Mohsen Khosravi

Background and Aims: In the ever-evolving domain of healthcare, Decision Support Systems (DSS) stand at the forefront of technological advancement, promising to revolutionize patient care. This systematic review investigated the factors that influence the adoption of DSS, and provides important insights into their use in healthcare services. Methods: Using the PRISMA framework, this systematic review was conducted based on articles published in the time frame of 2000-2024. A systematic literature search was conducted across PubMed, Scopus, ProQuest, and the Cochrane Library. The AACODS checklist was used for quality assessment of the included studies. Subsequently, a thematic analysis was conducted on the acquired data. Results: Eighty-six studies were included in the research. The included studies were deemed to bear high quality and low risk of bias. The research illuminated 11 themes and 47 sub-themes. The themes included system design and integration, user characteristics, personalization, trust, quality, utility, satisfaction and acceptability, education and awareness, involvement, settings, and ethical-legal issues. Conclusion: The study yielded comprehensive data that addressed the research questions by presenting an extensive list of factors influencing the utilization of DSS in healthcare. The perceived importance of these factors varied, as indicated by the differing frequencies with which they were cited across the included studies. This disparity in factor importance was particularly pronounced when comparing developed and developing countries.

Large language model (LLM)-based chatbots have been utilized across various healthcare domains and have garnered substantial attention. This study aimed to evaluate and compare the performance of several LLM-based chatbots with that of medical students in responding to neurology questions. This cross-sectional study, conducted in December 2025 in Iran. ChatGPT-5, Gemini 3, Copilot 2025, Perplexity, and 20 medical students responded to a neurology questionnaire. A confusion matrix was utilized to analyze the data. In this regard, four metrics—sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)—as well as overall accuracy were calculated. Moreover, correlations examined chatbot performance against question characteristics (word count, context, format, type, modality, language). The study revealed that overall performance metrics for the evaluated chatbots significantly outperformed those of medical students (p < 0.001). Among the evaluated chatbots, Copilot exhibited superior performance (0.88), followed by ChatGPT-5 (0.86), in terms of accuracy. Meanwhile, quantitative question types were associated with a significant reduction in chatbot performance (r = 0.470, p = 0.001). The study findings presented valuable insights results particularly pertinent to neurology, where chatbots can serve as supplementary tools for practitioners, enhancing diagnostic accuracy and clinical decision-making while adhering to established ethical standards. However, further research is required to provide more precise insights, particularly with a larger sample size of human participants.

This cross-sectional study was conducted in 2025-2026. Initially, a systematic review of reviews was conducted by searching PubMed, Scopus, and the Cochrane Database of Systematic Reviews for relevant review articles published between 2000 and 2025.

Introduction The literature highlights the considerable potential of Artificial Intelligence (AI), particularly large language models (LLMs), in advancing health promotion among individuals. This study aimed to evaluate the performance of several LLMs in responding to questions from the Principles of Health course. This cross-sectional study was conducted in 2025. The LLMs evaluated included ChatGPT-4o, Gemini 2.5, Copilot 2025, and Perplexity 2.250619.0. These LLMs were utilized to respond to the study questionnaire pertaining to the Principles of Health course. To analyze and compare the performance of the LLMs in answering the research questions, a confusion matrix was constructed. Accordingly, four key metrics were calculated: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), in addition to overall accuracy. The LLMs included in the study demonstrated perfect sensitivity, each achieving a value of 1. Regarding specificity, ChatGPT and Perplexity attained the highest scores of 0.8, while Gemini and Copilot exhibited comparatively lower specificity values of 0.66 and 0.6, respectively. Furthermore, ChatGPT and Perplexity recorded the highest accuracy rates of 0.93, surpassing Gemini and Copilot, both of which achieved an accuracy of 0.86. The findings provided a detailed assessment of the performance of the LLMs. Results indicated that the performance of LLMs generally declined as the complexity, length, and verbosity of questionnaire items increased. Additionally, certain LLMs, such as Copilot, demonstrated particular difficulty when responding to quantitative questions involving numerical data. Further research is recommended to investigate these observations more comprehensively.

Large language models (LLMs) have recently gained prominence in healthcare content provision due to their numerous advantages. Despite these benefits, LLMs exhibit notable limitations in this domain. This study aimed to systematically identify the limitations of LLMs in provision of healthcare content. This study was a systematic review conducted in September 2025, including articles published in English between 2018 and 2025. Searches were performed in PubMed, Scopus, and the Cochrane Database of Systematic Reviews. Two independent evaluators screened the references and assessed quality of the selected studies using the Authority, Accuracy, Coverage, Objectivity, Date, and Significance (AACODS) checklist. Data were analyzed using Boyatzis's qualitative thematic approach with an inductive methodology, applying the input-process-output (IPO) model as the analytical framework. A total of 81 studies were included in the final analysis. The included studies were predominantly of high quality and demonstrated minimal risk of bias. The thematic analysis identified key themes: data limitations, dependence on input and prompt quality, accessibility issues, model design and architecture constraints, interaction challenges, response quality and comprehensiveness, and ethical, safety, and regulatory concerns. The study identified multiple limitations of LLMs in healthcare, with output issues being most common. In this regard, the most frequently cited limitation was the accuracy gap. However, these output issues were mainly resulted from flaws in input data, emphasizing the crucial role of input quality. The study also proposed strategies to address these challenges.

