|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2023 | Volume
: 14
| Issue : 1 | Page : 55 |
|
A fuzzy BWM-TOPSIS framework for identifying and prioritizing measures to overcome obstacles to the regionalization of Iranian healthcare services
Zahra Zare1, Arash Haqbin2, Khalil Kalavani3, Payam Shojaei2
1 Department of Health Services Management, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Management, Shiraz University, Shiraz, Iran
3 Department of Health Services Management, Research committee student, Kerman University of Medical Sciences, Kerman, Iran
| Date of Submission | 07-Apr-2022 |
| Date of Acceptance | 27-Oct-2022 |
| Date of Web Publication | 28-Apr-2023 |
Correspondence Address:
Payam Shojaei
Department of Management, Shiraz University, Shiraz
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijpvm.ijpvm_114_22
Background: One of the most effective strategies to improve the access of community members to health services is to regionalize health services. The purpose of this study is to examine and prioritize measures that could help to counteract obstacles and problems in implementing the regionalization of healthcare in Iran. Methods: The study relied on a mixed research method, including qualitative and quantitative phases. First, by conducting semi-structured interviews and analyzing them through qualitative content analysis, the obstacles and measures were identified. In the quantitative phase, the obstacles identified were weighted using the fuzzy best-worst method (FBWM), and the measures were then prioritized through the fuzzy TOPSIS (FTOPSIS) method. Results: The obstacles were categorized into four main dimensions: “infrastructural,” “political,” “human resources,” and “managerial.” Among the 15 obstacles identified, “absence of performance guarantees” was the most important obstacle, while “insufficient education” was the least important obstacle to the regionalization of healthcare services in Iran. Meanwhile, the following eight measures that could help to overcome the obstacles were extracted from the interviews: “conducting a needs assessment,” “providing clinical guidelines,” “employing specialized human resources,” “reinforcing the referral system,” and “preparing electronic health records,” “enhancing education and information dissimilation,” “building executive support,” and “providing cost-effective equipment and technology.” “Employing specialized human resources” was also the most effective measure to overcome the obstacles. Conclusions: Iranian healthcare policy-makers can use the empirical findings of this investigation to accelerate the implementation of Iran's regionalization plan to improve the access of community members to healthcare services.
Keywords: Fuzzy best-worst method, fuzzy TOPSIS, medical services, regionalization
How to cite this article:
Zare Z, Haqbin A, Kalavani K, Shojaei P. A fuzzy BWM-TOPSIS framework for identifying and prioritizing measures to overcome obstacles to the regionalization of Iranian healthcare services. Int J Prev Med 2023;14:55 |
How to cite this URL:
Zare Z, Haqbin A, Kalavani K, Shojaei P. A fuzzy BWM-TOPSIS framework for identifying and prioritizing measures to overcome obstacles to the regionalization of Iranian healthcare services. Int J Prev Med [serial online] 2023 [cited 2023 Jun 8];14:55. Available from: https://www.ijpvmjournal.net/text.asp?2023/14/1/55/375440 |
| Introduction | |  |
Providing access to healthcare services represents one of the crucial aspects of a functional healthcare system. The indisputable significance of facilitating services to communities of people has been a criterion for evaluating the functionality of health policy.[1],[2],[3] However, access to health services is a complex concept; generally speaking, it can be defined as individuals' or communities' right to access a specific service, provider, or institution to address their medical needs.[4] Despite the significant growth of health indicators at the national level, there is marked inequality between social groups and geographical areas in terms of their access to healthcare services.[5] Numerous studies have shown that there are problems making it difficult for people to access healthcare services in different parts of Iran.[6] Accordingly, one of the main goals of policy-makers in the health sector of every country is to facilitate access to healthcare services so that all segments of society can effectively benefit from such services.[6] One of the most effective ways to improve people's access to health services is to adopt rationing policies or regionalization of health services.[7],[8] The World Health Organization defines regionalization as the rational distribution of healthcare across a country in a process that ensures the availability and cost-effectiveness of services/facilities at all healthcare levels (primary, secondary and tertiary).[8]
As such, regionalization helps to achieve better health-related conditions while providing full access to the healthcare network at regional/local levels by decentralizing geographical areas and concentrating healthcare activities within a unified structure.[7],[9] In addition, the regionalization of healthcare services arranges healthcare units/facilities in a way that would improve access to the range of services available to the public, at the lowest cost and with the best quality.[10]
