Optimizing health service location in a highly urbanized city: Multi criteria decision making and P-Median problem models for public hospitals in Jeddah City, KSA

Abdulkader Murad; Fazlay Faruque; Ammar Naji; Alok Tiwari; Emad Qurnfulah; Mahfuzur Rahman; Ashraf Dewan

doi:10.1371/journal.pone.0294819

. 2024 Jan 2;19(1):e0294819. doi: 10.1371/journal.pone.0294819

Optimizing health service location in a highly urbanized city: Multi criteria decision making and P-Median problem models for public hospitals in Jeddah City, KSA

Abdulkader Murad ^1,^*, Fazlay Faruque ², Ammar Naji ¹, Alok Tiwari ¹, Emad Qurnfulah ¹, Mahfuzur Rahman ³, Ashraf Dewan ⁴

Editor: Mohammed Sarfaraz Gani Adnan⁵

PMCID: PMC10760729 PMID: 38165977

Abstract

Rapid urbanization and population growth have increased the need for optimizing the location of health services in highly urbanized countries like Kingdom of Saudi Arabia (KSA). This study employs a multiple-criteria decision making (MCDM) approach, e.g., fuzzy overlay technique by combining the P-Median location-allocation model, for optimizing health services. First, a geodatabase, containing public hospitals, road networks and population districts, was prepared. Next, we investigated the location and services of five public hospitals in Jeddah city of KSA, by using a MCDM model that included a fuzzy overlay technique with a location-allocation model. The results showed that the allocated five hospitals served 94 out of 110 districts in the study area. Our results suggested additional hospitals must be added to ensure that the entire city is covered with timely hospital services. To improve the existing situation, we prioritized demand locations using the maximize coverage (MC) location problem model. We then used the P-Median function to find the optimal locations of hospitals, and then combined these two methods to create the MC-P-Median optimizer. This optimizer eliminated any unallocated or redundant information. Health planners can use this model to determine the best locations for public hospitals in Jeddah city and similar settings.

1. Introduction

Important factors influencing the direct and societal expenses of healthcare services in an area include the optimal location of healthcare facilities and the distribution of patients [1]. These broad strategic choices also affect quick decisions like allocating resources and setting priorities. Choosing a site for a facility is a long-term investment, and once committed, it may be costly to relocate or alter the location. In addition, population growth and urbanization may make current policies less ideal in future. Hence, a city’s incredible expansion propelled by rapid urbanization and population growth has increased the need for optimizing health facilities.

The need for reasonable and accessible healthcare facility design has received increased interest in recent time [2]. Providing high-quality healthcare in emerging cities and surrounding regions is vital to understanding the healthcare system’s growth process. Locating healthcare centers in inappropriate locations would be inefficient in providing better service to people, especially during emergencies. Therefore, appropriate locations promote patient accessibility and enhance the quality of services. Several studies have confirmed the pertinence of location in healthcare services, especially in minimising response time [3]. However, choosing the most optimal location of facilities and allocating clients to those are crucial steps in designing a healthcare distribution network. Location-allocation models (LAMs) provide a suitable choice for this kind of decision-making challenge. LAMs include population or demand locations, facility locations, and travel distance or time between facilities and demands.

Numerous studies have examined the challenges of allocating resources to meet changeable demand resulting from the population boom and relocating facilities, e.g., warehouse location, saving client’s travel time [4]; improving location of public schools [5]; placement of food pantries [6]; optimization of periodic distribution network [7]; placing fire stations [8]; emergency evacuation centers for natural hazard victims [9, 10]; and optimized location-allocation of earthquake relief centers [11]. In addition, many studies have also examined healthcare locational issues [12–16].

Evidence-based planning of hospitals, medical centers, and other health facilities using state-of-the-art technique is becoming invaluable to warrant the provision of sufficiently available, cost-effective, and high-quality healthcare in rural and urban regions across the world. The complete results, well-grounded suggestions, and recommendations of such investigations offer promising outcomes to health managers and policymakers in both developed and developing countries. Geographic information systems (GIS) and other spatial methods in health planning answer fundamental concerns regarding spatial analytics and incorporate multiple geographic perspectives of individuals, health care infrastructures and the environment. A GIS has become a useful tool with specific advantages as it can facilitate complex analyses [14]. Improvements in healthcare planning may be made by examining early reactions of patient flows using distance optimization simulations and healthcare scenarios in a geographical context. This optimization method integrates demographic characteristics, patient records, hospital information, and transportation networks using normative location/allocation modelling [15].

Policymakers need to consider various socioeconomic [16] and infrastructural factors [17], including distance to healthcare facilities, the distribution of population, availability and number of beds, size of hospitals, number of patients, and median household income to construct new hospitals. A GIS is an important tool in determining the optimum location of facilities [18]. The current work optimizes the location of hospitals by combining MCDM-based fuzzy overlay and P-Median LAM tools with maximize coverage (MC) location problem. The LAM is an optimization model used to determine the best location to allocate resources, such as facilities or services, to meet the demands of a particular population. This model aims to minimize the cost of providing these resources while ensuring they are adequately distributed across space to satisfy the needs of the populations. The model has two components; location and allocation. The location component involves determining the optimal location for a facility or service. The allocation part determines how to distribute resources to the population to meet their needs based on the proximity to the facility or service. The LAM model can be used in various fields, including healthcare, transportation, and emergency services. For example, it can be used in healthcare to determine the optimal location of hospitals, clinics, or pharmacies to serve a particular population. The model can also be used to allocate medical resources, such as doctors, nurses, and medical equipment, to different locations based on the needs of the populations.

This work has the following research questions: (i) How can healthcare centers be located and fulfil the demand during an emergency? and (ii) how can demand be ensured equitable facilities? While models of geographic accessibility help evaluate existing public healthcare delivery systems, they do not guide where resources should be allocated or relocated. This issue may be overcome by integrating LAMs with MCDM in a GIS. Besides, healthcare planning can be better planned using P-Median and MC models [19–22].

This study aims to demonstrate capabilities of spatial analytics to compute optimum hospital service areas using LAM and fuzzy overlay technique in Jeddah City, KSA. It sheds light on how healthcare facilities should be best located in densely populated places like Jeddah. This study aims to contribute to the fields of urban planning and healthcare management. It helps stakeholders, including researchers and health professionals, better use limited healthcare resources to meet the needs of a growing population. The study also provides evidence for the utility of MCDM methods in the healthcare sector. It demonstrates maximizing healthcare locations by integrating geospatial data with population demographics, road networks, and other factors. This information may pave the way for further studies and improved methods of urban planning and support resource allocation.

Rapid urbanization and population increase may have far-reaching social consequences, including the exhaustion of healthcare infrastructure and the inability of certain populations to get the treatment they need. Recommendations from this work might greatly enhance healthcare access and equality for the people of Jeddah City by finding the most appropriate places for constructing future hospitals. Besides, this study has wider ramifications than only for Jeddah City. The methods and insights presented in this work can be a useful reference for health planners and policymakers beyond the study area. According to the literature, a study in São Paulo [1] concluded that access to healthcare is important for people, especially those in low-income groups. The study identified various barriers to healthcare accessibility, including proximity, safety, and quality of care. They recommended that planners need to integrate transport and health policies to tackle health inequalities. Another relevant study in Shenzhen City, China [2] found that an appropriate adjustment of general hospital location could significantly improve healthcare equity. Likewise, a study from Irbid municipality, Jordan [3] confirmed that the optimum location of healthcare services would improve the quality of services, including spatial accessibility and the patient-doctor capacity ratio.