Objectives: Artificial Intelligence (AI) presents significant advantages in healthcare. The Iran-Afghanistan border region, marked by low socioeconomic status, experiences poor health outcomes, especially among women and children. The presence of armed conflict in the region has further exacerbated the situation. This study examined barriers to patient adoption of AI chatbots in this region. Methods: This was a qualitative exploratory study conducted in June and July 2025 in Birjand, eastern Iran, near the Iran-Afghanistan border. Forty-two unstructured interviews were held with patients at a group of clinics and primary healthcare centers. The interviews were conducted during and immediately following the onset of the armed conflict between Iran and Israel. Data were analyzed using the Boyatzis framework. Results: The findings revealed that barriers to AI chatbot usage among patients could be categorized into four main areas: lack of awareness, knowledge, and training; user barriers; technological barriers; and financial barriers. User barriers encompassed the greatest number of sub-themes, whereas financial barriers were represented by only a single sub-theme, indicating less representation by the study participants. Conclusion: The study provided novel data on wartime conditions, offering valuable insights for stakeholders in conflict-affected or at-risk regions. The findings can support effective AI chatbot adoption by local populations during war or crises and inform efforts to improve usage in low-resource and conflict settings.

Introduction: Air pollution remains the leading environmental risk factor for human health. Although artificial intelligence (AI) has demonstrated strong technical potential for air quality monitoring and prediction, its integration into environmental policy and governance remains unclear. This study examines how AI is currently addressed in policy-oriented literature on air pollution management. Methods: A scoping review was conducted following Arksey and O’Malley’s framework and its extension by Levac et al. Systematic searches of major scientific databases and policy sources identified English-language documents published between 2015 and 2025 that addressed the use of AI in air pollution control from a policy, governance, or strategic perspective. Thematic analysis was used to synthesize the findings. Results: Eight policy-relevant documents met the inclusion criteria of this review. The analysis identified four core themes: applications of AI, perceived benefits, governance and ethical concerns, and policy strategies. AI applications have primarily been framed around real-time monitoring, predictive modeling, and data-driven policymaking. The reported benefits included improved accuracy, responsiveness, and decision support, whereas the key concerns were related to data quality, privacy, energy use, transparency, and institutional capacity. Policy strategies emphasized regulatory frameworks, digital infrastructure, capacity building, cross-sector collaboration, and international coordination. Conclusions: The limited number of policy-oriented studies highlights a significant governance gap between technical AI development and environmental policy-making. Integrating AI into air pollution management requires evidence-based, transparent, and accountable governance. Future research should focus on policy design, implementation, and evaluation to support the responsible and sustainable adoption of AI in environmental governance.

Mental health is a global issue, and mobile applications, such as chatbots, offer a partial solution by providing improved services through various communication forms. This study aimed to identify chatbots and their technical features in mental health services. This study conducted a systematic review of mental health chatbots and their technical features from 2000 to 2025. A search was performed across databases such as PubMed, Scopus, ProQuest and the Cochrane database. The CASP (Critical Appraisal Skills Programme) appraisal checklist was used to assess the quality of the studies. In the next step, the Braun and Clarke's approach was utilized for conducting thematic analysis on the data. The search yielded 2,921 records, of which 10 were duplicates and removed. After screening for relevance and eligibility, 33 papers met all the requirements. The mean quality score of the included studies was 13.36 (standard deviation = 1.36). The studies had a moderate risk of bias, as they mostly had a clear question, searched for the right type of papers, included all relevant papers and reported the results precisely. The research conducted an analysis of 138 mental health chatbots, categorizing them based on five distinct attributes: the disorder they target, their input and output modalities, the platform they operate on and their method of generating responses. The research emphasized the need for designing chatbots that suit patients' preferences and needs, and also indicated that the digital divide within societies should be taken into account when designing and producing chatbots for mental health services. Although mental health chatbots can assist underserved communities, ethical concerns must be addressed before their deployment.

Evidence indicates that conducting research, especially within the health sector, is associated with numerous challenges, notably those related to individual participation. This study aimed to identify the barriers affecting stakeholder participation in healthcare research. This cross-sectional study was conducted in 2025 employing a mixed-methods design comprising two distinct phases: a systematic review of reviews and an exploratory qualitative interview approach. During the initial phase, multiple databases were systematically searched. In the subsequent phase, 17 unstructured interviews were conducted with university faculty members. Data analysis was performed in accordance with the Boyatzis framework for thematic analysis. The systematic review resulted in the inclusion of 20 studies. The findings indicated that multiple thematic barriers hinder individual participation in healthcare research projects, including Communication Barriers, Attitudinal Barriers and Motivational Deterrents, Structural and Institutional Challenges, Awareness and Knowledge-Related Challenges, and Research and Methodology-Related Challenges. The interview data corroborated approximately 77% of the findings reported in previously published studies within the literature. Overall, Structural and Institutional Challenges emerged as the most frequently cited barriers impeding beneficiary participation in healthcare projects. The study’s findings identified multiple thematic barriers restricting individual participation in healthcare research projects. These findings can be leveraged by relevant stakeholders within the context to promote participation in such projects through an evidence-based approach.

