Cost and Efficiency Analysis in Iranian Primary Health Centers: A Micro Costing and Data Envelopment Analysis

Abstract

Background: As the first experience in Public-Private Partnerships, the Health Complex Model was implemented to provide primary health care services in urban, especially in slum areas. As a pilot at a provincial level, Chamran Health Complex offers healthcare for more than 57000 residents of Tabriz, the capital city of East Azerbaijan province. Despite the necessity of cost information in healthcare decision-making, there was limited knowledge about the unit cost of services. This study aims to analyze the cost and efficiency of Health Centers affiliated with Chamran Health Complex.

Methods: Activity-Based Costing method with direct and step-down allocation methods was adopted. Service throughput was gathered from the Iranian Integrated Health Record Portal and cost information from organization accounting records. We estimated unit costs in a hypothetical scenario according to national standards to quantify the gap between current and standard practice. Input-oriented Data Envelopment Analysis was administered to measure the efficiency of health centers.

Results:The total cost of the complex was $2841897, of which 67% ($1910373) and 33% ($931523) were accounted for direct and indirect costs, respectively. Among 80 service types, the vaccination center had the lowest ($9), and the occupational health center had the highest average unit cost ($76). The average technical efficiency of the health centers was 0.519, where the HC1 and HC3 showed the best performance compared to their counterparts.

Conclusions: There is remarkable variability in service costs across health centers, which must be addressed in performance management and contracting practices. Although we found a gap between current and standard practice in terms of staff and facilities according to national standards, Chamran Health Complex has an untouched capacity that can be utilized with better planning and without incurring additional costs. It raises the need for revising national standards by the Iran Ministry of Health.

Background

The increasing cost of healthcare is one of the most important reasons for inadequate access to essential health services (1). Health systems worldwide have tried to deal with the issue by prioritizing primary healthcare services. Given the challenges regarding the quality and efficiency of health care (2), the Iranian health system has attempted to address the issue by initiating a series of health sector reforms, including the pilot of the Family Physician Program (FPP) in urban areas (3). Since its establishment in 2005, the program was commissioned to prevent unnecessary specialist visits, control the over/underutilization of services and improve access to the proper care in cities with a 20000 population (4). Several challenges have been attached to the FP program that put other reforms on the table for the Iranian health system (5). According to Takian et al., the FP was built up based on a public sector mindset, raising the government's share in healthcare provision (6). This arrangement not only was against Iran’s fourth development plan but also has limited opportunities for private or non-governmental partnerships. In response to these criticisms, Tabriz University of Medical Sciences launched a new Public-Private Partnership PHC model called Health Complexes (HCs) in 2013. The program aimed to expand population coverage, benefits package, and financial protection through efficient management of service provision by public and private partnerships. While many considerations have been taken before the establishment of the HCs, its accounting system was built up based on public sector accounting principles, which can only identify and record the current expenses of an organization. This system might not always reflect the opportunity cost of the resources necessary to assess its economic performance and the value of money provided (7). 

Cost analysis and efficiency measurement are widely used economic tools that guide decision-making regarding the best use of healthcare resources (8). Activity-Based Costing (ABC) has been recommended by the panel of specialists and effectiveness evaluators as the best choice approach for costing purposes (9). ABC has been classified as a “top-down micro-costing” method in the health economics literature because of its focus on activities and accurate assigning overhead costs to final products (10). This highly cited costing method requires the health providers to collect sophisticated data about the step-by-step process and attach the activities performed in delivering services to each approach. Finally, the overhead costs are first allocated to cost pools, then traced to cost objects (11).

Despite the necessity of detailed cost information in economic decision-making (12), most of the literature in Iran has adopted gross costing methods or top-down methods, even claiming it is ABC (13). The findings of these studies may not be transferred to the Tabriz HCs setting because of the methodological and transferability issues such as reporting costing results and heterogeneity in cost object selection. For example, most studies did not provide sufficient details about cost components and process costs, so it is impossible to attach the cost data to cost objects(14). Due to these challenges, the current study aimed to calculate the cost of delivered services in five Chamran Health Complex (CHC) health centers through the ABC method and assess these centers' economic performance through Data Envelopment Analysis (DEA). 

DEA is a dynamic, rigorous, and leading method in measuring efficiency and productivity which is mainly used in public and non-profit organizations, particularly in private entities whose price information is not available or reliable. This method determines whether the desired decision-making unit is placed on the efficiency line or not. In this way, efficiency and inefficiency units are separated. Also, in this method, the output can be maximized based on determined inputs, or the inputs can be minimized according to the specified output  (15-17). The study findings can be used in cost management and budget setting practice and as a basis for further economic analysis.