The novelty of this study is the use of spatial analytics to produce an optimized hospital facilities area in Jeddah City. Specifically, the use of LAM and fuzzy analytical techniques is a unique approach that has not been extensively studied in the context of healthcare planning in Saudi Arabia or elsewhere. Furthermore, fuzzy overlay allows assessing the viability of potential locations and identifying ideal spots for healthcare facilities. In addition, it allows a multicriteria analysis that considers the possibility of a phenomenon belonging to multiple sets, which is useful when a location must meet specific criteria to be suitable. Additionally, the approach considers both reduced costs and optimized travel time as goals of LAM, to ensure that healthcare facilities are efficiently located to serve high-demand locations within a specified threshold followed by less-demand sites. Overall, the approach of this study can benefit relevant stakeholders, such as healthcare providers and policymakers, by providing a more efficient and effective way to plan and allocate healthcare resources in Jeddah and beyond.

2. Materials and methods

2.1 The study area

Jeddah, the second-largest city in KSA, is used as a case because firstly, it is experiencing a higher population growth compared to other cities in KSA (with its population doubling over the past 20 years). In 2020, Jeddah’s population was over 3.4 million, which was only 2.3 million in the year 2020. During 2000 to 2010 decennial population growth rate was as high as 56.4%, indicating a significant increase in healthcare services demand, and making it an excellent location to study hospital placement [23, 24]. Additionally, Jeddah is a major economic hub and serves as a gateway to the holy cities of Makkah and Madinah, attracting many tourists, business travellers, and expatriates who require access to quality healthcare services. Economic significance of Jeddah [25] also makes it a useful location for studying hospital placement.

Jeddah is a regional healthcare center, with several major hospitals and medical centers (Fig 1). Studying hospital placement in Jeddah can provide valuable insights about overall healthcare system in KSA and the region, as well as the challenges and opportunities associated with hospital placement in a regional medical center. Moreover, Jeddah is prone to natural disasters such as flooding [26], which can significantly impact healthcare services. Thus, studying hospital placement in Jeddah can help identify optimal locations that can withstand natural disasters and ensure continuous healthcare services. Finally, Jeddah’s diverse population includes a large expatriate community with varying healthcare needs, providing an opportunity to study how hospital placement can address the unique healthcare needs of different communities.

2.2 Data

We used several spatial and non-spatial datasets to identify optimum hospital locations and respective service areas. The databases include features for public hospitals, road networks, and population districts, obtained from respective government agencies (Table 1). Each feature is associated with pertinent attributes that support developing the required models.

Table 1. Data used in this study.

Data	Detail	Feature type	Source	Process
Public hospitals	Location	Point	Ministry of Health, Jeddah (2020)	Determining location of facilities
Road networks	Connectivity	Linear	Jeddah municipality (2018)	Explaining service network
Districts	District boundaries	Polygon	Jeddah municipality (2018)	Deriving extent of service area
Hospital beds	Availability of beds	Numeric	Ministry of Health, Jeddah (2020)	Finding density of health services and classifying public hospitals
Population	Number of people in each district	Numeric	Jeddah municipality (2018)	Obtaining set of demand points of health users

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2.3 Data analysis with MCDM process

2.3.1 Fuzzy overlay

Fuzzy logic and fuzzy set theory, first introduced in 1965 by Zadeh (1965), are extensively used in uncertainty modeling and in decision-making [27]. In this study, fuzzy overlay is utilized to estimate the spatial distribution of hospitals in the city. This process includes several steps: (i) selecting target layers that reflect site characteristics, which is used in deciding best locations for hospitals, including the number of beds and population density; (ii) assigning fuzzy membership values, on a scale of 1 to 0, to each layer, based on relevant parameters; and (iii) combining fuzzy layers to determine logical operators.

In general, high-score places are the best and most fitting. Once the model is validated by hospital locations and the findings assessing its accuracy and the need for modification are reviewed, suitable locations can be identified, and the results can be applied to view the final suitability layer by fuzzy membership functions.

The fuzzy overlay tool also allows analysis of the possibility of a phenomenon belonging to multiple sets in a multi-criteria analysis. The fuzzy analysis covers the following:

2.3.1.a Spatial distribution of hospitals. Geographical viewing starts with points of interest and asks about attributes of events located within area of interest. Fig 1 shows the location of hospitals, describing the distribution of five public hospitals in the city. The sizes of these hospitals are not the same. For example, King Fahad Hospital, located in the central part, has a capacity of about 900 beds. Meanwhile, Al-Thaqar Hospital, located in the south of the city, contains as low as 109 beds.

2.3.1.b Kernel density estimation. Kernel density can be estimated from either points or line features using a non-parametric approach. This method is extensively used in various applications, including risk mapping and hotspot identification. Kernel functions are derived from quadratic model explained by Silverman (2018) [28]. We use point features as an input to the kernel density, which includes spatial filtering by a search window (also called bandwidth). For point data, a kernel can give each point a “spatial meaning” and estimate the variable’s probability distribution at each location within a study area. The output dimensions for the associated cells contribute to the definition of the kernel as a three-dimensional feature.

2.3.1.c Location-allocation modelling. In this study, the LAM toolset is used to determine optimal hospital locations for the surrounding populations. We used the P-Median tool as it selects optimal locations based on impedance to the facility (e.g., hospital, Fig 2). A modified P-Median model, which considers both geographic accessibility and service quality, is proposed that employs both exact and approximate strategies [29]. Another example is found in the application of this model in a case study in Hainan Province, China, resulting in the selection of three optimal healthcare centers from candidate cities and allocating resources considering capacity constraints and spatial compactness constraints [30].

Facilities such as hospitals can reduce costs and keep their accessibility high using LAM. The following six issues can be addressed through LAM analysis: minimize impedance, maximize coverage, minimize facilities, maximize attendance, maximize market share, and target market share. The P-Median problem model produces service catchment area and allocates demand locations. The P-Median clustering model has its roots in operations research, originating from efforts to optimize the planning of facility locations.

3. Results and discussion

Fig 3a, 3b shows the results of kernel density analysis for hospital beds and population. The former identifies bed density and reveals that the density is higher in the downtown area but relatively low in the north and east of the city. The latter demonstrates population density, which increases in the downtown area and to the north and east of the city. Meanwhile, the southern and eastern city districts are having low population densities.

The present study has selected the fuzzy ‘And’ model to get the maximum value from all inputs. In this case, all inputs must have a high value to obtain a high output value. This means that cells located near higher population density and, at the same time, having higher bed density will indicate locations of high provider-to-population ratio with better hospital services than locations with lower provider-to-population ratio.

Fig 3c defines these less-served locations in the city, which are mainly in the south, east, and north. Health planners should prioritize these locations to increase optimized hospital locations in Jeddah City. Health planners can use this output to help decide where to build a new hospital. This work suggests local health planners to assign new hospitals in these locations to get better health services in the city.

