Introduction: The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks. Materials and methods: This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles. Results: Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes. Conclusions: This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

Background: The COVID-19 outbreak was defined as a pandemic on 11 March 2020 by the World Health Organization. After that, COVID-19 has enormously influenced health systems around the world, and it has claimed more than 4.2 million deaths until July 2021. The pandemic has led to global health, social and economic costs. This situation has prompted a crucial search for beneficial interventions and treatments, but little is known about their monetary value. This study is aimed at systematically reviewing the articles conducted on the economic evaluation of preventive, control and treatment strategies against COVID-19. Material and method: We searched PubMed, Web of Science, Scopus, and Google Scholar from December 2019 to October 2021 to find applicable literature to the economic evaluation of strategies against COVID-19. Two researchers screened potentially eligible titles and abstracts. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist was used to quality assessment of studies. Results: Thirty-six studies were included in this review, and the average CHEERS score was 72. Cost-effectiveness analysis was the most common type of economic evaluation, used in 21 studies. And the quality-adjusted life year (QALY) was the main outcome applied to measure the effectiveness of interventions, which was used in 19 studies. In addition, articles were reported a wide range of incremental cost-effectiveness ratio (ICER), and the lowest cost per QALY ($321.14) was related to the use of vaccines. Conclusion: Based on the results of this systematic review, it seems that all strategies are likely to be more cost-effective against COVID-19 than no intervention and vaccination was the most cost-effective strategy. This research provides insight for decision makers in choosing optimal interventions against the next waves of the current pandemic and possible future pandemics.

Introduction Mental health disorders, particularly depression and anxiety, are widespread globally and necessitate effective solutions. The patient-centred approach has been identified as a viable and effective method for addressing these challenges. This paper synthesised the principles of patient-centred mental health services and provides a comprehensive review of the existing literature. Materials and methods This is a qualitative content analysis study conducted in a systematic review framework in 2022. PubMed, Scopus, ProQuest and Cochrane databases were systematically searched, and by screening the titles, abstracts, and the texts of studies related to the purpose of the research, the data were extracted. Evaluation of the quality of the studies was done using the CASP checklist for qualitative studies. After selecting the final studies based on the entry and exit criteria, subsequently, a thematic analysis of findings was conducted on the data obtained from the systematic review. Results The database search produced 6649 references. After screening, 11 studies met the inclusion criteria. The quality scores indicated the studies were of high level of quality with acceptable risk of bias. The thematic analysis identified six major principles of patient-centredness in mental health services: education, involvement and cooperation, access, effectiveness and safety, health and well-being, and ethics. Conclusions Patient-centredness is a complex approach in mental health services. The principles and elements of patient-centredness foster positive patient outcomes, enhance healthcare quality and ensure compassionate and effective care. Upholding these principles is crucial for delivering patient-centred, ethical and effective mental health services. Furthermore, the study found that patient education can boost adherence and satisfaction, and decrease unnecessary hospitalisations. Patient involvement in decision-making is influenced by their age and the relationship with their psychologists. And, effective leadership and resource management can enhance clinical processes and patient-centredness in mental health services.

Population-related policies are among macro-strategic policies considered by all governments in the world. To achieve the desired population structure, it is first necessary to identify the general policy approach over time. This article aims to identify the main requirements of population policies during the last 70 years in Iran. This is a qualitative content analysis study conducted via the analysis of all relevant national policy documents from 1951 to 2022. To retrieve the relevant documents, we searched the official website of eight policymaking bodies in Iran. After identifying the documents, their eligibility was evaluated using Scott’s method, and as a result, 40 documents were selected for analysis. Finally, we used a qualitative content analysis to synthesize the data using MAXQDA version 10. The findings showed that the political requirements for population reduction can be classified into four main themes of “Religious, scientific, and legal infrastructure”, “Changes in the rules”, “Institution building, programming and division of tasks”, and “Information and service provision”, with 11 sub-themes. Furthermore, the political requirements for an increasing population can be divided into six main themes of “Education & acculturation”, “Legal dos, and don’ts”, “Financial and non-financial support for families”, “Structural and information infrastructure”, “Health services”, and “Stewardship”, with 30 sub-themes. In this study, by an all-round look, and the analysis of policies of the last 70 years of Iran, it was determined how the population policies originate from the political-cultural background of society, and provide the ground for the changes in the cultural-social, political, and economic structures and as a result demographic change. In other words, the main requirements to formulate the population increase and decrease policies in Iran as a country with golden experiences to implement the population policies were shown; which can be helpful as a guide to formulate the population policies in Iran and provide a model for successful policy making in the countries with a similar background to Iran.