Methods

Chamran Health Complex (CHC) is an integrated health center that provides primary health care for more than 57000 householders under the urban coverage area of Tabriz city. The administrative office manages the complex activities through 5 health facilities (HC1, HC2, HC3, HC4, and HC5) throughout the Akhmaghaye district to ensure fair access to healthcare. More than 83 services ranging from GP visits, vaccination, disease control and surveillance, dental care, and a limited sort of specialist visit were provided by the Complex. The Tabriz University of Medical Sciences proposed this project to produce evidence regarding the Complex's cost profile and economic performance as a first launched PPP program providing primary healthcare services. 

The project was conducted in 4 steps. First, the team engaged with stakeholders to assess the ongoing data sources or patient records to determine which data collection method would provide the best-quality information. Data for service activities and process maps, time and workload, human and financial resources, equipment, and consumables were collected using paper-based forms or excel datasheets. To characterize the analysis unit, we categorized service departments into overhead, intermediate and final activity centers according to their role in service provision. The last activity center directly contacts patients to provide requested services. Intermediate activity center supports final activity centers during the care process; even it can provide intermediary services, for example, laboratory, radiology, cash, and bill wards. Overhead activity centers support intermediate and final activity centers; they are not in direct contact with patients while facilitating service provision. Second, the project team held several meetings at the activity centers and conducted face-to-face interviews with providers and staff to develop a logbook for each activity center. It included information about the staff time, equipment modality and cost, room dimensions, service modality, process map, time, and frequency of delivered services in the study year. Second, we measured cost items within activity centers by adopting a micro-costing approach which identifies resources at very detailed level. Data for cost items (staff, capital investment, consumables, and energy consumption) were collected from Accounting Information System. Utilization data were collected from the Iranian Integrated Health Record Portal (SIB: HTTPS://sib.tbzmed.ac.ir).

Where data was not available, we used expert opinion, direct observation, and log forms for recording needed data. During the third step the overhead costs were allocated by means of two different methods; Direct, and Step-down allocation (18). In direct allocation, the total costs of overhead and intermediate activity centers are directly allocated to the final activity centers, apart from their interactions. However, the step-down method allocates one overhead activity center’s cost, for example IT, to another overhead or intermediate activity center, for example HR or Laundry, which is then allocated to the final activity centers (19).  Considering that, the unit cost which is calculated from previous steps reflects ongoing, but not standard practice arrangement, we hypothesized a standard scenario in which the current gap in staff and facilities assumed to be filled in accordance with the Iranian Ministry of Health standards for medical staff and organizational structure. Then, all the calculations were re-executed to determine the optimum unit costs (20). We used Purchasing Power Parity (PPP) 2015 to adjust unit costs in national currency and reported in international dollars (21). In the fourth step, the economic performance of five health centers was assessed through Data Envelopment Analysis. To estimate the technical efficiency, the input minimization approach with assumption of variable return to scale was adopted pursuing below linear programming.

In general, three types of efficiency can be distinguished from each other: First, technical efficiency: means that minimum resources are used to produce a particular product. The fundamental question in this type of efficiency is whether the health center's minimum personnel, tools, and equipment have been used to produce the output. Technical inefficiency can be caused by the lack or improper use of health center resources. Second, scale efficiency: faces planning issues regarding the number of resources and the size of the center. Third, managerial efficiency seeks to increase output with proper management and personnel effort (22). Also, Return to scale shows the rate of increase in production provided that all other resources are equally increased. Net technical efficiency (managerial efficiency) was assessed by dividing technical efficiency (in VRS state) to scale efficiency. The inputs used in the model were salary and wage, equipment expenses, building expenses, and covered population, while the outputs were the total time spent on service provision (23). Costing was performed in Microsoft Excel 2013, and efficiency analysis by Deap_2.1. 

Results

From October 2016 to June 2017, data was collected from five Health Centers (HC1-5) with at least 1000 unique observations per center. CHC comprises five centers that serve more the 57000 population with a wide variety of preventive care, outpatient visits, and para clinical services without a more affordable charge. The complex spent $2841897, consisted of staff (80%), building (4.8%), equipment (2.1%), consumables (2.8%), energy (0.75%) and miscellaneous[1] (9.6%). HC1 absorbed the highest per capita budget ($70) among five health centers, while this share for HC2 was $20. There was great heterogeneity in calculated unit costs among health centers; for example, the unit cost of a Physician visit in HC2 was $15, compared to $38 (60% higher) in HC5. In general terms, the unit cost of services provided by HC1 is almost lower in 10 sample services. In contrast, the HC5 and HC4 generated unit cost values more than average in direct and step-down allocation methods (Table 1, Figure 1).  

Table 1: Cost components in five Health Centers of Chamran Health Complex ) PPP $(

Discussion

We calculated the unit cost of services delivered in five health centers affiliated with the Chamran Health Complex (CHC), known for the first Public-Private Partnership (PPP) program in primary health care. The total budget allocated to the complex in the study period was $2841897, of which $1910373 (67%) was directly assigned to cost objects and the rest, $931523 (33%), allocated based on appropriate cost drivers. The unit cost of 10 sample services was reported and compared to whether there was no statistically significant difference between average unit costs in terms of overhead cost allocation approaches. 