Of course, the successful implementation of regionalization plans, as practiced in some countries such as Canada, Brazil, and the United Kingdom,[9] demands several basic requirements. The most fundamental concerns are specifying the responsibility of each facility and its sub-units, matching supply with people's demand (needs assessment), building a referral system, managing a continuous flow of mutually shared information, establishing scientific and technical support mechanisms, designing a proper organizational arrangement, and establishing partnerships between health professionals and technicians.[11],[12]
In this process, it would be extremely important to prioritize the requirements for the implementation of regionalization by using scientific and effective methods to formulate policy interventions and practical procedures. One of the frequently used prioritization methods is multi-criteria decision-making (MCDM). From a scientific perspective, MCDM models explore problems in which the number of alternatives is already determined, and the decision-maker selects, prioritizes, and ranks a limited number of alternatives.[13]
The purpose of this study is to identify and prioritize the obstacles to the regionalization of healthcare plans while finding and prioritizing measures that could help to overcome the obstacles in Iran. Although the issues of stratification and regionalization of healthcare services have surfaced in Iran in recent months, regionalization has not yet been fully accomplished in the country. Meanwhile, no study has focused on the prioritization of the measures that could contribute to the implementation of the regionalization plan in Iran. Therefore, this research seeks to prioritize both the obstacles to the implementation of this plan and the measures to overcome the obstacles in Iran.
| Methods | | |
The primary purpose of this study was to prioritize the potential measures to counteract the obstacles to the regionalization of healthcare services in Iran. To accomplish this, the study relied on a mixed research design composed of both qualitative and quantitative phases. The methodology of this study is based on qualitative content analysis and soft operations research techniques. Each method is regulated and referred to as a valid reference.
Primarily, in the qualitative phase, the obstacles and the measures were identified through semi-structured interviews with 21 experts selected through the purposeful sampling method [Table 1]. The experts were selected based on their experience in the management and policy-making of health systems and their rich background of valid research in the field of regionalization of health services.
The content of the obtained interviews was then analyzed in MaxQDA 10.1 software according to the following steps suggested by Mayring[14]: identifying and collecting data, determining coding categories, checking validity and reliability, and presenting the results.
The entire content was broken into categories that simplified the analysis and contributed to the manageability of the content. In doing so, primarily, the interviews were transcribed, and the resultant texts were inspected several times so that they could be holistically understood. Next, following an informed understanding of the texts, codes were assigned to the keywords and phrases reflecting the participants' opinions. The coding procedure was examined and reviewed several times in joint sessions, and the content of the interviews was categorized based on conceptual similarities and differences through a focus group discussion.
Then, to measure the validity and reliability, the four criteria proposed by Guba and Lincoln were used: credibility, transferability, dependability, and confirmability. These factors ensured the validity, accuracy, and reliability of the qualitative data.[15] To include various experts, maximum variation sampling was used. The voice-recorded interviews were meticulously analyzed, while notes were taken during each interview session. The codes extracted from the interviews were submitted to the participants who evaluated the ideas. After implementing the corrections suggested by the participants, the codes were finally confirmed. To increase the transferability factor, highly experienced managers/specialists from various fields of specialty were included. Of course, many of the findings obtained from the interviews were already confirmed in other studies.
To verify the reliability, the interviews were separately coded by two analysts and were accurately compared with the codes observed by the research team. The findings were also monitored by experts in qualitative research. As the three criteria mentioned above were precisely controlled, it could be argued that confirmability was automatically established as well. After the analysis, several sets of information were organized and stored as a file. Next, it was necessary to present the findings in the form of a report that could be easily understood by readers. In doing so, the researchers reviewed the final results, identified possible patterns, sorted all the information sequentially, and finally prepared a full report.
In the next phase, the obstacles and the measures identified were further analyzed quantitively through MCDM methods in a fuzzy environment. The weights of the obstacles were calculated according to the fuzzy best-worst method (FBWM) processed in LINGO 18 software. Then, the measures were prioritized according to the fuzzy TOPSIS (FTOPSIS) technique. To collect data, copies of a questionnaire were submitted to six of the panel experts who were more familiar with regionalization. Below, the FBWM and FTOPSIS are explained in more detail.