The P-Median model was set up to choose the best facilities that could serve the most-demand districts within 6-hour of the nearest hospitals (facilities). We have assumed that all facilities are equally important, so each receives a weight of 1. The choice of the 6-hour impedance cut-off in the LAM may have been based on several factors, including practical considerations, such as available resources, population density, and the nature of the demand locations. For example, a 6-hour impedance cut-off may have been selected because it is a reasonable travel time for patients seeking medical services and covers a significant portion of the study area. It may have been chosen because it is a manageable distance for healthcare providers to travel to reach the target locations. In addition, the 6-hour impedance cut-off may have been chosen because it reveals peak demand locations, which may be critical in identifying areas that require immediate attention or additional resources. However, other impedance cut-offs could also have been considered, such as 3 hours, 4 hours, or 5 hours. The choice of the impedance cut-off ultimately depends on specific context, objectives, and constraints of the location-allocation problem. The results of the P-Median model reveal that 94 districts out of 110 are covered by the hospitals, where the King Abdualaziz Hospital served 22 districts. The analysis shows the optimum catchment area for this hospital, which helps local health planners to focus on these 22 districts in terms of providing the required healthcare services for all residents living in these districts.

According to this study, demand locations should be covered within the specified coverage time of 6-hour from the nearest facilities, and highly neediest districts should be served within an hour’s drive from the hospitals. Coverage optimization of facilities utilizing standalone LAM can be complicated due to unpredictability of demand locations routing problems for services. Therefore, we applied an optimized MC-P-Median problem model to address these shortcomings. We use several impedance factors to evaluate coverage of demand locations within impedance thresholds (cf. optimized maximize coverage location problem model to select facility locations).

After applying P-Median LAM, it was determined that five hospitals (facilities) were unable to reach all districts (110 districts) within the specified travel time due to unallocated and redundant information (Fig 4). Therefore, the P-Median problem model was optimized with the MC location problem model. The MC location problem model attempts to optimize the distribution of the potential locations for an assigned travel time. The MC model solves problems that serve the highest demand location, based on distance or time. Besides, the MC model does not minimize the number of hospitals (facility locations) needed to cover all districts over specific distances or times. It helps to solve the redundant information of the LA problem. The results of the P-Median problem model indicate that there were 16 demand locations inaccessible or redundant (Fig 4). In the optimization process, redundant demand locations were eliminated to cover the maximum demand locations within a short time, owing to the fact that it can be challenging to optimize the demand locations by employing standalone P-Median model because of associated uncertainty (Fig 5). The results reveal that ~45% of demand locations could be accessed within a travel time of 1.5 hours to cover districts of the city (Table 2). This proportion rises as the impedance cut-off rises to 6 hours, i.e., the impedance cut-off reveals peak demand locations (94 out of 94 demand locations, Table 2 and Fig 5). This outcome can be used to visualize the aim of this work, which can further be optimized by considering the capacity of each hospital. If the capacity of a hospital was known, this optimized model could identify if further facilities were needed for the study area.

Fig 4 — This figure is made using ArcGIS software (https://www.esri.com/). [Shape file or base map is freely available at https://www.diva-gis.org/gdata and does not have copyright restrictions].

Fig 5 — This figure is made with ArcGIS software (https://www.esri.com/en-us/). [Shape file or base map is freely available at https://www.diva-gis.org/gdata and does not have copyright restrictions].

Table 2. MC-P Median-based optimization considering different impedance cut-offs (shaded cells indicate maximum demand locations for each facility).

Facility (Hospitals)	Demand (Districts)	Percentage (%)	Demand and inaccessible locations with impedance cut-off (hour)
Facility (Hospitals)	Demand (Districts)	Percentage (%)	0.5	1.0	1.5	2.0	2.5	3.0	3.5	4.0	4.5	5.0	5.5	6.0
H1	21	22.34	0	3	9	11	15	16	20	21	21	21	21	21
H2	19	20.21	1	3	10	15	16	18	18	19	19	19	19	19
H3	22	23.40	1	6	11	16	16	18	18	18	20	20	20	22
H4	20	21.28	2	2	2	2	3	8	13	13	16	19	20	20
H5	12	12.77	2	6	10	12	12	12	12	12	12	12	12	12
Total	94	Percentage (%)	6.38	21.28	44.68	59.57	65.96	76.60	86.17	88.30	93.62	96.81	97.87	100.0

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H1: King Fahad Hospital, H2: Eastern Jeddah Hospital, H3: King Abdualaziz Hospital, H4: King Abdullah Medical Complex, and H5: Altaghar Hospital

In the last few decades, GIS has increasingly been used as a critical spatial decision support system (SDSS) for evaluating suitable locations of healthcare services. Although several methods are available within the geospatial community to identify suitable locations of hospitals, we used fuzzy overlay and LAM techniques in this work.

As the P-Median LA model alone cannot handle uncertainty related to real numbers, we used a fuzzy overlay to reduce uncertainty. According to the P-Median model results, existing five hospitals were insufficient to address all demand points within the specified travel time of 6 hours. Particularly, 16 districts were inaccessible due to unlocated issue and redundant information. To choose the best distribution of hospitals across Jeddah City and to ensure better coverage of demand location, an optimized model considering MC and P-Median models was applied. Such a combination, including MC-P-Median and fuzzy overlay, is rarely used in the health planning sector. The models applied in this study are expected to contribute to overcoming the problem of exponential increase in patients’ accessibility to hospitals in Jeddah City. These models significantly improved the results of hospital accessibility over traditional overlay index methods, such as buffer analysis or distance-based catchment area delineation.

The solution to the optimized model reveals that expanding the impedance threshold enables us to cover a more significant number of districts. According to our findings, raising the impedance threshold can improve the coverage of unconnected demand nodes. The analysis presented here may help satisfy the interest of the decision-makers in moving some of the centers to redistribute the healthcare facilities.

The study has wider ramifications given the global surge in urban populations. Although the results of a LA model are often limited to the study area and may not be readily transferable to other areas or cities, we believe the method can be scaled up to other settings. Besides, the study appears to be limited to the boundaries of Jeddah City, despite the existence of other adjacent localities in the problem set. By excluding adjacent localities from the study area, the results may not fully capture the broader healthcare needs and dynamics of the entire region. However, this limitation should not significantly affect the overall accuracy and reliability of the optimized locations for healthcare facilities, as the model may not account for potential demand from neighbouring areas or the influence of population movement across boundaries. However, to address this issue, further research is warranted.

We can infer that the solutions presented in the study are feasible as this model-based allocation of hospitals can serve substantially more districts of the study area. While the approach implemented in this study can improve healthcare services through better allocation, further optimization will be required to adjust to population dynamics by redistributing healthcare facilities and creating new facilities.

One of the key takeaways from this study is the knowledge gained through the integration of the P-Median model and fuzzy overlay technique, contributing to enhanced healthcare access via resource allocation. The results underscore the importance of considering factors, such as population density, proximity of healthcare facilities, and travel times in healthcare planning. This approach acknowledges that healthcare accessibility is not solely contingent on the number of hospitals but also on their strategic placement relative to population centers. Furthermore, the study highlights the significance of considering the local context, such as available resources, population density, and the nature of demand locations when determining travel time thresholds. Thus, adaptability and context-based decision-making are vital for effective healthcare planning.