OBJECTIVE: This study aimed to examine the barriers and facilitators influencing voluntary participation in health care crises. METHODS: This qualitative and exploratory study was conducted in 2025 at governmental health care centers in eastern Iran. Data were collected through a series of unstructured interviews. Stratified, purposeful, and snowball sampling were employed. Data analysis was performed using the qualitative thematic analysis approach as outlined by Braun and Clarke. RESULTS: Data saturation was achieved after conducting interviews with 14 participants. Barriers to voluntary participation among health care personnel were organized into four primary themes: Physical, Psychological, and Emotional Challenges; Organizational Issues; Financial and Material Constraints; and Family, Social, and Cultural Concerns. Conversely, facilitators promoting voluntary participation were classified into 5 main themes: Intrinsic Motivation, Values, and Personal Characteristics; Cultural, Social, and Family Support; Organizational Support and Leadership; Experience and Skills; and Material and Financial Incentives. CONCLUSIONS: Barriers and facilitators varied across clinical and paramedical professions, with differences also noted between physicians and nurses. The negative impact of closed, autocratic, and non-transparent organizations highlighted the importance of an open, accountable, and equitable environment in promoting voluntary engagement.

Large language models (LLMs) have recently gained prominence in healthcare content provision due to their numerous advantages. Despite these benefits, LLMs exhibit notable limitations in this domain. This study aimed to systematically identify the limitations of LLMs in provision of healthcare content. This study was a systematic review conducted in September 2025, including articles published in English between 2018 and 2025. Searches were performed in PubMed, Scopus, and the Cochrane Database of Systematic Reviews. Two independent evaluators screened the references and assessed quality of the selected studies using the Authority, Accuracy, Coverage, Objectivity, Date, and Significance (AACODS) checklist. Data were analyzed using Boyatzis's qualitative thematic approach with an inductive methodology, applying the input-process-output (IPO) model as the analytical framework. A total of 81 studies were included in the final analysis. The included studies were predominantly of high quality and demonstrated minimal risk of bias. The thematic analysis identified key themes: data limitations, dependence on input and prompt quality, accessibility issues, model design and architecture constraints, interaction challenges, response quality and comprehensiveness, and ethical, safety, and regulatory concerns. The study identified multiple limitations of LLMs in healthcare, with output issues being most common. In this regard, the most frequently cited limitation was the accuracy gap. However, these output issues were mainly resulted from flaws in input data, emphasizing the crucial role of input quality. The study also proposed strategies to address these challenges.

Background and Aims: Public-Private Partnerships (PPPs) have been proposed with an aim to improve performance of healthcare systems. This paper aimed to investigate the outcomes of implementing PPPs in healthcare systems within the Middle East. Methods: In 2024, a systematic review was conducted on the studies within the literature. The quality of the included studies was assessed using the AACODS (Accuracy, Coverage, Objectivity, Date, Significance) checklist. Subsequently, a thematic analysis was conducted on the gathered data adhering to the guidelines published by Thomas and Harden. The search was conducted across multiple scholarly databases, such as PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews. Results: 12 studies were found to meet the inclusion criteria. The quality assessment of these studies indicated a high standard and an acceptable level of bias. A thematic analysis of the data from these studies revealed four main themes: 'positive client-based outcomes', 'positive service delivery outcomes', 'positive economic outcomes', and 'challenging outcomes'. Conclusion: PPPs have been presented to improve outcomes and efficiency but face organizational, financial, legal, and ethical challenges. Their success requires specific conditions, ongoing oversight, and adaptation to unlock their transformative potential in regional healthcare.

Introduction: Air pollution remains the leading environmental risk factor for human health. Although artificial intelligence (AI) has demonstrated strong technical potential for air quality monitoring and prediction, its integration into environmental policy and governance remains unclear. This study examines how AI is currently addressed in policy-oriented literature on air pollution management. Methods: A scoping review was conducted following Arksey and O’Malley’s framework and its extension by Levac et al. Systematic searches of major scientific databases and policy sources identified English-language documents published between 2015 and 2025 that addressed the use of AI in air pollution control from a policy, governance, or strategic perspective. Thematic analysis was used to synthesize the findings. Results: Eight policy-relevant documents met the inclusion criteria of this review. The analysis identified four core themes: applications of AI, perceived benefits, governance and ethical concerns, and policy strategies. AI applications have primarily been framed around real-time monitoring, predictive modeling, and data-driven policymaking. The reported benefits included improved accuracy, responsiveness, and decision support, whereas the key concerns were related to data quality, privacy, energy use, transparency, and institutional capacity. Policy strategies emphasized regulatory frameworks, digital infrastructure, capacity building, cross-sector collaboration, and international coordination. Conclusions: The limited number of policy-oriented studies highlights a significant governance gap between technical AI development and environmental policy-making. Integrating AI into air pollution management requires evidence-based, transparent, and accountable governance. Future research should focus on policy design, implementation, and evaluation to support the responsible and sustainable adoption of AI in environmental governance.

