Introduction: Hospital beds, human resources, and medical equipment are the costliest elements in the health system and play an essential role at the time of treatment. In this paper, different phases of the NEDA 2026 project and its methodological approach were presented and its formulation process was analysed using the Kingdon model of policymaking. Methods: Iran Health Roadmap (NEDA 2026) project started in March 2016 and ended in March 2017. The main components of this project were hospital beds, clinical human resources, specialist personnel, capital medical equipment, laboratory facilities, emergency services, and service delivery model. Kingdon model of policymaking was used to evaluate NEDA 2026 development and implementation. In this study, all activities to accomplish each step in the Kingdon model was described. Results: The followings were done to accomplish the goals of each step: collecting experts’ viewpoint (problem identification and definition), systematic review of the literature, analysis of previous experiences, stakeholder analysis, economic analysis, and feasibility study (solution appropriateness analysis), three-round Delphi survey (policy survey and scrutinization), and intersectoral and interasectoral agreement (policy legislation). Conclusion: In the provision of an efficient health service, various components affect each other and the desired outcome, so they need to be considered as parts of an integrated system in developing a roadmap for the health system. Thus, this study demonstrated the cooperation process at different levels of Iran’s health system to formulate a roadmap to provide the necessary resources for the health sector for the next 10 years and to ensure its feasibility using the Kingdon policy framework.

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Background: The health sector evolution plan was implemented in 2014 in government hospitals across the country as a part of the universal health coverage achievement programs. This study assessed the performance of hospitals before and after the implementation of this plan, using the Pabon Lasso model. Methods: The population of this study consisted of the hospitals of the country in the 2013-2015 time frame; overall, 874 hospitals (94.5% of the population) were included in the study. In order to assess performance, we used the Pabon Lasso model and hospital performance indicators (Average Length of Stay, Bed Turnover, and Bed Occupancy Rate). The data were collected from the Hospital Information System and provincial deputies of curative affairs and were then analyzed using the descriptive indicators of mean, frequency, and median in SPSS 22. Also, Paired Student T-test and ANOVA were used to compare the performance of different groups of hospitals before and after the implementation of the health sector evolution plan. Results: The implementation of the health sector evolution plan has led to a significant improvement in the three performance indicators in the hospitals of the country. Before the implementation of the health sector evolution plan, the most inefficient, inefficient, fairly efficient, and most efficient zones included 31%, 18%, 17%, and 33% of the studied hospitals, respectively. However, the implementation of the health sector evolution plan changed the percentages to 29%, 21%, 20%, and 30%, respectively. Teaching hospitals, which are governmental and are mostly located in capital cities of the provinces, were overall more inefficient than non-teaching hospitals. Conclusion: The number of the most efficient and most inefficient hospitals has decreased, and the number of average performance hospitals has increased after the implementation of the health sector evolution plan. Therefore, the health sector evolution plan has not led to an overall increase or decrease in the performance of hospitals but has reduced the difference in the performance of hospitals. Equal support of government hospitals along with financial protection against health expenses, improves the performance indicators of hospitals and reduces performance differences among them.

Background The absence of a referral system and patients’ freedom to choose among service providers in Iran have led to increased patient mobility, which continues to concern health policymakers in the country. This study aimed to determine factors associated with patient mobility rates within the provinces of Iran. Methods This cross-sectional study was conducted in Iran. Data on the place of residence of patients admitted to Iranian public hospitals were collected during August 2017 to determine the status of patient mobility within each province. The sample size were 537,786 patients were hospitalized in public hospitals in Iran during August 2017. The patient mobility ratio was calculated for each of Iran’s provinces by producing a patient mobility matrix. Then, a model of factors affecting patient mobility was identified by regression analysis. All the analyses were performed using STATA14 software. Results In the study period, 585,681 patients were admitted to public hospitals in Iran, of which 69,692 patients were referred to the hospital from another city and 51,789 of them were admitted to public hospitals in the capital of the province. The highest levels of intra-provincial patient mobility were attributed to southern and eastern provinces, and the lowest levels were observed in the north and west of Iran. Implementation of negative binomial regression indicated that, among the examined parameters, the distribution of specialist physicians and the human development index had the highest impact on intra-provincial patient mobility. Conclusion The distribution of specialists throughout different country areas plays a determining role in patient mobility. In many cases, redistributing hospital beds is impossible, but adopting different human resource policies could prevent unnecessary patient mobility through equitable redistribution of specialists among different cities.