While the results suggest the usefulness of our approach, we duly acknowledge the limitations of this study. This study may have generalizability problems, especially if it does not give insights into the transferability of the suggested paradigm to other situations. The study does not appropriately address the possible effect of infrastructure, dynamic changes in population, or socio-economic disparities that they may have on the findings. This limitation may affect comprehensive understanding of healthcare needs and dynamics in the broader region. The discussion should have the implications for not accounting for potential demand from neighbouring areas and the importance of regional dynamics in healthcare planning.

4. Conclusions

The results of the study in Jeddah City, using a combined LAM and fuzzy overlay method, demonstrated that about 45% of demand locations could be reached within a travel time of 1.5 hours. This proportion increases as the impedance cut-off (travel time threshold) raises to 6 hours. In fact, at this impedance cut-off, all demand locations could be reached (94 out of 94 demand locations). These results may help in enhancing the accessibility of each population site based on the improvement of nearby facilities and transportation modes.

In conclusion, this study offers a comprehensive approach to allocating and optimizing healthcare facility in urban areas. The findings underscore the importance of data-driven and context-specific strategies in addressing complex issue associated with healthcare accessibility for growing urban populations. Future studies might look at how multi-objective location-allocation issues can be used to address the above-mentioned problem.

Supporting information

S1 Data

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Click here for additional data file.^{(2.4MB, zip)}

Data Availability

This research article does not present new data. The study is based on a comprehensive literature review and analysis of existing data sources. No original data were generated or collected for this research.

Funding Statement

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia, under grant no. (KEP-8-137-41). The authors, therefore, acknowledge with thanks DSR for their technical and financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0294819.r001

Decision Letter 0

Mohammed Sarfaraz Gani Adnan

5 Jun 2023

PONE-D-23-14949Journal name: Plos One

Optimizing Health Service Location in Highly Urbanized Countries:

Multi Criteria -P-median model for Public Hospitals in Jeddah City, Saudi ArabiaPLOS ONE

Dear Dr. Murad,

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I Don't Know

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: My pleasure to review this work. The authors presented interesting findings of “Optimizing Health Service Location in Highly Urbanized Countries: Multi-Criteria -P-Median model for Public Hospitals in Jeddah City.” As climate change will bring changes in diseases geography everywhere, the topic is interesting and thus merit publication. However, there are some issues in the present work as it currently stands. I think considering those could further improve the quality of the manuscript before its publication. Overall, I would recommend a round of revision. My comments are outlined below.

1. The general description of the problem (Introduction) and the description of its importance for science and society may be further improved with some examples.

2. It will be better to add a study area map with lat. and long., so that international reader can get localtional information precisely.

3. Road features considered in this study are believed to be highly useful in developing a successful location-allocation model. Did you face any topological errors in the road network? If yes, then how you managed the error in the datasets? Please state it in your work.

4. Figures 1 and 2 unnecessarily increased the volume of the paper. I’d suggest combining them in a single figure.

5. Are the results (or the method) sensitive to this specific study area or can be scaled up?

6. Regarding the solutions, are these solutions Feasible or Optimal solutions? Please make this distinction both in the results and in their discussion.

7. Please make sure your conclusions section underscores the scientific value added to your paper and/or the applicability of your findings/results. It would help if you enhanced your contributions and limitations, underscored the scientific value added to your paper, and/or the applicability of your findings/results and future study in this session.

Reviewer #2: I would like to thank the authors for this interesting work. Please consider shortening the paper by avoiding repetition and too much theoretical descriptions that could be resolved using brief description and references to original studies introducing the theories/concepts. My detailed comments can be found below:

A)Abstract: “better serve better 94 districts” – please check

B)Introduction: The introduction is needlessly long. It would be better to remove some components that explain the applied model to the methodology section. The parts that could be considered for moving are listed below:

1. “This work proposed a novel method…facility or service”

2. “Based on the preceding discussion…facilities?” – the research question should come at the end of this section.

3. “The fuzzy overlay toolset…less-demand sites”

Additionally, please include a condensed literature review (a single para should suffice) that compares the existing LA model and how this method is novel or a new addition to the knowledge base. Moreover, the introduction does not have a meaningful flow. Please revise and reorganize.

C) Methods:

-Consider changing the heading of 2.1 to Study Area or a similar simpler heading

-2.1: First para requires some references

-2.1: A map of the study area could be useful

-2.3: Consider renaming the heading. Should be ‘Data Analysis’ or ‘MCDM Process’ or words similar to that effect. This is because all your sub-sections under section 2 are part of the methods.

-2.3: ArcGIS, which version?

-2.3: When explaining the algorithm that was used to determine the default kernel, the term ‘weighted’ was used. It is important to mention what this implies and what kind of weight assignments were performed.

-2.3.2: Condense. There are too many introductory descriptions here. Figure 1 could be placed in 2.1 to give general readers an idea about the study area before the variables are introduced in the subsequent parts. Jeddah’s boundary should be included in the Figure.

-2.3.2: Figure 1 and 2 could be merged. Consider showing the point locations of the hospitals and color gradation based on hospital numbers, as in Figure 2. The labels could be turned on to display hospital names on the points.

-2.3.4: Again, such a detailed description of the location-allocation model and its applications are unnecessary. Focus on the P-median model that you have applied, how it is novel, and how it was implemented in a GIS platform.

-2.3.4: Consider moving the mathematical description to a supplementary file. The paper is not method intensive. Therefore, too much focus on the mathematical model might distract the readers from the main objectives. Also, if you choose to include mathematical notations, there must be equation numbers, and every function (English alphabet) must be expressed with what they stand for. For example, it is unclear what the ‘a’, ‘d’, ‘x’ etc., stand for in the objective function. This description was provided at the end but every description must come underneath the equation when the function was first introduced. This part raises more questions than it answers. I would suggest simplifying this part and instead focusing on how this model was implemented by providing references to the P-median MCDM model from earlier studies as references.

-2.3.4: Instead of such a detailed mathematical description, consider including a schematic diagram and its description to explain the study design or P-median implementation.

- MCDM generally has a scaling factor applied to combine the multi-criteria variables. Please mention if this was done, and if yes, how.

-Figure 3: Scalebar has problems. The numbers should be spread out more in the beginning part.

3.2: First para repeats the methods. No need.

3.2: Third and fourth para could be used to replace section 2.3.4

Figure 6 (e.g., Candidate) and 7 (e.g., restriction) have symbols that do not exist in the map. Please revise. What does ‘lines’ mean?

Conclusion: Should be rewritten. The focus should not be on the application of GIS or fuzzy overlay or p-median. Rather, what you found in the study, specifically, what discrepancies in existing health facilities locations were revealed through the study analysis?

