Introducing optimal performance scenario for an Urban Comprehensive Health Center affiliated with Primary Healthcare Network in Iran

Abstract

Background

The Urban Comprehensive Health Center (UCHC) is a vital institution that offers healthcare services to the people residing in cities in Iran, and its efficient functioning is critical.

Objectives

This study aimed to introduce optimal performance scenarios for a UCHC using a new methodology to help managers make more efficient decisions.

Methods

This empirical study was conducted in 2021 at a UCHC in the south of Tehran City, Iran. The study comprised five phases: collecting information about the center, recording the number of patients and the time taken to provide services, analyzing the current state of the center, creating scenarios, and comparing scenarios to identify the best solution. We used Arena21 software to generate scenarios and quantitative criteria, the nominal group technique to analyze proposed decisions, and qualitative criteria and expert choice.

Results

We extracted three scenarios. The changes in quantitative criteria in the third scenario compared to the current situation were patients entered (94% increase), patients served (137% increase), patients’ waiting time (−107% decrease), and employee productivity rate (3% increase). Qualitative criteria were presented to validate the scenarios. The scores for the quantitative criteria for the current situation and the first, second, and third scenarios were 111, 141, 347, and 401, respectively. For the qualitative criteria, the scores were 116, 208, 216, and 458, respectively.

Conclusions

The third scenario was the optimal performance scenario. We recommend using simulation tools, extracting decision methods and proposed scenarios, validating scenarios, and making decisions about them for other UCHCs.

Introduction

Promoting public health is the primary objective of healthcare systems globally [1], with providing healthcare services and care being among their most critical functions [2]. Providing healthcare services is one of the most crucial functions of countries’ health systems for preventing, maintaining, and improving the well-being of their populations [3, 4]. The World Health Organization (WHO) has emphasized that all countries should prioritize providing healthcare services and ensure accessibility for all citizens [3]. While providing healthcare services varies across different countries, the low- and middle-income countries (LMICs) have similar similarities [5]. Iran is a lower middle-income country [6]. Similar to many LMICs, most people in Iran reside in urban areas, where the trend of urbanization is rapidly increasing [7, 8]. The growth of urbanization, particularly in metropolitan areas, has created several challenges across various domains [9], i.e. the demand for and delivery of healthcare services [10–12].

In 2023, over 70% of Iran’s population lived in urban areas [13], with the capital city of Tehran as the third largest metropolitan area in the Eastern Mediterranean region being home to more than 9 million people [14], facing various challenges, particularly in deprived southern districts. These include inadequate urban planning and management, incompleteness of infrastructure, and unforeseen events, which have exacerbated the challenges, faced by the vulnerable population in these areas [9]. Ray County, situated in southern Tehra [15], has a population exceeding one million. The area attracts a range of demographic groups, including immigrants, marginal residents, seasonal workers [16], religious tourists [17], and predominantly Afghan refugees [18], due to the presence of industrial centers, and historical and religious sites [19].

The amplification of the marginalized population and the epidemiological transition from communicable to non-communicable diseases (NCDs) necessitates preventive measures and extensive, proportionate healthcare services in Iran [12]. The implementation of the Health Transformation Plan (HTP) in 2015 reinforced Primary Health Care (PHC) and fortified the active care of the population in urban areas, particularly for NCDs [20]. Before the execution of the HTP, PHC did not actively cover urban areas [21, 22]. The need for special attention to access to healthcare and its conditions in urban areas, especially in the suburbs, is crucial [23]. Since the PHC Network is the primary provider of healthcare services [24], improving the performance of these provider units becomes crucial [25].

As depicted in Figure 1, the most peripheral operational units providing PHC services are called “Health Houses” at the rural level and “Health Posts” at the urban level in the Iranian healthcare system. These units operate under the supervision of rural and urban comprehensive health centers, respectively. Patients are referred to these centers or directly visit them if necessary [26, 27].