Air pollution remains a serious environmental and public-health issue in Iran. Despite the Clean Air Act (CAA), enforcement is limited and compliance is weak. This 2025 scoping review outlines major barriers to CAA enforcement and proposes evidence-based policy actions. Multiple databases including Scopus, Web of Science, PubMed, and Google Scholar were searched and Policy documents, institutional reports, and legal texts were identified through Google and official organizational websites. Finally, a thematic analysis was conducted on the data using a four-dimensional conceptual framework. The data were categorized into four main categories of challenges. In the cultural, information and evidence domain, key issues included low policy priority for air pollution, weak public awareness, insufficient transparency, and lack of evidence for policymaking. Governance-related challenges involved institutional fragmentation, limited authority of the Department of Environment (DOE), multiple actors without coordination mechanisms, weak enforcement, and absence of performance metrics. Financial constraints included reliance on public budgets, lack of sustainable revenue sources, project interruptions due to budgetary constraints, and absence of cost-benefit models. Technical and infrastructural issues included lack of a data-governance system, absence of mandatory data and equipment standards, no technological roadmap, and weak industrial technology and monitoring infrastructure. Governance and institutional challenges represented the predominant share of factors hindering the CAA implementation in Iran. Addressing these challenges requires stronger political commitment, governance reform, sustainable financing, integrated data and monitoring systems, enhanced technical infrastructure, and evidence-based policymaking. A comprehensive, intersectoral, and long-term redesign of air-pollution control mechanisms in Iran is essential.

Large language model (LLM)-based chatbots have been utilized across various healthcare domains and have garnered substantial attention. This study aimed to evaluate and compare the performance of several LLM-based chatbots with that of medical students in responding to neurology questions. This cross-sectional study, conducted in December 2025 in Iran. ChatGPT-5, Gemini 3, Copilot 2025, Perplexity, and 20 medical students responded to a neurology questionnaire. A confusion matrix was utilized to analyze the data. In this regard, four metrics—sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)—as well as overall accuracy were calculated. Moreover, correlations examined chatbot performance against question characteristics (word count, context, format, type, modality, language). The study revealed that overall performance metrics for the evaluated chatbots significantly outperformed those of medical students (p < 0.001). Among the evaluated chatbots, Copilot exhibited superior performance (0.88), followed by ChatGPT-5 (0.86), in terms of accuracy. Meanwhile, quantitative question types were associated with a significant reduction in chatbot performance (r = 0.470, p = 0.001). The study findings presented valuable insights results particularly pertinent to neurology, where chatbots can serve as supplementary tools for practitioners, enhancing diagnostic accuracy and clinical decision-making while adhering to established ethical standards. However, further research is required to provide more precise insights, particularly with a larger sample size of human participants.

Evidence indicates that conducting research, especially within the health sector, is associated with numerous challenges, notably those related to individual participation. This study aimed to identify the barriers affecting stakeholder participation in healthcare research. This cross-sectional study was conducted in 2025 employing a mixed-methods design comprising two distinct phases: a systematic review of reviews and an exploratory qualitative interview approach. During the initial phase, multiple databases were systematically searched. In the subsequent phase, 17 unstructured interviews were conducted with university faculty members. Data analysis was performed in accordance with the Boyatzis framework for thematic analysis. The systematic review resulted in the inclusion of 20 studies. The findings indicated that multiple thematic barriers hinder individual participation in healthcare research projects, including Communication Barriers, Attitudinal Barriers and Motivational Deterrents, Structural and Institutional Challenges, Awareness and Knowledge-Related Challenges, and Research and Methodology-Related Challenges. The interview data corroborated approximately 77% of the findings reported in previously published studies within the literature. Overall, Structural and Institutional Challenges emerged as the most frequently cited barriers impeding beneficiary participation in healthcare projects. The study’s findings identified multiple thematic barriers restricting individual participation in healthcare research projects. These findings can be leveraged by relevant stakeholders within the context to promote participation in such projects through an evidence-based approach.

This cross-sectional study was conducted in 2025-2026. Initially, a systematic review of reviews was conducted by searching PubMed, Scopus, and the Cochrane Database of Systematic Reviews for relevant review articles published between 2000 and 2025.

Mental health is a global issue, and mobile applications, such as chatbots, offer a partial solution by providing improved services through various communication forms. This study aimed to identify chatbots and their technical features in mental health services. This study conducted a systematic review of mental health chatbots and their technical features from 2000 to 2025. A search was performed across databases such as PubMed, Scopus, ProQuest and the Cochrane database. The CASP (Critical Appraisal Skills Programme) appraisal checklist was used to assess the quality of the studies. In the next step, the Braun and Clarke's approach was utilized for conducting thematic analysis on the data. The search yielded 2,921 records, of which 10 were duplicates and removed. After screening for relevance and eligibility, 33 papers met all the requirements. The mean quality score of the included studies was 13.36 (standard deviation = 1.36). The studies had a moderate risk of bias, as they mostly had a clear question, searched for the right type of papers, included all relevant papers and reported the results precisely. The research conducted an analysis of 138 mental health chatbots, categorizing them based on five distinct attributes: the disorder they target, their input and output modalities, the platform they operate on and their method of generating responses. The research emphasized the need for designing chatbots that suit patients' preferences and needs, and also indicated that the digital divide within societies should be taken into account when designing and producing chatbots for mental health services. Although mental health chatbots can assist underserved communities, ethical concerns must be addressed before their deployment.