Reviewer #3: Title: Optimizing Health Service Location in Highly Urbanized Countries: Multi Criteria -P-median model for Public Hospitals in Jeddah City, Saudi Arabia Authors: Murad et al

Summary: The manuscript presents a novel approach for the optimization of hospital locations in urbanized regions like Jeddah City, Saudi Arabia, based on a multi-criteria decision model (MCDM) and the P-median location-allocation model. By combining these techniques into an MC-P-median optimizer, the authors aim to aid health planners in determining the best locations for public hospitals. The study was conducted using a geodatabase of public hospitals, road networks, and population districts in Jeddah, with results suggesting the need for additional hospitals for comprehensive coverage.

Major Concerns:

The major concern with this manuscript is its structure and organization, which presently do not facilitate a clear understanding of the work's aims, objectives, or hypotheses, particularly how these are addressed in the research findings.

Specifically, the authors should consider separating the results and discussion sections to clearly present and then interpret their findings. By doing so, they can more effectively contextualize their results within the broader literature, address the implications of their study, and discuss any limitations that might impact the applicability of their research in real-world settings.

Minor Observations:

• It's unclear from the manuscript whether the road network data was used in distance calculations.

• The figure captions need to be improved for better clarity and to guide the reader's focus within the figure.

• The authors mention localities adjacent to Jeddah City in the problem setting, but the study appears limited to the city's boundaries. The impact of this exclusion on the model's outcomes should be addressed.

• The text following the aim statement in the introduction seems redundant and could potentially be removed.

• The authors should consider merging Figures 1 and 2 to better convey the number of beds information. A consistent, rounded scale could also improve reader comprehension.

• On page 14, the formula for a linear programming problem should be contextualized for the current problem, explaining how various parameters in the equation are applicable in this context.

• The scale inconsistency across Figures 3 to 7 can be confusing for readers. The authors should ensure the scales remain consistent for the same aerial extent.

• Figures 3 to 5 lack internal reference points to aid reader comparison and understanding. The inclusion of recognizable features, such as significant points or road junctions, could improve this.

In conclusion, while the manuscript offers a promising contribution to the field of health service optimization, certain adjustments are necessary to enhance the clarity and impact of the findings. It is recommended that the authors carefully address the above concerns and observations in their revisions.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PLoS One. 2024 Jan 2;19(1):e0294819. doi: 10.1371/journal.pone.0294819.r002

Author response to Decision Letter 0

23 Aug 2023

Response Letter

Dear Academic Editor, Mohammed Sarfaraz Gani Adnan, PhD

Thank you very much for allowing us to revise the manuscript. We greatly appreciate the editor’s and the reviewers constructive comments and suggestions on the manuscript entitled “Optimizing health service location in a highly urbanized country: multi criteria -P-Median model for public hospitals in Jeddah city, KSA.”

We have carefully reviewed the editor’s/reviewer’s comments and addressed almost them in the revised mansucript. All revised portions are marked in yellow in the revised manuscript, which we would like to submit for your kind consideration. The authors pay special thanks to the editor and reviewer because their comments and suggestions have greatly improved quality of the manuscript.

Regards

Prof Abdulkader Murad

Response to Reviewer 1 Comments

Comment: My pleasure to review this work. The authors presented interesting findings of “Optimizing Health Service Location in Highly Urbanized Countries: Multi-Criteria -P-Median model for Public Hospitals in Jeddah City.” As climate change will bring changes in diseases geography everywhere, the topic is interesting and thus merit publication. However, there are some issues in the present work as it currently stands. I think considering those could further improve the quality of the manuscript before its publication. Overall, I would recommend a round of revision. My comments are outlined below.

Response: The authors would like to thank the reviewer for suggestions. We are highly indebted to your comments, which improved quality of the manuscript.

The manuscript has been rechecked and appropriate changes have been made in accordance with your suggestions. A response has been prepared to indicate changes in the revised manuscript relative to the comments. Corrected/inserted/amended texts are highlighted in YELLOW.

Comment 1: The general description of the problem (Introduction) and the description of its importance for science and society may be further improved with some examples.

Response 1: Thank you for your valuable comments. We have revised the introduction section and described how science and society could be benefitted from this work (please see pages 5-6 and lines 100-126).

“Important for both science and society at large, this study sheds light on how healthcare facilities should be best located in densely populated places like Jeddah, KSA. This study makes a scholarly contribution to the fields of urban planning and healthcare management. It helps stakeholders, including researchers and health scientists, better use limited healthcare resources to meet the need of a growingpopulation. The study also provides evidence for the utility of MCDM methods in the healthcare sector. It demonstrates maximizing healthcare locations by integrating geospatial data with population demographics, road networks, and other factors. This information may pave the way for further studies and improved methods of urban planning and support resource allocation. Rapid urbanization and population increase may have far-reaching social consequences, including the exhaustion of healthcare infrastructure and the inability of certain populations to get the treatment they need. Recommendations from this research might greatly enhance healthcare access and equality for the people of Jeddah city by finding the most appropriate places for future hospitals. Besides, this study has wider ramifications than only for Jeddah city, KSA. The methods and insights presented in this research can be a useful reference for health planners and policymakers worldwide. In conclusion, this study can contribute to urban planning and healthcare management, and it is significant for society because of its potential to increase access to healthcare, improve public health outcomes, and inform policy decisions in similar urban settings. According to the literature, a study in São Paulo [1] concluded that access to healthcare is important for people, especially those in low-income groups. The study identified various barriers to healthcare accessibility, including proximity, safety, and quality of care. They recommended that planners need to design integrated transport and health policies to tackle health inequalities. Another relevant study in Shenzhen City, China [2], found that an appropriate adjustment of general hospital location could significantly improve healthcare services equity. Likewise, a study from Irbid municipality, Jordan [3], confirmed that optimum location of healthcare services would improve quality of services, including spatial accessibility and the patient-doctor capacity ratio.”

References

1. Guimarães, Thiago, Karen Lucas, and Paul Timms. "Understanding how low-income communities gain access to healthcare services: A qualitative study in São Paulo, Brazil." Journal of Transport & Health 15 (2019): 100658.

2. Maslamani, A., Almagbile, A., & Dayafleh, O. (2021). Analysis of Spatial Accessibility and Capacity of Multi-Level Healthcare Facilities in Greater Irbid Municipality, Jordan. International Journal of Geoinformatics, 17(6), 111-121.

3. Hu, Wei, Lin Li, and Mo Su. "Spatial inequity of multi-level healthcare services in a rapid expanding immigrant city of China: a case study of Shenzhen." International journal of environmental research and public health 16, no. 18 (2019): 3441.

Comment 2: It will be better to add a study area map with lat. and long., so that international reader can get locational information precisely.

Response 2: Thanks. We have included study area map in the revised manuscript.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 3: Road features considered in this study are believed to be highly useful in developing a successful location-allocation model. Did you face any topological errors in the road network? If yes, then how you managed the error in the datasets? Please state it in your work.

Response 3: Thank you for your comment. We agree that road features are highly useful in developing a successful location-allocation model. In this study, we used the Jeddah Municipality Road Network map dataset that is similar to OpenStreetMap road network dataset. We encountered some topological errors whilst working, such as roads that were disconnected or had incorrect attributes. We managed these errors by manually inspecting the dataset and correcting the errors, wehre appropriate. In addition, we developed topology datasets in ArcGIS (v. 10.5) to get rid of certain topological defects (e.g., overextended line and gap between lines). The layers were then used to create a network dataset within a geodatabase.

