Ngobeni Victor, Breitenbach Marthinus C, Aye Goodness C
Department of Economics, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria, 0002 South Africa.
Cost Eff Resour Alloc. 2020 Jan 28;18:3. doi: 10.1186/s12962-020-0199-y. eCollection 2020.
Forty-nine million people or 83 per cent of the entire population of 59 million rely on the public healthcare system in South Africa. Coupled with a shortage of medical professionals, high migration, inequality and unemployment; healthcare provision is under extreme pressure. Due to negligence by the health professionals, provincial health departments had medical-legal claims estimated at R80 billion in 2017/18. In the same period, provincial health spending accounted for 33 per cent of total provincial expenditure of R570.3 billion or 6 per cent of South Africa's Gross Domestic Product. Despite this, healthcare outcomes are poor and provinces are inefficient in the use of the allocated funds. This warrants a scientific investigation into the technical efficiency of the public health system.
The study uses data envelopment analysis (DEA) to assess the technical efficiency of the nine South African provinces in the provision of healthcare. This is achieved by determining, assessing and comparing ways that individual provinces can benchmark their performance against peers to improve efficiency scores. DEA compares firms operating in homogenous conditions in the usage of multiple inputs to produce multiple outputs. Therefore, DEA is ideal for measuring the technical efficiency of provinces in the provision of public healthcare. In DEA methodology, the firms with scores of 100 per cent are technically efficient and those with scores lower than 100 per cent are technically inefficient. This study considers six DEA models using the 2017/18 total health spending and health staff as inputs and the infant mortality rate as an output. The first three models assume the constant returns to scale (CRS) while the last three use the variable return to scale (VRS) both with an input-minimisation objective.
The study found the mean technical efficiency scores ranging from 35.7 to 87.2 per cent between the health models 1 and 6. Therefore, inefficient provinces could improve the use of inputs within a range of 64.3 and 20.8 per cent. The Gauteng province defines the technical efficiency frontiers in all the six models. The second-best performing province is the North West province. Other provinces like KwaZulu-Natal, Limpopo and the Eastern Cape only perform well under the VRS. The other three provinces are inefficient.
Based on the VRS models 4 to 6, the study presents three policy options. Policy option 1 (model 4): the efficiency gains from addressing health expenditure wastage in four inefficient provinces amounts to R17 billion. Policy option 2 (model 5): the potential savings from the same provinces could be obtained from reducing 17,000 health personnel, advisably, in non-core areas. In terms of Policy option 3 (model 6), three inefficient provinces should reduce 6940 health workers while the same provinces, inclusive of KwaZulu-Natal could realise health expenditure savings of R61 million. The potential resource savings from improving the efficiency of the inefficient provinces could be used to refurbish and build more hospitals to alleviate pressure on the public health system. This could also reduce the per capita numbers per public hospital and perhaps their performance as overcrowding is reportedly negatively affecting their performance and health outcomes. The potential savings could also be used to appoint and train medical practitioners, specialists and researchers to reduce the alarming numbers of medical legal claims. Given the existing challenges, South Africa is not ready to implement the National Health Insurance (NHI) Scheme, as it requires additional financial and human resources. Instead, huge improvements in public healthcare provision could be achieved by re-allocating the resources 'saved' through efficiency measures by increasing the quality of public healthcare and extending healthcare to more recipients.
在南非，4900万人，即5900万总人口中的83%，依赖公共医疗系统。再加上医疗专业人员短缺、高移民率、不平等和失业问题，医疗服务面临着巨大压力。由于医疗专业人员的疏忽，2017/18年度省级卫生部门面临的医疗法律索赔估计达800亿兰特。同期，省级卫生支出占省级总支出5703亿兰特的33%，即南非国内生产总值的6%。尽管如此，医疗成果却很差，各省在使用分配资金方面效率低下。这就需要对公共卫生系统的技术效率进行科学调查。
本研究使用数据包络分析（DEA）来评估南非九个省份在提供医疗服务方面的技术效率。这是通过确定、评估和比较各个省份与同行进行绩效对标以提高效率得分的方式来实现的。DEA比较在相同条件下使用多种投入来生产多种产出的企业。因此，DEA非常适合衡量各省在提供公共医疗服务方面的技术效率。在DEA方法中，得分100%的企业在技术上是有效的，得分低于100%的企业在技术上是无效的。本研究考虑了六个DEA模型，以2017/18年度的卫生总支出和卫生工作人员作为投入，婴儿死亡率作为产出。前三个模型假设规模报酬不变（CRS），而后三个模型使用规模报酬可变（VRS），两者均以投入最小化为目标。
研究发现，在模型1至6中，各省的平均技术效率得分在35.7%至87.2%之间。因此，效率低下的省份可以在64.3%至20.8%的范围内提高投入的使用效率。豪登省在所有六个模型中都定义了技术效率前沿。表现第二好的省份是西北省。夸祖鲁 - 纳塔尔省、林波波省和东开普省等其他省份仅在VRS模型下表现良好。其他三个省份效率低下。
基于VRS模型4至6，本研究提出了三个政策选项。政策选项1（模型4）：解决四个效率低下省份的卫生支出浪费问题所带来的效率提升达170亿兰特。政策选项2（模型5）：通过在非核心领域削减17000名卫生人员，可从这些省份获得潜在节省。就政策选项3（模型6）而言，三个效率低下的省份应削减6940名卫生工作者，而包括夸祖鲁 - 纳塔尔省在内的这些省份可实现6100万兰特的卫生支出节省。提高效率低下省份的效率所带来的潜在资源节省可用于翻新和建造更多医院，以减轻公共卫生系统的压力。这也可以减少每个公立医院的人均数量，而且据报道，过度拥挤对医院的绩效和医疗成果有负面影响，或许还能改善医院的绩效。潜在节省还可用于聘请和培训医生、专家和研究人员，以减少令人担忧的医疗法律索赔数量。鉴于现有的挑战，南非尚未准备好实施国家健康保险（NHI）计划，因为这需要额外的财政和人力资源。相反，通过重新分配通过效率措施“节省”的资源，提高公共医疗服务质量并将医疗服务扩展到更多接受者，可以在公共医疗服务提供方面取得巨大改善。
