There are several limitations to this study. First, the potential population coverage expansion estimates and associated increased service utilization related to increased technical efficiency assume that public facility user-fees will be paid under a social health insurance scheme. The projected cost of additional user-fee payments to expand the Health Equity Fund to the uncovered 1st–3rd wealth quintile people (approximately 3.5 million people) are modeled to range from US$ 23.0 (using current HEF payment rates) to 36.5 million (using NSSF payment rates), assuming the adoption of several complimentary policy options [17].
Second, the study does not assess performance relating to high-level health outcomes such as morbidity, mortality or life-expectancy. However, given the overall low utilization of public health services and the plethora of factors which impact on such outcomes, the public health system’s contribution is likely limited and very difficult to measure. In addition, there is very limited data available that measure such outcomes at the provincial-municipal level.
Third, this study does not evaluate the efficiency of private sector services which are the predominant provider as comparable data is not available. However, the study does assess the impact of private providers on public health service efficiency by including several related variables in the second stage analysis. In addition, this study does not assess technical efficiency among individual public health facilities. Although this approach is possible if the data is available our focus is on the provincial-municipal level where management responsibility has recently been delegated. The study estimates grouped hospital and grouped health center technical efficiency at the provincial-municipal level. With the exception of specialized national hospitals, this approach captures the full range of public health services and aims to provide a better understanding of where to focus efficiency improvement efforts within each province.
Fourth, we find that for public health facilities to be fully efficient service output would need to increase by 34% and 73% for hospitals and health centers, respectively. It is important to note that DEA is only appropriate to compare like units (i.e. aggregated hospital services are compared with other aggregated hospital outputs and aggregated health center services with other aggregated health center services). Therefore, the result cannot be interpreted to mean that the primary care services provided by Cambodian public health centers are less cost-effective compared to Cambodian public hospitals. Rather, the results of this study show that in Cambodia there is more variation in technical efficiency among health center service output grouped at the province-municipality level (compared to hospitals) and therefore more opportunity to increase outputs and service space for the services they provide.
Fifth, comparative service-type unit costs do not exactly align with the social health insurance payment categories. We address this issue by generalizing unit costs which include the specific reimbursement category. In addition, published unit cost data is limited to health facilities in three provinces. To increase the reliability of the estimates we also compare unit cost estimates with updated data from ongoing data collection of a high-quality costing study. In addition, the model outputs are limited to the major service categories. Two additional service payment categories (i.e. emergency services and long-acting family planning methods) were initially included in the models as these services have different payment rates (with utilization/claims data recorded by the social health insurance mechanisms). However, the Ministry of Health does not separate these services in the provincial-municipal aggregated data and the assumptions required to model utilization for these services among the general population yielded inconsistent results. In addition, the discriminatory power of DEA is constrained when there is a large number of inputs and outputs and a small number of decision-making units. Limiting the model to the essential components of the service production process is considered a best practice [51].
Finally, this study does not assess system-wide reforms that could further improve cost efficiency or financial savings such as pooling health insurance funds and merging schemes, improving procurement to lower the purchase cost of pharmaceuticals, consumables, equipment and supplies, and reducing overhead [6, 57]. Such measures could increase budgetary space for health providing that they are well-defined and public financial management systems enable such gains to be repurposed toward prioritized health needs [58]. This topic is further discussed below.
This study assesses public health service technical efficiency at the provincial-municipal level. The results reconfirm under-utilization of public health services and quantify the potential to improve efficiency by expanding social health insurance population coverage with current supply-side financing. These findings are consistent with other empirical studies. Ensor et al. found HEF to be associated with higher public health facility efficiency [11]. A recent costing study found that most health facilities make a minor surplus suggesting that they could increase the number of patients without running a loss [31]. Jacobs et al. note that service volume along with contextual factors such as poverty incidence, population density and accessibility affect unit costs [13].
There is mixed evidence as to if the HEF increases public health service utilization due to issues with gaps in financial risk protection, general low utilization of public providers, and deficient eligibility targeting [10]. However, a direct comparison of utilization rates by service level and type demonstrates that public health service utilization among HEF beneficiaries is generally higher compared to the rest of the population. This provides evidence that Cambodia’s largest social health protection scheme improves access. Notwithstanding, it is important to note that utilization data does not capture effectiveness or quality of the service provided [59].
There is some evidence that fee-for-service reimbursements, the system used by Cambodia’s social health protection schemes, may contribute to oversupply as it incentivizes service provision [60,61,62,63]. However, provider remuneration is complex and there is also evidence that the risk of overprovision is contextual ([64, 65]. Moreover, health service utilization rates in Cambodia are considered low compared with other Asian countries [66].