Comment 4: Figures 1 and 2 unnecessarily increased the volume of the paper. I’d suggest combining them in a single figure.

Response 4: Thanks. We have combined Figs. 1 and 2 in the revised manuscript.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 5: Are the results (or the method) sensitive to this specific study area or can be scaled up?

Response 5: Thanks. The study has wider ramifications given urban populations are skyrocketing globallyup. Although the results of a location-allocation model are often limited to the study area and may not be readily transferable to other areas or cities, we believe the method can be scaled up to other setting. The above statements were also included in the revised manuscript. Please see the page 20 and lines 325-333.

Comment 6: Regarding the solutions, are these solutions Feasible or Optimal solutions? Please make this distinction both in the results and in their discussion.

Response 6: Thanks for your comment. We can infer that the solutions presented in the study are feasible in the sense that they result in the allocation of hospitals that effectively serve a substantial number of districts in the study area. However, they may not be considered optimal because there is a need to establish additional hospitals to cover all residents of the city. The discussion of the study would likely emphasize the feasibility of the proposed solutions in terms of improved coverage achieved by the allocated hospitals. However, it would also highlight the need for further optimization to ensure that the entire city receives timely and adequate healthcare services. The study might suggest that the current allocation, while feasible to some extent, falls short of being fully optimal in meeting the healthcare demands of the growing population. Please see the pages 20-21 and lines 334-343.

Comment 7: Please make sure your conclusions section underscores the scientific value added to your paper and/or the applicability of your findings/results. It would help if you enhanced your contributions and limitations, underscored the scientific value added to your paper, and/or the applicability of your findings/results and future study in this session.

Response 7: Thanks again. We have revised our conclusions according to your suggestion in the revised manuscript. The following texts are added (please see pages 21-22, lines 351-364).

“The approach employed in this study has important policy implications for future health management planning, not only in Jeddah city but also in similar contexts. Its service-agnostic nature means that the findings can be applied to various healthcare services beyond hospitals, such as clinics or specialized healthcare centers. This flexibility allows policymakers to adapt this approach to different healthcare needs and optimize the spatial distribution of services accordingly. Overall, this study not only contributes to the improvement of health services distribution but also highlights the importance of interdisciplinary assessment. By bringing together professionals from various fields, policymakers can effectively address challenges of healthcare provision and ultimately enhance overall well-being of urban populations.”

Response to Reviewer 2 Comments

Comment: I would like to thank the authors for this interesting work. Please consider shortening the paper by avoiding repetition and too much theoretical descriptions that could be resolved using brief description and references to original studies introducing the theories/concepts. My detailed comments can be found below:

Response: The authors would like to thank the reviewer for his excellent suggestions and comments. We are highly indebted to your valuable comments, which improved the quality of the manuscript.

The manuscript has been rechecked and appropriate changes have been made in accordance with the reviewers’ suggestions. A response document is prepared to indicate changes in the manuscript relative to the comments. Corrected/inserted/amended texts are highlighted in YELLOW in the revised manuscript.

Comment A: Abstract: “better serve better 94 districts” – please check

Response A: Thanks. It was amended in the revised manuscript. Please see the page 1 and lines 22-23.

Comment B: Introduction: The introduction is needlessly long. It would be better to remove some components that explain the applied model to the methodology section. The parts that could be considered for moving are listed below:

Response B: Thank you. We have modified our introduction section as per your suggestion.

Comment 1: “This work proposed a novel method…facility or service”

Response 1: Thanks. It was amended in the revised manuscript. Please see the page 6 and lines 128.

Comment 2: “Based on the preceding discussion…facilities?” – the research question should come at the end of this section.

Response 2: We have modified structure of the introduction section to enahnce readability. Please see the revised introduction section (pages 5-6, lines 100-126).

Comment 3: “The fuzzy overlay toolset…less-demand sites”

Response 3: We have modified structure of the introduction section to accommodate your observation (please see pages 5-6, lines 100-126).

Comment C: Methods

-Consider changing the heading of 2.1 to Study Area or a similar simpler heading

Response C: We have changed the heading in the revised manuscript.

Comment 1: 2.1 First para requires some references.

Response 1: We have included references according to your comment.

Comment 2: 2.1 A map of the study area could be useful.

Response 2: A study area map has been added.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 3: 2.3 Consider renaming the heading. Should be ‘Data Analysis’ or ‘MCDM Process’ or words similar to that effect. This is because all your sub-sections under section 2 are part of the methods.

Response 3: Heading and sub-heading have been revised accordingly.

Comment 3: 2.3 ArcGIS, which version?

Response 4: ArcGIS (V.10.5).

Comment 5: 2.3 When explaining the algorithm that was used to determine the default kernel, the term ‘weighted’ was used. It is important to mention what this implies and what kind of weight assignments were performed.

Response 5: Thanks for your comment. The specific weight assignments performed in the context of kernel determination can vary depending on the algorithm and the problem domain. However, the weights are generally used to assign relative importance or influence to different elements or factors within the algorithm. We have considered that all the elements have the same importance, so the weight would be the same for all, i.e., 1 in this case.

Comment 6: 2.3.2 Condense. There are too many introductory descriptions here. Figure 1 could be placed in 2.1 to give general readers an idea about the study area before the variables are introduced in the subsequent parts. Jeddah’s boundary should be included in the Figure.

Response 6: We have added a study area map in the revised manuscript.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 7: 2.3.2 Figure 1 and 2 could be merged. Consider showing the point locations of the hospitals and color gradation based on hospital numbers, as in Figure 2. The labels could be turned on to display hospital names on the points.

Response 7: Figures 1 and 2 were combined according to your suggestion in the revised manuscript.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 8: 2.3.4 Again, such a detailed description of the location-allocation model and its applications are unnecessary. Focus on the P-median model that you have applied, how it is novel, and how it was implemented in a GIS platform.

Response 8: We have revised section 2.3.4 according to your suggestion. Besides, we have included a flowchart of the P-Median problem workflow in the revision.

Figure 2. Schematic diagram, showing P-Median solution.

Comment 9: 2.3.4 Consider moving the mathematical description to a supplementary file. The paper is not method intensive. Therefore, too much focus on the mathematical model might distract the readers from the main objectives. Also, if you choose to include mathematical notations, there must be equation numbers, and every function (English alphabet) must be expressed with what they stand for. For example, it is unclear what the ‘a’, ‘d’, ‘x’ etc., stand for in the objective function. This description was provided at the end but every description must come underneath the equation when the function was first introduced. This part raises more questions than it answers. I would suggest simplifying this part and instead focusing on how this model was implemented by providing references to the P-median MCDM model from earlier studies as references.

Response 9: We have modified this section to enhance clarity and readability.

Comment 10: 2.3.4 Instead of such a detailed mathematical description, consider including a schematic diagram and its description to explain the study design or P-median implementation.

Response 10: We have included a flowchart of the P-Median workflow.

Figure 2. Schematic diagram, showing P-Median solution.

Comment 11: MCDM generally has a scaling factor applied to combine the multi-criteria variables. Please mention if this was done, and if yes, how.