OBJECTIVE: This study aimed to examine the barriers and facilitators influencing voluntary participation in health care crises. METHODS: This qualitative and exploratory study was conducted in 2025 at governmental health care centers in eastern Iran. Data were collected through a series of unstructured interviews. Stratified, purposeful, and snowball sampling were employed. Data analysis was performed using the qualitative thematic analysis approach as outlined by Braun and Clarke. RESULTS: Data saturation was achieved after conducting interviews with 14 participants. Barriers to voluntary participation among health care personnel were organized into four primary themes: Physical, Psychological, and Emotional Challenges; Organizational Issues; Financial and Material Constraints; and Family, Social, and Cultural Concerns. Conversely, facilitators promoting voluntary participation were classified into 5 main themes: Intrinsic Motivation, Values, and Personal Characteristics; Cultural, Social, and Family Support; Organizational Support and Leadership; Experience and Skills; and Material and Financial Incentives. CONCLUSIONS: Barriers and facilitators varied across clinical and paramedical professions, with differences also noted between physicians and nurses. The negative impact of closed, autocratic, and non-transparent organizations highlighted the importance of an open, accountable, and equitable environment in promoting voluntary engagement.

Homeless women constitute one of the most vulnerable populations. The aim of the study was to identify the Social Determinants of Health (SDH), also known as social drivers of health among homeless women residing in Iran’s border regions adjacent to Afghanistan, characterized by socioeconomic and healthcare disparities. This qualitative study was carried out between September 2025 and January 2026. The research was conducted in government-operated shelters situated in a low-resource region of eastern Iran. Using Microsoft Office Word 2016, verbatim transcripts of audio-recorded data from 13 semi-structured, in-depth interviews with homeless women, recruited through convenience and purposive sampling, were subjected to a manual thematic analysis following Boyatzis’s methodological approach. The analysis was guided by the Healthy People 2030 framework as the study’s conceptual framework. The thematic analysis revealed 24 SDH under five overarching themes: Economic Stability, Education Access and Quality, Health Care Access and Quality, Neighborhood and Built Environment, and Social and Community Context. The domains of economic stability, along with, social and community context, encompassed the highest number of determinants, presenting their influential role in the ultimate health outcome of homeless women. This study presented a comprehensive list of SDH for homeless women, highlighting the pivotal roles of governments in addressing the issue. The findings hold particular value for low-resource settings.

In the field of health education, chatbots have demonstrated considerable potential. This study aimed to evaluate the quality of definitions for health systems terminology provided by chatbots. This was a quantitative cross-sectional study conducted in Iran and Saudi Arabia during 2025–2026. ChatGPT-5, Copilot, Gemini 3, and Perplexity were evaluated. A total of 155 terms derived from the World Health Organization’s Health Systems Strengthening Glossary were given to the chatbots to define. Data analysis employed multiple measures, including the Global Quality Score and the mDISCERN Score, to evaluate the quality of the content generated by the artificial intelligence chatbots. Statistical significance was assessed using repeated-measures ANOVA, the Friedman test, and the Wilcoxon test. Finally, a semantic similarity analysis was conducted on the study data using Sentence-BERT (SBERT). The study found a significant difference in performance among chatbots in defining health-system terms ( p < 0.001). Perplexity outperformed the others on both the global quality score and the mDISCERN score ( p < 0.05), with a more pronounced advantage on the latter ( p < 0.001). Copilot presented moderately superior performance than Gemini 3 at some instances, while ChatGPT-5 recorded the lowest scores on both. Perplexity’s seemingly superior performance was attributed to greater reliability, including the provision of references, whereas other chatbots’ lack of citations was associated with their lower scores. Notably, Perplexity exhibited the lowest level of semantic similarity, whereas the remaining chatbots demonstrated higher and relatively comparable levels of semantic alignment. The study provided valuable insights regarding the performance of chatbots in defining health-system terms. These insights can be utilized by stakeholders to inform practice.

Background and Aims: The use of Large Language Model (LLM)-based chatbots across various fields has yielded positive outcomes. Understanding the health service delivery system offers numerous benefits. This study aimed to analyze the performance of LLMs in answering healthcare delivery questions. Methods: A validated questionnaire relevant to the research context was administered to a sample of LLM-based chatbots. The chatbots evaluated in this study included GPT-4.1-mini, Gemini 2.5, Copilot 2025, and Perplexity. A written prompt was provided to facilitate response generation by the chatbots. To analyze and compare the performance of the AI models in addressing the research questions, confusion matrices were constructed, and key metrics-sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy-were calculated. Results: The initial assessment of the chatbots showed perfect sensitivity (1.00), accurately identifying all true positives without false negatives. Specificity varied, with ChatGPT and Perplexity at 0.50, Gemini at 0.43, and Copilot at 0.33. Positive predictive values (PPV) ranged from 0.67 (Gemini) to 0.75 (ChatGPT and Perplexity), while negative predictive values (NPV) were uniformly perfect (1.00). Overall accuracy was highest for ChatGPT and Perplexity (0.80), with Gemini and Copilot at 0.73. In the second round, sensitivity remained perfect for all chatbots. Gemini achieved the highest specificity (0.80), followed by ChatGPT (0.67), Perplexity (0.60), and Copilot (0.50). PPVs improved, ranging from 0.75 (Copilot) to 0.91 (Gemini). NPVs remained perfect (1.00) across all models. Overall accuracy led by Gemini (0.93), with ChatGPT and Perplexity both at 0.87, and Copilot at 0.80. Conclusion: ChatGPT and Perplexity showed the highest initial performance, while the second round revealed improvements in most chatbots, especially in specificity and accuracy, with Gemini performing best. Further research is needed for deeper insights.