As provider of PHC services to urban populations, the Urban Comprehensive Health Centers (UCHCs) are critical in Iran’s healthcare system. UCHCs are responsible for covering 30 000 to 50 000 designated urban populations, with each center being monitored by the District Healthcare Network [28–30]. The primary objectives of UCHCs are to activate Health Posts and strike a balance between preventive and curative services [30]. These centers offer comprehensive services, including family (general) physician consultations, vaccinations, Pap smears, dental and oral public health services, environmental health services, occupational health services, nutrition and dietary counseling, and psychological counseling. The staff at UCHCs consist of general physicians, dentists, midwives, healthcare workers, nutrition experts, psychologists, environmental and occupational health experts, and paramedics, with some centers providing additional services such as obstetrics and gynecology, pediatrics, para-clinical services (laboratory and imaging), optometry, and audiometry, as per population-specific needs [22, 27].

The UCHCs, similar to any organization, need to enhance their performance to adequately serve a large and diverse population, which necessitates increasing their efficiency [31]. Improving performance in healthcare settings can be complex and multifaceted, with significant consequences for decision-making [32]. It is crucial to utilize tools that can predict the outcomes of potential decisions to mitigate these challenges, thereby reducing the risks associated with decision-making [33]. Simulation tools can provide a virtual representation of the healthcare setting, allowing busy and complex decision-makers to evaluate the impact of different decisions before implementation [34, 35]. The effectiveness of simulation tools in optimizing healthcare performance has been demonstrated through various studies [36–38].

Most performance simulation studies have been conducted in various healthcare settings, including hospitals [39, 40], emergency departments [41], paraclinics [42, 43], and clinics [44, 45]. This study, however, takes a novel approach by applying simulation optimization techniques to a primary healthcare center [46]. Additionally, it would be more appropriate to seek the perspectives of relevant experts to increase the accuracy and comprehensiveness of managerial decisions [47] to identify the necessary criteria for making those decisions. For this purpose, we needed to employ qualitative and quantitative methods [48]. Therefore, this study adopted a mixed-methods (quantitative–qualitative) approach.

Similar research has primarily targeted other areas of healthcare, neglecting the significance of PHC. Several factors, including extended wait times, subpar service quality, inefficient resource allocation, and insufficient focus on preventive care, have contributed to the underperformance of PHC systems worldwide [21]. Despite the importance of PHC, limited research has been conducted to optimize the system and develop evaluation criteria for policymakers and managers [45, 46]. Given the explanations provided, it is necessary to make high-confidence decisions to improve the performance of these healthcare centers. Additionally, considering the internal and external conditions of the centers, the need to use appropriate criteria is clear [49]. In this study, we extracted and proposed the optimal performance scenario for the case study center through the application of simulation tools and the simultaneous integration with a mixed-methods (quantitative–qualitative) methodological approach. Moreover, a high-confidence decision-making method for enhancing the performance of other healthcare centers has been presented.

Methods

We conducted this empirical study in 2021 at a UCHC in southeast Tehran (Ray County), affiliated with the Tehran University of Medical Sciences (TUMS), Iran. Empirical studies involve collecting and analyzing data from real-world observations or experiments [51, 52]. This study is empirical because it utilizes data gathered directly [51] from the UCHC through field observations, patient records, and expert consultations.

The designated UCHC is one of four large UCHCs affiliated with the TUMS and supervises four Health Posts. The center is led by a general practitioner and provides obstetrics and gynecology services, pediatric care, dental services, psychological counseling, nutrition and dietary therapy, environmental health, occupational health, paramedic services, optometry, audiology, and disease control for approximately 45 000 inhabitants.

This study was conducted in five phases and in 14 steps, as shown in Figure 2.

Phase 1: collecting the characteristics of the UCHC

Initially, the lead researcher (S.H.H.) conducted a 2-hour in-depth, face-to-face interview with the head of the UCHC to gather information and obtain general characteristics of the UCHC, including details on services provided, number of employees, expertise, and procedures for entering and referring patients. The interview was transcribed and served as the basis for subsequent phases of the study.

Phase 2: time recording by field observation

We employed a chronometer and registration forms to record the patient arrival rate and mean duration of services during a standard working week (6 days). Our sampling method was a census, and we collected data on 781 patients who entered the UCHC. However, 101 patients refused to participate in the study, and we only recorded service time for 680 samples.