Fuzzy best-worst method (FBWM)
The best-worst method was developed by Rezaei[16] for the first time. This method benefits from fewer pairwise comparisons and more consistent results compared to other weighting techniques.[16] However, to cope with the vagueness and uncertainty of the decision-making environment, Guo and Zhao[17] integrated the classic BWM with fuzzy set theory to reduce ambiguities in the experts' judgments and to obtain more reliable results. According to Guo and Zhao,[17] the steps of Fuzzy BWM are as follows:
Step 1. First, a set of decision criteria will be determined, which are depicted as {C1, C2,…, Cn}. Then, the best (most important and most desirable) and worst (least important or least desirable) criteria will be identified. The best criterion is CB, whereas the worst criterion is CW.
Step 2. Next, the preference of the best criterion in comparison to other criteria will be determined according to the linguistic scale mentioned in Guo and Zhao[17] from equally important to absolutely important. The best-to-others vector is depicted by:
where aBj indicates the preference of the best criterion (B) over criterion j, and: aBB = (1, 1, 1).
Similarly, the preference of the worst criterion compared to other criteria will be determined, and The others-to-worst vector is as follows:
Where ajW indicates the preference of criterion j over the worst criterion (W). Needless to say: aww = (1, 1, 1)
Step 3. Then, the optimal weights  will be found. To calculate the optimal weights of each criterion, the following model must be solved.
To solve the above-mentioned model, it can be transformed into the following nonlinearly constrained optimization model, where ɛ̃ is also a TFN.
min ɛ̃
Since lε ≤ mε ≤ uε, it can be assumed that ɛ̃* = (k*,k*,k*) and k* ≤ lε. Thus, the model can also be transformed to:
min ɛ̃*
By solving the model in Eq. (7), the optimal weights  will be determined.
Step 4. Finally, the consistency of the model must be calculated. The consistency ratio (CR) is a significant index to evaluate the consistency degree of the pairwise comparison. According to Rezaei,[16] models with a CR, less than 0.1, are considered consistent. For the steps of calculating the CR, readers can refer to Guo and Zhao.[17]
Fuzzy TOPSIS (FTOPSIS)
Being one of the inveterate multicriteria decision-making techniques, TOPSIS was first developed by Hwang and Yoon.[18] The foundation of TOPSIS is based on the concept that the best alternative should have the shortest distance from the positive ideal solution (PIS) and the farthest from the negative ideal solution (NIS). Similar to BWM, in the classic TOPSIS, the experts express their judgment using crisp values. However, to increase the reliability of the results, the fuzzy set theory could be integrated by TOPSIS. In the following, the steps of Fuzzy TOPSIS proposed by Patil and Kant[19] are explained:
Step 1. First, a linguistic scale for rating the alternative with respect to criteria must be selected. To this end, suppose there are m alternatives called A = {A1,A2,...,Am}and n criteria, C = {C1,C2,...,Cn}. Also, the weights of each criterion are shown by Wj(j = = 1, 2.,n), and the performance ratings of each expert are Dk (K = 1, 2., k) Ai (i = 1, 2,…, m) with respect to criteria Cj (j = 1, 2., n) are shown by R̃k = X̃ijk(i = 1, 2, ‥,m; j = 1, 2, ‥,n;k =1, 2, …,k) membership function µR̃k(x). The linguistic scale for rating the alternatives is mentioned in Patil and Kant[19] from very poor to very good.
Step 2. In the Next step, the aggregated fuzzy ratings for all the alternatives must be calculated. Assume the fuzzy ratings of all experts are described as TFN R̃k = (ak,bk,ck), k = 1, 2,…,k then the aggregated fuzzy rating is given by R̃k = (a,b,c), k = 1, 2,…,k where,
Step 3. After aggregating the fuzzy ratings for all the experts, the fuzzy decision matrix will be developed. The fuzzy decision matrix for the alternatives (D̃) is as follows
Step 4. The constructed fuzzy decision matrix then must be normalized to bring all the criteria scales into a comparable one. The normalized fuzzy decision matrix R̃ is calculated according to linear scale transformation as follows:
Also:
Step 5. Next, the weighted normalized matrix must be calculated by multiplying the weights (Wj) of the criteria with the normalized fuzzy decision matrix as follows:
Note that Vij is a TFN shown by  .