The heterogeneity in unit costs across five health centers might depend on the variation in the infrastructure or the performance of these centers. Understanding and managing the source of variabilities is essential; first, it helps managers trace inefficient points and address them by implementing productivity enhancement or cost containment strategies. Second, since the provision of services in the form of Health Complexes is the first PPP experience, the costing findings can shed light on fundamental differences in unit costs which can support decisions concerning the outsourcing and PPP contracts (24). 

According to the World Health Organization report (2000), two-thirds of total costs typically belong to human resources. Likewise, a growing body of literature reported a 60 to 65% personnel cost rate (25). This proportion is much higher in all five health centers highlighting the impact of human resources on final unit cost. Notwithstanding the high personnel costs, our results showed a gap between current human resources and the Iran Ministry of Health’s standards. If the complex wishes to fill the gap, $160317 additional spending sounds necessary. These further costs were of particular concern, as the efficiency analysis demonstrated untouched capacity in all health centers; more spending seems not to be justifiable. We believe that the Iran Ministry of health’s national standards in human resources could be revised according to the health needs and service utilization at the local level.  

According to the scenario analysis, investment in human resources and infrastructure according to MOH’s national standards will increase the unit cost by 36.4%. The highest 189% would influence the communicable disease department on average. Since the staff skills in this department are the same as Family Health and Vaccination departments, it seems that merging these departments would result in lower overhead costs and, consequently, unit costs. 

Comparing the unit cost of services across health centers showed considerable variability in almost 70% of services. Part of this can be managed by proper planning, for example, job standardization and resource consumption management. However, part of these differences is due to the heterogeneity in sociodemographic characteristics and the geographical location of health facilities. For example, prolonged service time for the elderly or environmental health services in remote areas can produce higher unit costs. This variability needs to be reflected in budget allocation, reimbursement, and payment decisions. Previous studies highlighted such variability, which geared the establishment of new methodologies to estimate adjusted capitation payments. (26-29)  

Exploring how allocation methods impact final unit costs, we detected that the average unit cost calculated from direct allocation methods was not different from the unit cost calculated from the step-down adjustment. This finding aligns with Carreras et al. (30), which concluded that the observed variation in disease cost depends mainly on direct costs, regardless of the cost allocation methodology. Unlike researchers interested in using the direct allocation method because of timing issues and its easiness, a comparison of services across health centers showed that the step-down allocation method seems to be more sensitive to resource consumption by cost centers and able to breakdown indirect costs into final services more accurately. 

The mean technical efficiency measured 0.519, indicating inefficiency in health centers. Health centers have the potential to increase their efficiency by %48 with better management and performance without incurring additional costs. According to DEA results, 40% of health centers showed a constant return to scale, implying that a unit increase in inputs leads to an equivalent rise in output. The remaining 60% of health centers showed an increasing return to scale (output increases by more than the proportional increase in inputs). The managerial efficiency of the health centers was 0.893. This rate means that efficiency can be increased by 11% without increasing inputs and relying only on administrative decisions and employees' efforts. The mean scale efficiency of the studied health centers was 0.546, and the scale efficiency score is less than one implies that the health centers are not operating at optimal scale or size.

Conclusion

There is a striking variability in service costs across health centers that must be addressed in performance management and contracting practices. Despite the existing gap between current and standard practice in terms of staff and facilities, which calls for extra investment in personnel and equipment recruitment, efficiency analysis highlights inefficiency in the Health Complex, which could be resolved by better planning without additional spending. Such a discrepancy raises the need for revising current national standards to ensure more efficient performance. It is worth noting that the newer costing methods like Activity Based Costing can realize the inefficient points in resource consumption by different processes in primary health care and guide better resource allocation decisions.

Declarations

Ethics approval and consent to participate: Ethical approval for this study was obtained from the Ethics Committee of Tabriz University of Medical Sciences in January 2018. TBZMED.REC.1394.981

Consent for publication: Not applicable since the study does not contain any person’s data.

Availability of data and materials: The data used to support the findings of this study are available from the corresponding author upon request. 

Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding: This work was financially supported by Tabriz University of Medical Sciences.  

Authors’ contributions: This project was carried out in collaboration with all authors. Authors Alireza Mahboub-Ahari and Somayeh Khanlari contributed to the study protocol design. Author Somayeh Khanlari conducted the interviews and data collection. Alireza Mahboub-Ahari, Hasan Yusefzadeh, and Alireza Ghorbani participated in the data analysis and interpretation of results. All of the authors participated in manuscript redaction. They read and approved the final manuscript. 

Acknowledgments: Authors would like to express their special thanks and gratitude to the Health Deputy staff in TUoMS, particularly those working in Chamran Health Complex. We respect and thank all research staff at the School of Management and Medical Informatics of TUoMS.  

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