Phase 3: current situation of the UCHC

The data collected from the first two phases of the study (i.e. characterizing the UCHC and gathering time-related information) were entered into the Arena software, a discrete event simulation and automation platform developed by Systems Modeling. The software utilizes the SIMAN processor and simulation language to analyze the data and generate scenarios. Quantitative results (the current situation’s results) are categorized into four areas of interest: number of patients entered, number of patients served, employee productivity rate, and patient waiting time.

Phase 4: extraction of the scenarios

We used the Nominal Group Technique (NGT) [53] to identify relevant and useful decision variables for scenario extraction. We presented the output of three scenarios (current situation, first scenario, and second scenario) to a panel of 12 experts who met the criteria of 1—at least 3 years of experience in managing UCHCs; 2—currently hold a higher executive position above the management of the related UCHC; and 3—have studied in one of the specialized fields of medical/public health sciences, to ensure their experience and knowledge. The characteristics of the experts participating in the study are presented in Table 1.

The experts presented their decision variables in three virtual stages, and we obtained the relevant variables through voting in each stage. The methodology was as follows: after presenting the initial data of the UCHC, derived from simulating the current situation, the extracted scenario (based on the simulation with the application of previous round decisions) was further examined by the experts using the NGT. The experts considered the quantitative results of each scenario (mentioned in phase 3) and external factors: compliance with regulations and the feasibility of implementing those decisions when extracting the relevant decisions.

These variables focused on the working hours of the UCHC, expertise, number of employees, and number of related Health Posts.

Phase 5: comparing scenarios to choose an optimal scenario for prioritization

Utilizing Multiple-Criteria Decision Analysis (MCDA) and employing a multi-criteria decision tree approach, we prioritized the most optimal performance scenarios in two conditions [54]. In the first condition, we derived four criteria from the quantitative output of the simulation software (Arena). In the second condition, we solicited input from 12 experts through the NGT, resulting in the identification of 6 qualitative criteria (Table 2).

The definition of criteria allowed for a context-specific evaluation that reflects the real-world conditions and constraints of the UCHC. However, we recognize the importance of situating our criteria within the broader literature on healthcare performance optimization. The empirical quantitative criteria have been used in other health-related studies and organizations [42, 46]; but were examined for the first time in a UCHC, while other studies that used six solely experimental qualitative criteria, identified in this study, were not observed.

We obtained the values of each scenario by assigning weights to the criteria based on experts’ opinions. These weights were then used to normalize the values and enter them into the Expert Choice software for prioritization. The MCDA was conducted in a distributed manner, considering the quantitative and qualitative aspects of the scenarios. To account for any inconsistencies between the two modes of evaluation, we set a threshold of lower than 0.08.

Results

The findings from the interviews with the head of the UCHC indicate that patients either directly access the UCHC or are referred from four affiliated Health Posts. The operating hours of the UCHC were from Saturday to Thursday and from 8 a.m. to 2 p.m. Additional characteristics of the studied UCHC are presented in Table 3.

We derived the decision-making variables through the NGT based on experts’ opinions. These variables are presented in Table 4.

The results of the second phase, which involved field observations and time recording, revealed that an average of 130 individuals visited the center daily. The entry rate was 22 people per hour. The UCHC’s mean service time and proportion of patients based on each service are presented in Table 5.

The mean service time for certain services, including general physician visits, nutrition and diet counseling, and psychological counseling, was affected by the patient’s needs, executive instructions, and the minimum number of required visits per day. According to their contracts, general physicians, nutritionists, and clinical psychologists were expected to provide at least 575, 192, and 92 registered services per month, respectively. Environmental and occupational health experts were expected to provide 80 services, including visiting places and registering them in the relevant system.

The UCHC’s simulation scenarios

Current situation

After creating the processes and entering the relevant parameters, the researchers ran the simulated model and obtained the first output of the current situation, which includes the results of the four quantitative criteria mentioned (Table 6).

First scenario

The study found that the center experienced a high volume of referrals, and based on the interviews, increased access to services was necessary. To address this challenge, the working hours of all personnel were extended by 4 h, from 8 a.m. to 2 p.m. to 8 a.m. to 4 p.m. This resulted in an average increase of 25% in the patient entry rate during working hours (Table 6).