Step 6. The fuzzy ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) must be determined as follows:
Step 7. Afterward, the distance of each alternative from FPIS (di+) and FNIS (di-) must be determined as follows:
Step 8. Finally, the closeness coefficient (CCi) for each alternative must be computed. The closeness coefficient CCi shows the distances to the fuzzy PIS (A*) and the fuzzy NIS (A¯) simultaneously and is determined as follows:
The alternatives can be prioritized by CCi. Higher CC means a more ideal alternative.
Ethical approval and consent to participate
The Ethical Approval Code is IR.KMU.REC.1400.528. Moreover, consent for participation was taken from the experts in written and oral form.
| Results | | |
By analyzing the content of the interviews with the experts, two themes of regionalization implementation obstacles and measures to overcome them were identified, each of which had its own category and codes [Table 2].
To calculate the weights of each obstacle through the FBWM, the four dimensions obtained from the analysis of experts' interviews were used. Meanwhile, the following eight measures that could help to overcome the obstacles were extracted from the interviews and were also listed for weighting, and finally, the study model was designed. [Figure 1] illustrates the MCDM model utilized in this study.
After constructing the MCDM model, the overall weight of each obstacle was calculated through the FBWM. To this end, five separate questionnaires for pairwise comparisons (including the main dimensions and obstacles falling under each dimension) were submitted to the experts so that they could express their judgments based on the linguistics mentioned by Guo and Zhao.[17] The FBWM was developed and solved according to Eqs. (3)–(7). The consistency of each questionnaire was determined by calculating the consistency index mentioned by Guo and Zhao.[17] After ensuring that all the questionnaires were consistent, the final weight of each obstacle was computed by multiplying each measure's weight in their dimension by the weight of each dimension. Moreover, the arithmetic mean, as the most frequently used technique for aggregating individual priorities, was used to aggregate the weights specified by each expert.[20] [Table 3] shows the weights of the main dimensions and their respective obstacles.
The fuzzy weights were then used to prioritize the measures through the FTOPSIS technique. To accomplish this, the experts were asked to use the linguistic scale mentioned in Patil and Kant[19] and construct their decision matrices by comparing each measure in light of the obstacles.
Then, the aggregated fuzzy decision matrix was constructed using Eq. (8). Next, the normalized fuzzy decision matrix and the weighted normalized matrix were constructed via Eqs. (9)–(13). Because all the factors were obstacles, they were “cost” elements by nature. After that, the fuzzy ideal solution (FPIS or d+) and fuzzy negative ideal solution were calculated (FNIS or d−) through Eqs. (14)–(15), the CC was computed for each alternative according to Eqs. (16)–(18). The alternatives were ultimately prioritized based on their CC measures. [Table 4] shows the FPIS, FNIS, and CC values for each alternative.
As mentioned earlier, the ideal alternative had the highest CC value. Therefore, according to [Table 4], the best measure to overcome the obstacles to the regionalization of healthcare services in Iran was employing specialized human resources (A3), followed by providing cost-effective equipment and technology (A8), conducting a needs assessment (A1), enhancing education and information dissimilation (A6), preparing electronic health records (A5), building executive support (A7), and reinforcing the referral system (A4). Furthermore, providing clinical guidelines (A2) was the least significant measure that could help to overcome the obstacles.
| Discussion | | |
After developing an MCDM model, the FBWM-TOPSIS method was used as a comprehensive and systematic method to determine the priorities of the obstacles to the regionalization of healthcare services, as well as the priorities of the measures to overcome these obstacles. The results of the FBWM revealed that, among the “managerial,” “political,” “human resources,” and “infrastructural” dimensions of the obstacles, the “managerial” dimension was the most important factor, followed by “infrastructural,” “political,” and “human resources,” respectively. More specifically, the results [see [Table 3]] obtained by analyzing the experts' opinions suggested that, among the 15 obstacles identified, the main factors were as follows:
- The absence of performance guarantees: When implementing new policies in the healthcare system, guarantees are used to ensure the successful implementation of plans, as well as the provision of the necessary prerequisites/infrastructure. In this relation, trustees, policy-makers, and other stakeholders in the healthcare system must undertake the obligation to establish input agencies/organizations and to form a law-making team so that they can monitor the implementation of regionalization plans and ensure accountability during such plans; Neville et al.[21] also identified this as a barrier.