Response 11: Thank you. In this study, min-max scaling method was considered. This method rescales the criteria values linearly to a common range, typically between 0 and 1. The formula for min-max scaling is:

Scaled value = (value-min) / (max-min)

Here, “value” represents the original value of a criterion, “min” is the minimum value observed for that criterion, and “max” is the maximum value observed. This scaling ensures that all criteria have a similar range and avoids giving undue importance to criteria with large values.

Comment 12: Figure 3: Scalebar has problems. The numbers should be spread out more in the beginning part.

Response 12: Thanks. The problem is addressed in the revision.

Figure 3. Number of beds in hospital derived by kernel density.

Comment 13: 3.2 First para repeats the methods. No need.

Response 13: Amended.

Comment 14: 3.2 Third and fourth para could be used to replace section 2.3.4

Response 14: Done.

Comment 15: Figure 6 (e.g., Candidate) and 7 (e.g., restriction) have symbols that do not exist in the map. Please revise. What does ‘lines’ mean?

Response 15: It was amended in the revised manuscript.

Comment 16: Conclusion Should be rewritten. The focus should not be on the application of GIS or fuzzy overlay or p-median. Rather, what you found in the study, specifically, what discrepancies in existing health facilities locations were revealed through the study analysis?

Response 16: We have revised our conclusions as per your suggestion.

Response to Reviewer 3 Comments

The manuscript presents a novel approach for the optimization of hospital locations in urbanized regions like Jeddah City, Saudi Arabia, based on a multi-criteria decision model (MCDM) and the P-median location-allocation model. By combining these techniques into an MC-P-median optimizer, the authors aim to aid health planners in determining the best locations for public hospitals. The study was conducted using a geodatabase of public hospitals, road networks, and population districts in Jeddah, with results suggesting the need for additional hospitals for comprehensive coverage.

Response: The authors would like to thank the reviewer for his excellent suggestions and comments. We are highly indebted to your valuable comments, which improved the quality of the manuscript.

Major Concerns:

Comment 1: The major concern with this manuscript is its structure and organization, which presently do not facilitate a clear understanding of the work's aims, objectives, or hypotheses, particularly how these are addressed in the research findings.

Response 1: The reviewer’s concerns regarding the manuscript’s structure and organization are taken care of. Therefore, we have addressed these issues and improved clarity and coherence of the work. However, separating the results and discussion sections would make our manuscript less readable. Hence, we added them in one section.

Minor Observations:

Comment 2: It's unclear from the manuscript whether the road network data was used in distance calculations.

Response 2: We have apologized for this vague statement. The road network data was used in distance calculations. Basically, in this study, two types of impedance cutoffs (cost functions) were considered: travel distance (km) and travel time (minutes).

Comment 3: The figure captions need to be improved for better clarity and to guide the reader’s focus within the figure.

Response 3: They have been amended in the revised manuscript.

Comment 4: The authors mention localities adjacent to Jeddah City in the problem setting, but the study appears limited to the city's boundaries. The impact of this exclusion on the model's outcomes should be addressed.

Response 4: You are correct in pointing out that the study appears to be limited to the boundaries of Jeddah city, despite mentioning of adjacent localities in the problem set. The authors acknowledged this limitation and discussed its implications for the model’s outcomes and generalizability in the revised manuscript.

By excluding adjacent localities from the study area, the results may not fully capture broader healthcare needs and dynamics of the entire region. However, this limitation could not affect overall accuracy and reliability of the optimized locations for healthcare facilities, as the model may not account for potential demand from neighbouring areas or the influence of population movement across boundaries. Moreover, to address this issue, the authors emphasized the need for further research.

Comment 5: The text following the aim statement in the introduction seems redundant and could potentially be removed.

Response 5: It was amended.

Comment 6: The authors should consider merging Figures 1 and 2 to better convey the number of beds information. A consistent, rounded scale could also improve reader comprehension.

Response 6: Figures 1 and 2 were combined according to your suggestion.

Figure 1. Location of public hospitals with their bed capacity in Jeddah city, KSA.

Comment 7: On page 14, the formula for a linear programming problem should be contextualized for the current problem, explaining how various parameters in the equation are applicable in this context.

Response 7: Thanks. Considering your comment and in line with improving readability of methods section, the authors have deleted the formula and instead include a flowchart illustrating the workflow of P-Median solution. The graphical representation provides a concise methodological overview, enabling researchers to grasp the key steps. Ultimately, this revision contributes to overall readability of the manuscript and enhances clarity of the study’s approach to the readers.

Comment 8: The scale inconsistency across Figures 3 to 7 can be confusing for readers. The authors should ensure the scales remain consistent for the same aerial extent.

Response 8: We have revised all the figures in the revision and ensured the scales remain consistent for the same aerial extent.

Comment 9: Figures 3 to 5 lack internal reference points to aid reader comparison and understanding. The inclusion of recognizable features, such as significant points or road junctions, could improve this.

Response 9: We have revised all the figures and eliminated redundant features or symbols to improve readability.

Comment 10: In conclusion, while the manuscript offers a promising contribution to the field of health service optimization, certain adjustments are necessary to enhance the clarity and impact of the findings. It is recommended that the authors carefully address the above concerns and observations in their revisions.

Response 10: Thanks for your suggestion. According to your recommendation, we have carefully addressed your concerns and observations in the revised manuscript.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0294819.r003

Decision Letter 1

Mohammed Sarfaraz Gani Adnan

6 Nov 2023

PONE-D-23-14949R1Optimizing health service location in a highly urbanized city: multi criteria -P-Median model for public hospitals in Jeddah city, KSAPLOS ONE

Dear Dr. Murad,

Please submit your revised manuscript by Dec 21 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Mohammed Sarfaraz Gani Adnan, PhD

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Given the combination of the results and discussion sections, it is essential to provide comprehensive and critical discussions of the results obtained in this study. Such discussions are necessary to validate and justify the key findings of this research.
Regarding Figure 3-5, it is recommended to ensure consistency in the layout, such as positioning the mapping components in a similar fashion. Additionally, consider the possibility of combining these figures into one layout for clarity. Overlaying the locations of hospitals on these maps would enhance the reference points for readers, particularly when making geographical comparisons with Figure 1.
For the classification range, it is advisable to reconsider the current classes, which appear somewhat arbitrary (e.g., 6781.93 to 9040.88). A more reader-friendly and intuitive approach would be to use rounded ranges, such as 6500 to 9000, which will be easier for readers to comprehend.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Reviewer #2: Thanks for addressing the comments. It's in much better shape now.

Please check the journal guidelines for further instructions on scope of improvement.

Reviewer #3: (No Response)

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PLoS One. 2024 Jan 2;19(1):e0294819. doi: 10.1371/journal.pone.0294819.r004

Author response to Decision Letter 1

8 Nov 2023

Response to the editor comments

Dear Academic Editor

I am pleased to resubmit to you my new paper titled: Optimizing health service location in a highly urbanized city: multi criteria -P-Median model for public hospitals in Jeddah city, KSA

We have carefully reviewed the editor’s comments and addressed almost them in the revised manuscript. All revised portions are marked in YELLOW in the revised manuscript, which we would like to submit for your kind consideration. The authors pay special thanks to the editor and reviewer because their comments and suggestions have greatly improved quality of the manuscript.