Second scenario

Considering the high workload of physicians and nutritionists in the previous scenario, we added two physicians and one nutritionist to the first scenario.

Third scenario

Given the high workload of the personnel in the second scenario, we decided to reduce the number of Health Posts covered by the center, which resulted in a 15% reduction in the total number of patients entering the center. Other decision variables, except for the number of nutritionists, were maintained at the levels established in the second scenario (Table 7).

Table 5 presents a more balanced employee productivity rate in the center compared with the previous scenario, with a reduced waiting time. Researchers had to reduce the referral rate and the number of patients to maintain a balance between the employee productivity rate and patient volume. This resulted in a 10%–15% reduction in the number of patients compared to the previous scenario.

Figures 3 and 4 show the results of the quantitative criteria of the UCHC’s current situation and simulation scenarios and the trend of changes.

Figures 3 and 4 illustrate the upward trend in the number of patients entered and served from the baseline to the second scenario. This increase was achieved by implementing the decisions outlined in Rows 1 and 2 of Table 4. Additionally, we observed a decrease in the patient waiting time from the first to the third scenario, realized by applying all the decisions listed in Table 2. The changes in the staff productivity rate from the current situation to the third scenario were minimal although these changes fluctuated in proportion to the changes in the volume of patients served.

Choosing the optimal scenario based on quantitative and qualitative criteria

In alignment with the experts’ comparative scores across the four quantitative criteria of the study, we selected the third scenario, which received 401 points, as the optimal scenario. Conversely, the current situation received the lowest score, as depicted in Figure 5.

Additionally, based on the experts’ comparative scores across the six qualitative criteria of the study, we selected the third scenario, which received 458 points, as the optimal scenario. Conversely, the first scenario received the lowest score, as shown in Figure 6.

Based on the study’s performance criteria for the current center, the third scenario appears to be the most suitable option compared to the other two scenarios.

Discussion

This study aimed to introduce optimal performance scenarios for a UCHC to help managers make more confident decisions. In the current state of the UCHC, the numbers of patients entering and being served were 134 and 97, respectively. Additionally, the productivity rate for physicians and psychologists exceeded 90%, while waiting times for patients were 34 and 107 min, respectively. Furthermore, other services were balanced, and the number of optometry and audiology patients was relatively low. Based on these observations, decisions were made by experts, and simulation scenarios were subsequently developed.

Previous studies have reported similar challenges in healthcare settings. One study found that healthcare operational units often face issues such as overcrowding, which can negatively impact the quality of care provided to patients [55]. Similarly, others highlighted the need to reduce patient waiting times in primary healthcare centers [56], while others considered it critical to increase the number of patients served in healthcare [57], and emphasized the importance of healthcare workers’ productivity rate and optimizing the performance of healthcare operational units [36, 58].

We analyzed the changes in quantitative criteria from the current situation to the third scenario. The number of patients entered increased by 94%, the number of patients served increased by 137%, patients’ waiting time decreased by 107%, and the employee productivity rate increased by 2%. Additionally, we presented qualitative criteria, including authorizing the manager’s action, the possibility of implementation, the possibility of running the program indefinitely, financial capability, the acceptance rate of employees, and the acceptance rate of patients, to validate the scenarios. The experts’ choice scores for the quantitative criteria were 111 for the current situation, 141 for the first scenario, 347 for the second scenario, and 401 for the third scenario. Similarly, the experts’ choice scores for the qualitative criteria were 116 for the current situation, 208 for the first scenario, 216 for the second scenario, and 458 for the third scenario. Previous studies have highlighted the potential of simulation tools to enhance decision-making in healthcare operational units, hospitals, and other treatment settings [46, 59, 60].

The third scenario achieved the highest MCDA score based on the quantitative criteria, and it is accordingly identified as the optimal scenario. Other research studies have employed similar quantitative criteria to select the most desirable scenario [42, 59].