- A lack of a strong decision-making team: Decision-making in relation to healthcare issues is very important in the face of increasing social change. Therefore, there should be an expert and specialized decision-making team that can help the healthcare system to accomplish its main goals (e.g., to meet the needs of people and society, improve the health of individuals, and provide quality services) through evidence-based planning, despite many issues and problems;
- The arrangement and distribution of services: The arrangement of units providing healthcare services and the distribution of such services among different regions of a country should be accomplished in a way that can improve people's access to services. Moreover, community members should access quality health services at the lowest cost. The arrangement of services should also take into account local barriers, including the culture and customs of community members and the customs of each region;
- Failure to implement evidence-based planning: In the current unstable period, it would be highly important to rely on systematic and continuously updated statistics and evidence in areas such as problem identification, planning, and selection. As such, evidence-based planning can clarify the policy-making and planning process, detect possibly existing shortcomings, and specify the necessary infrastructure for the implementation of regionalization plans; This result was consistent with the study of Neville et al.[21]
- A lack of clear and proper guidelines: One of the main obstacles to the implementation of the regionalization plan is a lack of accurate and specific clinical guidelines., Formulating guidelines based on current and updated knowledge is an attempt to improve the provision of clinical services, which not only helps physicians/patients in the medical practice process[22] but also serves as a planning tool for policy-makers and healthcare providers.[23] The design of these guidelines raises people's awareness of these activities. An efficient referral system, in turn, contributes to the proper regionalization of healthcare services. This result was consistent with the study of Seymour and Kahn.[24]
It should be noted that in various articles, other obstacles such as education and information,[21],[22],[23],[24],[25] political pressures, lack of evidence-based programming,[21] lack of expert human resources, local barriers,[25] inter-sectoral cooperation,[26] and stakeholder pressures[24] were identified, which are consistent with the results of this study.
The results [see [Table 4]] also revealed the following necessary measures that the experts found important in overcoming the existing obstacles:
- Employing specialized human resources: Every organizational strategy/policy must consider the human resources working for the organization. The success or failure of the organization depends entirely on how human resources are recruited and retained.[27] Therefore, skilled and specialized human resources would have a significant role in various areas, such as policy-making, planning, project implementation, and other issues; this result was consistent with the study of de Oliveira and Artmann.[25]
- Providing cost-effective equipment and technology: Another way to overcome the obstacles to the implementation of the regionalization plan is to provide technology, infrastructure, and equipment compatible with medical goals. More specifically, the technology effectively increases productivity, enhances workplace safety and facilities operations, reduces staff workload, and reduces potential risks to staff members;
- Conducting a needs assessment: To implement the regionalization of healthcare services, a needs assessment can help to identify urgent needs in each area/city where, for instance, a hospital should be established or patients should be transferred from one area to a neighboring one. Before implementing this plan, policy-makers and healthcare providers must gain complete information about the population coverage of the region in question, its specialized needs, its disease burden, and even its demographic profile; this result was consistent with the study of Lewis.[28]
- Enhancing education and information dissimilation: Educating community members and training human resources can serve as an effective measure to overcome the obstacles to the implementation of regionalization plans. To overcome local barriers, as highly important issues, it is possible to educate people in the community through various channels such as local networking programs, lectures delivered by well-known and popular people in the area, or local gatherings[29]; Articles also mentioned this point.[30],[31]
- Preparing electronic health records: The full implementation of electronic health records in the Iranian health system represents one of the important strategies in formulating a regionalization plan. Having access to integrated and organized available data of patients is one of the main goals of the electronic health record. Preventing rework in diagnostic and clinical trials and tests, along with avoiding the risks of drug-prescribing interference, constitutes one of the basic infrastructural aspects that could strengthen the referral system and subsequently coordinate the series of medical services provided in the regionalization system;
- Building executive support: As the experts suggested, another way to overcome obstacles is to foster executive support when implementing a healthcare services regionalization plan. During the implementation of such a plan, various approaches take shape. Yet, according to the experts, scrutinizing the integral elements of the implementation plan and post-implementation inspection can serve as practical strategies to overcome the obstacles to regionalization plans; This result was consistent with the study of Neville et al.[21]
- Reinforcing the referral system: In actualizing regionalization plans, the referral system has the important function of providing services in the form of a consistently evolving chain. In this process, if a patient needs more specialized services, s/he could be transferred to a specialized service provider unit. This procedure would help patients to stir clear of complexities in specialized medical centers. This advantage is made possible by providing care at different levels by using the referral system. However, given the inefficiency of the presently used referral system, which addresses referrals based on informal communication and patients' personal decisions, policy-makers should strengthen the referral system. This result was consistent with the study of Seymour and Kahn.[24]
- Providing clinical guidelines: As mentioned in the previous section, one of the main obstacles to regionalization is a lack of specific and accurate clinical guidelines. Therefore, formulating clinical guidelines based on real-world evidence can further enhance the ability of the measures to overcome obstacles. This result was consistent with the study of Seymour and Kahn.[24]
| Strengths and Limitations | | |
The findings of this study can provide a foundation for policy-makers and decision-makers responsible for the health system. Using the findings, they can understand the obstacles to the implementation of regionalization plans and have an insight into the necessary strategies to increase such plans' success rate. The observations are highly important because the regionalization of healthcare services has not been seriously implemented in Iran. From the perspective of research, too, there is a dearth of studies in the literature in relation to the obstacles to regionalization and the necessary measures to overcome these obstacles. In addition, this study ranked the factors based on their importance levels, which will improve decision-making in terms of regionalization and its successful implementation.