I hope that this paper can be accepted for your journal.

Best Regards

Prof. Abdulkader Murad

Additional Editor Comments:

Comment 1: Given the combination of the results and discussion sections, it is essential to provide comprehensive and critical discussions of the results obtained in this study. Such discussions are necessary to validate and justify the key findings of this research.

Response 1: Thank you for your comments. The comprehensive discussion of the study’s results validates the critical importance of addressing healthcare access disparities in urban areas, as revealed by the spatial distribution of resources in Jeddah. The selection of the fuzzy ‘And’ model, P-Median model, and the integration of the MC-P-Median model with fuzzy overlay is justified by their ability to optimize resource allocation, tackle uncertainty, and eliminate redundancy. The findings emphasize that while the current allocation is deemed feasible, further optimization is imperative to meet the healthcare demands of Jeddah’s growing population. These results have broader implications for urban healthcare planning globally, underscoring the need for data-driven, context-specific strategies and advanced spatial decision support systems to ensure equitable and efficient healthcare services as urban populations continue to expand. Please see pages 20-21 and lines 354-381.

…. Added discussion….

“One of the key takeaways from this study is the utilization of a GIS for healthcare planning. Integration of GIS technology with hospital locations enabled a detailed analysis of the distribution of healthcare resources and service demands. Combining P-Median model and fuzzy overlay techniques in a spatial support system addressed complex issues related to healthcare access and resource allocation. Results emphasized the importance of population density, the proximity of healthcare facilities, and travel times in healthcare planning. This approach recognized that healthcare accessibility is not solely dependent on number of hospitals but also on their strategic placement relative to population centers. Furthermore, the study also underscored significance of understanding local context, including factors like available resources, population density, and nature of demand locations when determining travel time thresholds. This adaptability and context-based decision-making are vital for effective healthcare planning.

The study asserts that integration of fuzzy overlay with MC-P-Median approach is superior to traditional methods, such as buffer analysis or distance-based catchment modelling. The findings related to the need for additional hospitals to cover all residents in Jeddah are a crucial finding and can help policy makers to enhance healthcare facilities situation. While the current allocation is deemed feasible, it is acknowledged that further optimization is necessary to ensure comprehensive healthcare coverage for the growing population. This raises questions about the scalability of the proposed models to other urban areas with increasing populations, emphasizing the need for ongoing research and adaptability in healthcare planning strategies.

This work has some limitations including its focus was solely on Jeddah city. This limitation may affect comprehensive understanding of healthcare needs and dynamics in the broader region. The discussion should have the implications for not accounting for potential demand from neighbouring areas and the importance of regional dynamics in healthcare planning. In conclusion, this study offers a comprehensive and innovative approach to allocating healthcare facility and optimizing in urban areas. However, the findings underscored the importance of data-driven and context-specific strategies in addressing complex issue associated with healthcare accessibility for growing urban populations.”

Comment 2: Regarding Figure 3-5, it is recommended to ensure consistency in the layout, such as positioning the mapping components in a similar fashion. Additionally, consider the possibility of combining these figures into one layout for clarity. Overlaying the locations of hospitals on these maps would enhance the reference points for readers, particularly when making geographical comparisons with Figure 1.

Response 2: We greatly appreciate the editor’s valuable input regarding the layout and presentation of our figures. We have agreed that consistency in the positioning of mapping components is essential for a coherent presentation. We have ensured that the layout of Figure 3-5 has been standardized to make it easier for readers to navigate and compare the information. Additionally, we have explored the possibility of combining these figures into one layout to enhance clarity and facilitate geographical comparisons, as suggested. We have also overlaid hospital locations on these maps to provide readers with clear reference points, which has improved the accessibility of our findings.

Figure 3. a. Number of beds in hospital derived by Kernel density. b. Population density in Jeddah as a measure of health demand. c. A provider-to-population result-based fuzzy overlay analysis. The public hospitals better serve locations in red, while locations in green need additional hospitals (The figure made with ArcGIS v.10.5 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources)). [Shape file or base map that is freely available at https://www.diva-gis.org/gdata and do not have copyright restrictions.]

Comment 3: For the classification range, it is advisable to reconsider the current classes, which appear somewhat arbitrary (e.g., 6781.93 to 9040.88). A more reader-friendly and intuitive approach would be to use rounded ranges, such as 6500 to 9000, which will be easier for readers to comprehend.

Response 3: We appreciate the editor’s feedback regarding the classification range in our study. We agree that using more reader-friendly and rounded ranges, such as 6500 to 9000, could enhance the clarity and comprehension of our data. We have revisited the classification ranges and made the necessary adjustments to ensure that our readers can easily interpret the information in the revised manuscript.

Attachment

Submitted filename: Response Letter_PONE-D-23-14949R2.docx

Click here for additional data file.^{(841.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0294819.r005

Decision Letter 2

Mohammed Sarfaraz Gani Adnan

10 Nov 2023

Optimizing health service location in a highly urbanized city: multi criteria -P-Median model for public hospitals in Jeddah city, KSA

PONE-D-23-14949R2

Dear Dr. Murad,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Mohammed Sarfaraz Gani Adnan, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0294819.r006

Acceptance letter

Mohammed Sarfaraz Gani Adnan

20 Dec 2023

PONE-D-23-14949R2

PLOS ONE

Dear Dr. Murad,

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At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Supplementary Materials

S1 Data

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Data Availability Statement

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PERMALINK

Optimizing health service location in a highly urbanized city: Multi criteria decision making and P-Median problem models for public hospitals in Jeddah City, KSA

Abdulkader Murad

Fazlay Faruque

Ammar Naji

Alok Tiwari

Emad Qurnfulah

Mahfuzur Rahman

Ashraf Dewan

Roles

Abstract

1. Introduction

2. Materials and methods

2.1 The study area

Fig 1. Location of public hospitals with their bed capacity in Jeddah City, KSA (The figure is made with ArcGIS software (https://www.esri.com/)).

2.2 Data

Table 1. Data used in this study.

2.3 Data analysis with MCDM process

2.3.1 Fuzzy overlay

Fig 2. Schematic diagram, showing P-Median solution (The figure is made with Microsoft word v.2016 (https://www.microsoft.com/en-us/download)).

3. Results and discussion

Fig 3.

Fig 4. LAM for hospitals, based on the P-Median problem (here, straight lines connect each demand-point/facility pair).

Fig 5. LAM for hospitals, based on optimized P-Median considering MC location problem model (here straight lines connect each demand-point/facility pair).

Table 2. MC-P Median-based optimization considering different impedance cut-offs (shaded cells indicate maximum demand locations for each facility).

4. Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Mohammed Sarfaraz Gani Adnan

Roles

Author response to Decision Letter 0

Decision Letter 1

Mohammed Sarfaraz Gani Adnan

Roles

Author response to Decision Letter 1

Decision Letter 2

Mohammed Sarfaraz Gani Adnan

Roles

Acceptance letter

Mohammed Sarfaraz Gani Adnan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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