We noted a discrepancy between the number of patients who entered and received services in the current situation and all three simulation scenarios. This disparity was primarily attributable to the unavailability of necessary services to patients and the inability to accommodate late-hour visits at the center.

Two primary reasons contributed to the inescapable increase in patient registration at the center, resulting in an extended waiting time and the need to mitigate it: (1) The densely populated and large population of Ray County, with a pressing need for PHC and other essential services, faces limited access to healthcare facilities [9, 19]; (2) The UCHC under investigation is one of the region’s preeminent referral centers, offering a comprehensive range of services.

Similar to a previous study conducted in Iran, our findings demonstrate a 52% decrease in waiting time, from 27 min in the current situation to 13 min in the third scenario [61]. However, the magnitude of these changes and the reduction in waiting time may vary depending on the number of patients seeking services in different departments [42].

For this reason, the waiting time has also been adjusted and modified in some services through personnel activity. A study found that changing working hours could change patient waiting time [62]. The total mean waiting time to visit a general physician was remarkably short, at an average of 7.2 min (Figure 5 and Table 5), indicating that by adjusting simulation parameters, the minimum possible waiting time can be achieved [63]. Similar findings were observed in a simulation study of a cancer hospital in Jordan, which led to a significant reduction in service time [64]. Other studies have also shown that increasing the number of medical examination units and a pharmacy or adding additional medical personnel can reduce waiting time [65, 66].

Regarding the third quantitative criterion, the employee productivity rate, we observed an increase of 3%, which is less than the changes in the other quantitative criteria. This finding is consistent with another study [46] that suggested the possibility of predicting results using similar tools for improving employee productivity.

According to the employment contracts of all health service providers in this center, except paramedics and health workers, the remuneration was based on the number of services provided. As a result, employees were incentivized to increase their share of working hours to maximize their financial benefits, as detailed in reference [27]. Previous studies have shown that increased working hours can enhance service capacity and moderate personnel activity [42, 67] by improving access to services and making them more flexible [68].

We conducted a qualitative evaluation similar to a previous study to assess the feasibility and compliance of the proposed scenarios with actual conditions [69]. The comparative scores revealed that the third proposed scenario achieved the highest scores, consistent with the findings of another study [70].

Because of irregular fluctuations in the values of the quantitative criteria [71], prioritizing scenarios based solely on changes in these values is not feasible. To address this challenge, we employed the MCDA method, which considers the changes in the values of the quantitative criteria simultaneously with their relative importance in prioritizing the scenarios. This novel approach for prioritizing simulated scenarios has not been employed in similar studies.

One study advocated using multiple qualitative criteria in decision-making, specifically related to the healthcare problem [71]. In this context, the first criterion involves the involvement of managers at three distinct organizational levels: the UCHC, the PHC system, and the University of Medical Sciences. Typically, the head of the UCHC initiates decision-making proposals. However, for each scenario, the head of the PHC system and, when necessary, the Chancellor of the University of Medical Sciences (i.e. dissolution or establishment of the UCHC and related units and Health Posts) may also be consulted.

The successful implementation of these scenarios hinges on the organization’s management capacity [72]. Notably, experts scored the possibility of implementing the third scenario higher than the others, which requires influential management skills and the ability to manage the unit with minimal resources to achieve greater efficiency [73]. Management power is critical in implementing decisions and sustaining programs in this context. It is essential to have adequate human, physical, and information resources and sustainable financing to maintain the operation of the scenario [74].

Iran’s Ministry of Health and Medical Education and Medical Universities provide support to UCHCs through government funding, thereby alleviating any potential financial burdens [27, 30].

The motivation and continuity of staff are crucial factors in the successful implementation of decisions [75]. Our expert scores revealed that employees were more satisfied in the third scenario compared to the other scenarios and the current situation. We analyzed the impact of employees’ work engagement on their satisfaction and found it essential to adjust personnel distribution of labor and provide adequate time for rest, meals, and meetings with supervisors and colleagues to prevent adverse effects on performance [76]. To address these issues, we reduced the referral load and rate by 15%, excluding one of the four Health Posts and integrating it with the adjacent UCHC. Additionally, we balanced the activity of each personnel, with their activity level reaching 54% of the total personnel activity, to enhance accuracy, quality of service, and patient satisfaction [77, 78].

