to scale (CRS) and variable return to scale (VRS) models. Tobit regression model was used to determine factors related to the technical efficiency of the district hospitals. Results This study showed that a significant proportion of the district hospitals were technically inefficient. The Tobit regression model identified catchment population, the proportion of inpatients treated per medical personnel, the proportion of inpatients treated per nursing personnel and expenditure per patient day equivalent as factors influencing technical efficiency of the district hospitals. Conclusion Findings from this study suggest that the technical efficiency of the district hospitals can be enhanced through an effective referral system and improved peoples’ health-seeking behaviour. In addition, a standard mix of clinical staff toward efficient service delivery and periodic cost analysis of health services with the view to saving cost and maintaining the quality of health care should be considered.

Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

“…Technical Efficiency (TE), θ CCR = Pure Technical Efficiency (PTE), θ BCC x Scale Efficiency The Fig 2 illustrates geometrically the concepts of efficiency measurement in DEA Twenty nine percent (26 of 89, 29.21%) of the included studies measured the (overall) Technical Efficiency (TE) [77–102] and 26.97% (24 of 89) [75,76,103–124] used TE, PTE and SE in their evaluation. While the rest of the studies evaluated their efficiency by the combination of TE and PTE and some not stated clearly the type of efficiency measured (for details see Appendix C).…”

“…In other words, either output expansion or input decrease is the primary evaluation goal. More than half of the studies (55.06%, 49 of 89) [46–48,50–54,58,60,64–69,71–73,75–77,80,91–94,96,98,99,103–107,109–111,113–116,118,121,124,127,128,130,135] applied input-orientated DEA models. The researchers indicated that the choice of input orientation was made to be in line with the fact that the majority of healthcare units aim to minimize inputs given a goal level of output.…”

“…Most frequently, the size, capacity, or functioning of a hospital as a health service is determined by the number of fully staffed hospital or operational beds. 75 of the 89 (84.27%) studies included the number bed (i.e., general, ICU, special) as input in their analysis [48–55,57–62,64–74,76,78–93,95–101,103–109,112–116,118,119,121–125,128–131,133–135]. Seven of these 75 studies (9.33%) utilized variation of multiple beds as their inputs either by type of bed, bed cost, or ratio of bed [61,66,71,78,98,101,129].…”

“…Seven of these 75 studies (9.33%) utilized variation of multiple beds as their inputs either by type of bed, bed cost, or ratio of bed [61,66,71,78,98,101,129]. While 12 of these 75 (16.00%) studies combine both beds and capital assets as their capacity-related input [70,78,85,89,90,101,103,106,109,122,125,133]. One study only used capital assets as their input but combine with cost-related assets, this may explain why this study did not use beds as part of input like other studies [56].…”

“…Among the studies, 80 (89.89%) conducted extended analysis in their research [46–56,58–64,66–69,71–77,79–94,96–98,101–116,118–125,128–135]. The type of data applied were almost equally distributed among the studies 51 (57.30%) [47,49,52,58–60,67,70–73,75–77,79,80,83–85,88,91,92,94,96,97,100,101,103,105–109,111,112,114–116,120–122,124,125,127,129–135] used panel data and 38 (42.70%) [46,48,50,51,53–57,61–66,68,69,74,78,81,82,86,87,89,90,93,95,98,99,102,104,110,113,117–119,123,128] used cross sectional data in their research. Forty extended analyses were identified within the included studies.…”