This study estimates the potential supply-side ‘service space’ for 4.69 million additional social health protection beneficiaries in a fully efficient public health system. This could raise total population coverage of social health insurance to 60% while leveraging the unutilized service capacity of the public health system. However, this still leaves a population coverage gap of 40%. The gap is worrisome given the expected decline in out-of-pocket spending on healthcare due to pandemic-related economic hardship which will need to be offset with public financing [67]. Additional investments in the health system can ensure access to needed health services, particularly among the financially vulnerable [17]. For Cambodia this would imply an increase in government health expenditure of 0.6% of GDP [67]. Although policymakers may raise concerns about adding budget to an inefficient system, there are several smart investments to promote continuous health system efficiency improvement. These include the prioritization of primary health care, strategic purchasing, alignment of financing and delivery, better accountability through results-based outcome and output contracts and related provider incentives, decentralization, moving care out of hospitals, and independent regulatory agencies [57, 58, 68,69,70]. For example, one simple measure would be to link all social health insurance provider payments to both service provision and health facility quality scores [50]. Evidence shows that government health expenditure as a percentage of total health expenditure (i.e. inclusive of out-of-pocket expenditure) is positively associated with efficiency [71]. The expansion of social health protection, particularly to the financially vulnerable, can support economic recovery by enabling households to maintain productivity, thereby stabilizing household income and expenditure.
Finally, efficiency gains need to be reinvested to provide an incentive for continuous health system performance improvement [58]. To effectively address public health service inefficiency, provincial-municipal administrations need to be given adequate flexibility to reallocate resources to increase the volume or quality of the most efficiently delivered services [58]. In addition, predictable financing to sub-national governments is imperative to improve health service performance (Gertler, Giovagnoli and Martinez 2014).
The second stage analysis identifies several factors which explain the variation in technical efficiency among the provinces. High service volume hospitals are generally considered to be associated with better outcomes and economies of scale [72]. However, this study did not find utilization rates to be a significant explanatory factor of hospital efficiency. This may be attributable to negative spillover effects whereas increased volume in one service area may be associated with increased cost in another area [73]. By contrast, health centers offer a much more limited range of services, and therefore less potential for negative spillovers. This study did find a small, but highly significant positive relationship between health center utilization and technical efficiency.
The finding that provinces with the lowest and highest quality scores have higher technical efficiency suggests that among provinces with lower health facility quality scores, some improvements in quality may decrease technical efficiency, potentially indicating that the initial investments in quality such as training and facility upgrades increase costs and/or decrease service output. Similarly, it could also indicate that facilities with the lowest quality scores are underfinanced and therefore do not invest in quality improvement measures but have high patient volume which yields higher technical efficiency scores. This suggests that provincial-municipal level public health facilities need to attain a quality score critical threshold of about 70%-80% before quality improvement can contribute to technical efficiency. A study of Portuguese public hospitals found that good clinical safety practices tend to be associated with low technical efficiency, concluding that there are trade-offs between efficiency and quality [74].
The nonlinear relationship between small-scale private providers and public health centers suggests that there may be service complementarity between the sectors when the number of private providers is limited. However, the overall marginal negative effect of private sector providers on public health service technical efficiency is likely due to competition which reduces the number of patients seeking public sector care. Moreover, the dominance of the largely unregulated, pro-rich private sector accounts for a significant proportion (57.5%) of out-of-pocket spending [75, 76]. As private health services and health insurers can exacerbate health inequity, it is essential for countries to determine the appropriate level of privatization in their systems which necessitates transparent and responsible regulation alongside efforts to improve public system efficiency [77].
The marginal negative effect of supply-side resources/financing on technical efficiency suggests that increased financial autonomy and demand-side financing may yield better value for money. Health financing should focus on smart investments discussed above such as increasing social health insurance payments.
The finding that the hospital utilization has a large, statistically significant negative effect on health center technical efficiency suggests that patients bypassing health centers and going directly to hospitals is an issue. This is consistent with other research in Cambodia which found that primary care facilities are regularly bypassed due to a lack of key personnel, stock-outs of essential drugs and substandard quality of care [31]. This may be redressed by correcting the underlying causes and incentivizing health center referrals such as prioritizing service provision at hospitals for patients with a formal health center referral.
There are likely additional or secondary factors which contribute to public health system underutilization. For example, systematic factors can lead to patient avoidance of public facilities due to quality perceptions including competency and attitude of providers [78]. Another factor is limited service availability, particularly for non-communicable diseases [10]. Moreover, it is also possible that patients may avoid public care-seeking due to unofficial fees or face substantial indirect financial shocks relating to needed medical care and/or lost productivity [20].
We calculate unit costs using the DEA Aumann-Shapley applied cost allocation approach. To the best of our knowledge, this study is the first time this method has been used for health services costing. The results are comparable with recent, high-quality public health facility costing data, and we believe this approach to be a good alternative to traditional costing studies which can be labor intensive, time consuming, and expensive.
The approach is not without limitations however. First, the number of costing categories is limited as a function of the DEA model. This issue could be mitigated to a degree by increasing the number of decision-making units. For example, if data is available, more robust results would be expected by using hospitals and health centers as the primary unit of analysis (as opposed to grouping them by province-municipality). Second, this Aumann-Shapley analysis used previous costing study results for weighting. Although it is common to rely on existing data to parameterize cost models, it requires that such data exist.
Health provider payments can incentivize or de-incentivize particular services. The Health Equity Fund payment rates are inconsistent with the estimated cost of service provision across the major payment categories. The wide variability (6% - 255%) of payments as a proportion of the estimated unit costs suggests that payment rates should be realigned. In particular, the higher than cost reimbursements for hospital surgeries is notable. Given minor surgeries at the hospital level are 7.3 times higher among HEF beneficiaries compared with the rest of the population, the overpayment may be creating a perverse incentive and service overutilization. In fact, there is evidence of both public and private healthcare facilities providing surgeries for commercial interest [79].