Background and Aims: In the ever-evolving domain of healthcare, Decision Support Systems (DSS) stand at the forefront of technological advancement, promising to revolutionize patient care. This systematic review investigated the factors that influence the adoption of DSS, and provides important insights into their use in healthcare services. Methods: Using the PRISMA framework, this systematic review was conducted based on articles published in the time frame of 2000-2024. A systematic literature search was conducted across PubMed, Scopus, ProQuest, and the Cochrane Library. The AACODS checklist was used for quality assessment of the included studies. Subsequently, a thematic analysis was conducted on the acquired data. Results: Eighty-six studies were included in the research. The included studies were deemed to bear high quality and low risk of bias. The research illuminated 11 themes and 47 sub-themes. The themes included system design and integration, user characteristics, personalization, trust, quality, utility, satisfaction and acceptability, education and awareness, involvement, settings, and ethical-legal issues. Conclusion: The study yielded comprehensive data that addressed the research questions by presenting an extensive list of factors influencing the utilization of DSS in healthcare. The perceived importance of these factors varied, as indicated by the differing frequencies with which they were cited across the included studies. This disparity in factor importance was particularly pronounced when comparing developed and developing countries.

Introduction The literature highlights the considerable potential of Artificial Intelligence (AI), particularly large language models (LLMs), in advancing health promotion among individuals. This study aimed to evaluate the performance of several LLMs in responding to questions from the Principles of Health course. This cross-sectional study was conducted in 2025. The LLMs evaluated included ChatGPT-4o, Gemini 2.5, Copilot 2025, and Perplexity 2.250619.0. These LLMs were utilized to respond to the study questionnaire pertaining to the Principles of Health course. To analyze and compare the performance of the LLMs in answering the research questions, a confusion matrix was constructed. Accordingly, four key metrics were calculated: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), in addition to overall accuracy. The LLMs included in the study demonstrated perfect sensitivity, each achieving a value of 1. Regarding specificity, ChatGPT and Perplexity attained the highest scores of 0.8, while Gemini and Copilot exhibited comparatively lower specificity values of 0.66 and 0.6, respectively. Furthermore, ChatGPT and Perplexity recorded the highest accuracy rates of 0.93, surpassing Gemini and Copilot, both of which achieved an accuracy of 0.86. The findings provided a detailed assessment of the performance of the LLMs. Results indicated that the performance of LLMs generally declined as the complexity, length, and verbosity of questionnaire items increased. Additionally, certain LLMs, such as Copilot, demonstrated particular difficulty when responding to quantitative questions involving numerical data. Further research is recommended to investigate these observations more comprehensively.

Objectives: Artificial Intelligence (AI) presents significant advantages in healthcare. The Iran-Afghanistan border region, marked by low socioeconomic status, experiences poor health outcomes, especially among women and children. The presence of armed conflict in the region has further exacerbated the situation. This study examined barriers to patient adoption of AI chatbots in this region. Methods: This was a qualitative exploratory study conducted in June and July 2025 in Birjand, eastern Iran, near the Iran-Afghanistan border. Forty-two unstructured interviews were held with patients at a group of clinics and primary healthcare centers. The interviews were conducted during and immediately following the onset of the armed conflict between Iran and Israel. Data were analyzed using the Boyatzis framework. Results: The findings revealed that barriers to AI chatbot usage among patients could be categorized into four main areas: lack of awareness, knowledge, and training; user barriers; technological barriers; and financial barriers. User barriers encompassed the greatest number of sub-themes, whereas financial barriers were represented by only a single sub-theme, indicating less representation by the study participants. Conclusion: The study provided novel data on wartime conditions, offering valuable insights for stakeholders in conflict-affected or at-risk regions. The findings can support effective AI chatbot adoption by local populations during war or crises and inform efforts to improve usage in low-resource and conflict settings.

Homeless women constitute one of the most vulnerable populations. The aim of the study was to identify the Social Determinants of Health (SDH), also known as social drivers of health among homeless women residing in Iran’s border regions adjacent to Afghanistan, characterized by socioeconomic and healthcare disparities. This qualitative study was carried out between September 2025 and January 2026. The research was conducted in government-operated shelters situated in a low-resource region of eastern Iran. Using Microsoft Office Word 2016, verbatim transcripts of audio-recorded data from 13 semi-structured, in-depth interviews with homeless women, recruited through convenience and purposive sampling, were subjected to a manual thematic analysis following Boyatzis’s methodological approach. The analysis was guided by the Healthy People 2030 framework as the study’s conceptual framework. The thematic analysis revealed 24 SDH under five overarching themes: Economic Stability, Education Access and Quality, Health Care Access and Quality, Neighborhood and Built Environment, and Social and Community Context. The domains of economic stability, along with, social and community context, encompassed the highest number of determinants, presenting their influential role in the ultimate health outcome of homeless women. This study presented a comprehensive list of SDH for homeless women, highlighting the pivotal roles of governments in addressing the issue. The findings hold particular value for low-resource settings.