Employees’ satisfaction is critical to achieving positive patient satisfaction, which is a key indicator of success in organizational decision-making [79]. Our experts’ evaluations revealed that the third scenario scored higher in patient satisfaction than the other scenarios. Furthermore, the reduced waiting time and increased number of patients in this scenario can significantly improve patient satisfaction. Specifically, the waiting time for general physician visits was reduced from 34 min to 1.8 min, thus satisfying patients’ needs effectively.

An additional notable change observed in the third scenario is the significant increase in the number of patients served, particularly in general physician visits. Approximately 62% of the study’s patients requested a visit by a general physician. With the adjustments in the third scenario, the capacity of general physician services increased by 218% (from 38 patients served in the current situation to 121 patients served in the third scenario), which had a cascading effect on the number of referred patients and the total number of patients served. Our experts assessed that these changes will indeed improve patient satisfaction.

Since the impact of each introduced qualitative criterion (Table 2) on the simulated scenarios, and vice versa, is not uniform, we employed the MCDA method to compare these criteria based on the conditions of different scenarios. This enabled us to obtain each scenario’s scores and finally rank them. Our findings, which include the simulated scenarios’ qualitative criteria and their prioritization method, constitute a novel contribution compared to existing studies.

Rigor of study

The empirical nature of this research provided practical insights into the functioning of UCHCs, which can inform real-world decisions. Nevertheless, empirical studies are often limited in their generalizability, especially when the sample size or context is specific, as is the case with a single UCHC in Tehran. The advantage of empirical research lies in its ability to provide concrete data and insights that can lead to actionable recommendations. We leverage empirical data to create and validate scenarios that optimize healthcare performance at the UCHC. We advocate future studies to include a comparative analysis of the developed criteria against established frameworks to validate and potentially enhance the robustness of methodology.

Our findings are particularly relevant to the context of Iran, a lower-middle-income country, where urbanization poses significant challenges to healthcare delivery. While the scenarios developed are specific to the studied UCHC, the methodological approach, including using simulation tools and mixed-methods analysis, can be adapted to similar healthcare settings in other LMICs. We acknowledge that differences in healthcare infrastructure, population demographics, and resource availability may limit the direct applicability of our findings to other settings. However, the study provides a framework that can be tailored to other UCHCs or similar primary healthcare facilities in the LMICs, offering a basis for comparative analysis and potential adaptation.

Conclusions

Similar to the third and proposed scenario of this study, it is essential to make concerted efforts to maximize the number of patients served in a balanced manner, while simultaneously increasing employee productivity rates. Additionally, it is crucial to consider waiting time to prevent patients’ dissatisfaction and serve as an incentive to increase patient flow. To enhance healthcare services in cities, it is essential to prioritize the optimization of the UCHC performance. Therefore, simulation-based decision-making with a simultaneous quantitative–qualitative approach can help minimize risks and increase the accuracy and validity of the related results.

In light of our findings, we recommend that the supervisors of the studied UCHC and other UCHCs (with similar characteristics to the studied UCHC) adopt the decisions suggested in the third scenario to enhance their performance. Furthermore, supervisors of other UCHCs are encouraged to utilize simulation techniques to inform their decision-making process. Additionally, we advocate health network policymakers, particularly in the LMICs, to use the suggested quantitative and qualitative criteria to inform their decisions and improve the performance of UCHCs.

Acknowledgements

We would like to thank the Research and Technology deputy and public health deputy in TUMS who supported us in this study, the head and staff of UCHC who patiently participated in this study, and the expert panel who generously contributed their time to the study.

Funding

This research is part of a Ph.D. thesis in Healthcare Services Management (S.H.H.), supported by the Tehran University of Medical Sciences, Tehran, Iran. The authors received no specific funding for this work.

Conflict of interest

None declared.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The Ethics Committee of the Research and Technology Deputy of TUMS approved this study (IR.TUMS.SPH.REC.1396.4209). Before data collection, we obtained informed consent from the UCHC’s supervisor and staff and other participants.

References