Although in-depth interviews were used to collect data in this study, some interesting findings were observed. Yet, the findings reflected the observations of the experts reporting the situation in a developing country; therefore, caution should be exercised in generalizing the findings of this study to other communities. Similar studies conducted in other developing or advanced countries could help to discover other obstacles through other MCDM techniques.
| Conclusions | | |
A successfully implemented regionalization of healthcare services in Iran would demand due consideration of obstacles existing in the process and the necessary measures that could help to overcome these obstacles. The main contributions of this study were the identification and prioritization of the most important obstacles, along with the evaluation of the most effective measures to overcome the obstacles that would lead to unsuccessful/incomplete implementations of regionalization plans. Such measures could be practiced step-by-step, as it would be hardly possible to implement them simultaneously in the healthcare system.
Although several measures have been adopted in Iran, such as electronic health records and the referral system, more efforts are needed to reinforce and fully accomplish the regionalization of healthcare services in the country. The present study relied on an integrated version of fuzzy BWM-TOPSIS to compute the weights of the obstacles and to rank healthcare regionalization solutions. The findings revealed that the main obstacles to the implementation of regionalization were “the absence of performance guarantees,” “a lack of a strong decision-making team,” “the arrangement and distribution of services,” “failure to implement evidence-based planning,” and “the existence of clear and proper guidelines.”
In addition, the evaluation and ranking of the measures showed that “employing specialized human resources” and “providing cost-effective equipment and technology” were among the most important measures that could contribute to the regionalization of healthcare services. The other important measures were “conducting a needs assessment,” “enhancing education and information dissimilation,” “preparing electronic health records,” “building executive support,” “reinforcing the referral system,” and “providing clinical guidelines.”
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Murray CJL, Evans DB, World Health Organization. Global Programme on Evidence for Health P. Health systems performance assessment : debates, methods and empiricism / edited by Christopher J. L. Murray, David B. Evans. Geneva: World Health Organization;2003.  |
| 2. | Penchansky R, Thomas JW. The concept of access: Definition and relationship to consumer satisfaction. Med Care 1981;19:127-40. |
| 3. | Shengelia B, Murray CJ, Adams OB. Beyond access and utilization: Defining and measuring health system coverage. Health Systems Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2003. p. 221-34. |
| 4. | Haddad S, Mohindra K. Access, opportunities and communities: Ingredients for health equity in the South. In Public Health and International Justice Workshop Carnegie Council on Ethic and International Affairs.2002. |
| 5. | Sefiddashti SE, Ghazanfari S. Ranking Islamic Republic of Iran's development vision countries in term of access to healthcare indicators. J Health Administration (JHA) 2014;17:Pe58-71. |
| 6. | Khayatan M, Nasiri Pour AA, Amini M, Mohamad Nejad SM. The effective factors on recievers' access to health care services in urban health care centers. J Payavard Salamat 2011;4:18-27. |
| 7. | Dueñas Fernández R. Regionalización de los servicios de salud para la asistencia médica en Cuba: Ejemplo del Cardiocentro Ernesto Che Guevara. CorSalud 2016;8:209-16. |