Background and Aims: The use of Large Language Model (LLM)-based chatbots across various fields has yielded positive outcomes. Understanding the health service delivery system offers numerous benefits. This study aimed to analyze the performance of LLMs in answering healthcare delivery questions. Methods: A validated questionnaire relevant to the research context was administered to a sample of LLM-based chatbots. The chatbots evaluated in this study included GPT-4.1-mini, Gemini 2.5, Copilot 2025, and Perplexity. A written prompt was provided to facilitate response generation by the chatbots. To analyze and compare the performance of the AI models in addressing the research questions, confusion matrices were constructed, and key metrics-sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy-were calculated. Results: The initial assessment of the chatbots showed perfect sensitivity (1.00), accurately identifying all true positives without false negatives. Specificity varied, with ChatGPT and Perplexity at 0.50, Gemini at 0.43, and Copilot at 0.33. Positive predictive values (PPV) ranged from 0.67 (Gemini) to 0.75 (ChatGPT and Perplexity), while negative predictive values (NPV) were uniformly perfect (1.00). Overall accuracy was highest for ChatGPT and Perplexity (0.80), with Gemini and Copilot at 0.73. In the second round, sensitivity remained perfect for all chatbots. Gemini achieved the highest specificity (0.80), followed by ChatGPT (0.67), Perplexity (0.60), and Copilot (0.50). PPVs improved, ranging from 0.75 (Copilot) to 0.91 (Gemini). NPVs remained perfect (1.00) across all models. Overall accuracy led by Gemini (0.93), with ChatGPT and Perplexity both at 0.87, and Copilot at 0.80. Conclusion: ChatGPT and Perplexity showed the highest initial performance, while the second round revealed improvements in most chatbots, especially in specificity and accuracy, with Gemini performing best. Further research is needed for deeper insights.

In the field of health education, chatbots have demonstrated considerable potential. This study aimed to evaluate the quality of definitions for health systems terminology provided by chatbots. This was a quantitative cross-sectional study conducted in Iran and Saudi Arabia during 2025–2026. ChatGPT-5, Copilot, Gemini 3, and Perplexity were evaluated. A total of 155 terms derived from the World Health Organization’s Health Systems Strengthening Glossary were given to the chatbots to define. Data analysis employed multiple measures, including the Global Quality Score and the mDISCERN Score, to evaluate the quality of the content generated by the artificial intelligence chatbots. Statistical significance was assessed using repeated-measures ANOVA, the Friedman test, and the Wilcoxon test. Finally, a semantic similarity analysis was conducted on the study data using Sentence-BERT (SBERT). The study found a significant difference in performance among chatbots in defining health-system terms ( p < 0.001). Perplexity outperformed the others on both the global quality score and the mDISCERN score ( p < 0.05), with a more pronounced advantage on the latter ( p < 0.001). Copilot presented moderately superior performance than Gemini 3 at some instances, while ChatGPT-5 recorded the lowest scores on both. Perplexity’s seemingly superior performance was attributed to greater reliability, including the provision of references, whereas other chatbots’ lack of citations was associated with their lower scores. Notably, Perplexity exhibited the lowest level of semantic similarity, whereas the remaining chatbots demonstrated higher and relatively comparable levels of semantic alignment. The study provided valuable insights regarding the performance of chatbots in defining health-system terms. These insights can be utilized by stakeholders to inform practice.

Background and Aims: Public-Private Partnerships (PPPs) have been proposed with an aim to improve performance of healthcare systems. This paper aimed to investigate the outcomes of implementing PPPs in healthcare systems within the Middle East. Methods: In 2024, a systematic review was conducted on the studies within the literature. The quality of the included studies was assessed using the AACODS (Accuracy, Coverage, Objectivity, Date, Significance) checklist. Subsequently, a thematic analysis was conducted on the gathered data adhering to the guidelines published by Thomas and Harden. The search was conducted across multiple scholarly databases, such as PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews. Results: 12 studies were found to meet the inclusion criteria. The quality assessment of these studies indicated a high standard and an acceptable level of bias. A thematic analysis of the data from these studies revealed four main themes: 'positive client-based outcomes', 'positive service delivery outcomes', 'positive economic outcomes', and 'challenging outcomes'. Conclusion: PPPs have been presented to improve outcomes and efficiency but face organizational, financial, legal, and ethical challenges. Their success requires specific conditions, ongoing oversight, and adaptation to unlock their transformative potential in regional healthcare.