| 8. | Rashidian A, Omidvari AH, Vali Y, Mortaz S, Yousefi-Nooraie R, Jafari M, et al. The effectiveness of regionalization of perinatal care services-A systematic review. Public Health 2014;128:872-85. |
| 9. | Ramos MC, Barreto JO, Shimizu HE, Moraes AP, Silva EN. Regionalization for health improvement: A systematic review. PLoS One 2020;15:e0244078. |
| 10. | Rashidian A, Yousefi NR, Azemikhah A, Heydarzadeh M, Changizi N, Mahta BF, et al. Perinatal care regionalization and geographical distribution model in the Islamic Republic of Iran. HAKIM Res J 2008;11:1-11. |
| 11. | Álvarez Pérez AG, García Fariñas A, Rodríguez Salvá A, Bonet Gorbea M, De Vos P, Van der Stuyff P. La regionalización de los servicios de salud como una estrategia de reorganización sanitaria. Rev Cubana Hig Epidemiol 2008;46:1. |
| 12. | Roth-Deubel AN, Molina-Marín G. Rectoría y gobernanza en salud pública en el contexto del sistema de salud colombiano, 2012-2013. Rev Salud Pública 2013;15:44-55. |
| 13. | Jahangiri A, Jahangiri M. Performance evaluation of hospital by using Dynamic Multiple Attribute Decision Making (DMADM): A case study in Imam Khomeini hospital affiliated to social security organization in Arak 6. J Healthc Manag 2017;8:91-102. |
| 14. | Mayring P. Qualitative content analysis: theoretical foundation, basic procedures and software solution 2014:143. |
| 15. | Lincoln YS, Guba EG. But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Dir Program Eval 1986;1986:73-84. |
| 16. | Rezaei J. Best-worst multi-criteria decision-making method. Omega 2015;53:49-57. |
| 17. | Guo S, Zhao H. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl Based Syst 2017;121:23-31. |
| 18. | Hwang CL, Yoon K. Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. Springer, Berlin, Heidelberg; 1981. p. 58-191. |
| 19. | Patil SK, Kant R. A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Syst Appl 2014;41:679-93. |
| 20. | Mohammadi M, Rezaei J. Bayesian best-worst method: A probabilistic group decision making model. Omega 2020;96:102075. |
| 21. | Neville D, Barrowman G, Fitzgerald B, Tomblin S. Regionalization of health services in Newfoundland and Labrador: perceptions of the planning, implementation and consequences of regional governance. Journal of Health Services Research & Policy 2005 Oct;10(2_suppl):12-21. |
| 22. | Fretheim A, Oxman AD, Håvelsrud K, Treweek S, Kristoffersen DT, Bjørndal A. Rational prescribing in primary care (RaPP): a cluster randomized trial of a tailored intervention. PLoS medicine 2006 Jun;3:e134. |
| 23. | Yaşar I, Kahveci R, Baydar Artantaş A, Ayhan Başer D, Gökşin Cihan F, Şencan I, Koç EM, Özkara A. Quality assessment of clinical practice guidelines developed by professional societies in Turkey. PLoS One 2016 Jun 13;11:e0156483. |
| 24. | |
| 25. | Oliveira MD, Artmann E. Regionalization of health services: challenges for the Angolan case. Cadernos de Saúde Pública 2009 Apr;25:751-60. |
| 26. | Handley SC, Lorch SA. Regionalization of neonatal care: Benefits, barriers, and beyond. J Perinatol 2022;42:835-8. |
| 27. | Halmaghi EE, Bacila LM. Human resources-important resources of the contemporary organization. Knowl Based Organ 2018;24:314-18. |
| 28. | Lewis S. Regionalization and devolution: Transforming Health, Reshaping Politics? HEALNet Regional Health Planning; 1997. |
| 29. | Roberts M, Hsiao W, Berman P, Reich M. Getting Health Reform Right: A Guide to Improving Performance and Equity. Oxford University Press;2008. |
| 30. | Kahn JM, Asch RJ, Iwashyna TJ, Rubenfeld GD, Angus DC, Asch DA. Perceived barriers to the regionalization of adult critical care in the United States: A qualitative preliminary study. BMC Health Serv Res 2008;8:239. |
| 31. | Koh HK, Elqura LJ, Judge CM, Stoto MA. Regionalization of local public health systems in the era of preparedness. Annu Rev Public Health 2008;29:205-18. |
[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4]
|
Search Pubmed for