Air pollution remains a serious environmental and public-health issue in Iran. Despite the Clean Air Act (CAA), enforcement is limited and compliance is weak. This 2025 scoping review outlines major barriers to CAA enforcement and proposes evidence-based policy actions. Multiple databases including Scopus, Web of Science, PubMed, and Google Scholar were searched and Policy documents, institutional reports, and legal texts were identified through Google and official organizational websites. Finally, a thematic analysis was conducted on the data using a four-dimensional conceptual framework. The data were categorized into four main categories of challenges. In the cultural, information and evidence domain, key issues included low policy priority for air pollution, weak public awareness, insufficient transparency, and lack of evidence for policymaking. Governance-related challenges involved institutional fragmentation, limited authority of the Department of Environment (DOE), multiple actors without coordination mechanisms, weak enforcement, and absence of performance metrics. Financial constraints included reliance on public budgets, lack of sustainable revenue sources, project interruptions due to budgetary constraints, and absence of cost-benefit models. Technical and infrastructural issues included lack of a data-governance system, absence of mandatory data and equipment standards, no technological roadmap, and weak industrial technology and monitoring infrastructure. Governance and institutional challenges represented the predominant share of factors hindering the CAA implementation in Iran. Addressing these challenges requires stronger political commitment, governance reform, sustainable financing, integrated data and monitoring systems, enhanced technical infrastructure, and evidence-based policymaking. A comprehensive, intersectoral, and long-term redesign of air-pollution control mechanisms in Iran is essential.

RATIONALE: Research on patient-centeredness in managing chronic diseases like cancer, diabetes, stroke, and heart disorders is gaining prominence. This approach in chronic disease management involves various dimensions and elements, the importance of which has been presented to differ. AIMS AND OBJECTIVES: The present research, carried out in the year 2023, aimed to identify and prioritize existing dimensions and elements of patient-centered care for chronic diseases. METHODS: Firstly, multiple databases including the Cochrane Database of Systematic Reviews, Scopus, PubMed and ProQuest were searched. Secondly, 35 chronic patients were interviewed; Finally, a sample of 30 experts familiar with the context was asked to rank the dimensions and elements of patient-centered care using Best-Worst method. RESULTS: The study identified 6 dimensions and 13 elements of patient-centered care in chronic diseases. The findings indicated that accessibility, quality, education, timeliness and affordability were the five elements that ranked highest in importance for designing and implementing patient-centered care interventions for chronic diseases. CONCLUSION: The results presented that Iranian patients with chronic diseases exhibit a positive attitude towards paternalistic behavior and often prefer others to make the best therapeutic decisions on their behalf. Our research revealed that the concept of patient-centered care differs across regions and cultures, highlighting the need for policymakers and researchers to adapt strategies to the specific characteristics of local populations around the globe.

Introduction: In an era where geographical distance has become a barrier to equitable access to health services, an intelligent needs assessment of medical specialties is the key to enabling effective telemedicine. A systematic needs assessment of medical specialties is the cornerstone in designing a telemedicine roadmap. This study aimed to identify and prioritize key specialties based on needs-based criteria for the development of telemedicine in the community covered by Shiraz University of Medical Sciences. Methods: This study's methodology was modeled on the "need-based planning framework for telemedicine services" developed by the University of Queensland, Australia. A mixed approach was used in the study design. In the first three steps, quantitative and qualitative data were collected to identify the community's specialized medical needs. In the fourth step, the identified needs were prioritized. Results: A total of 11 specialties, including neurosurgery, cardiology, general surgery, general internal medicine, orthopedics, pediatrics, neurology, psychiatry, obstetrics and gynecology, imaging, and urology, were identified as key priority specialties needed for the development of telemedicine in the community covered by Shiraz University of Medical Sciences. These specialties were prioritized into eight ranks based on need-based criteria. Conclusion: By identifying and prioritizing 11 key medical specialties, this study has laid a valuable foundation for the development of telemedicine services. Its findings provide policymakers with an effective guide to strategically allocate resources toward prioritized specialties, enhance technological infrastructure, and implement relevant educational programs, thereby ensuring equitable access to services and taking a significant step toward advancing justice in healthcare.

The decision-making process in healthcare services encounters numerous challenges. Artificial intelligence (AI) has significantly contributed to enhancing healthcare decision-making. There is a lack of validated instruments available in the literature to measure AI utilization across various healthcare domains. This study aimed to validate an instrument designed to assess the level of AI utilization across various healthcare domains within healthcare organizations. This study was conducted in Iran during the 2024-2025 period, utilizing a methodological design for the development of the study instrument. Initially, the authors formulated and constructed items for a preliminary questionnaire based on a previously published study within the relevant context. To ensure the instrument's validity and reliability, a comprehensive evaluation was performed using multiple methods, including assessments of face validity, content validity, construct validity, and reliability analysis. The final version of the study instrument consisted of 12 items. The instrument demonstrated excellent validity and reliability. The average factor loading across the instrument's items was 0.8, and the principal component accounted for 65.31% of the total variance. Additionally, both Cronbach's alpha and the intraclass correlation coefficient (ICC) values were 0.95, indicating high internal consistency and reliability. Furthermore, the findings indicated that the level of AI utilization in Iran was predominantly low across most assessed items. The study presented a validated instrument for assessing AI implementation across healthcare decision-making domains. Further research is needed to develop specialized instruments for each decision-making domain to enhance data comprehensiveness.

BACKGROUND: With the global prevalence of chronic diseases increasing, healthcare systems designed primarily for acute conditions face challenges. It is indicated that patient-centered care can effectively manage chronic diseases, potentially improving health outcomes. The authors aimed to develop, assess, and validate a scale to measure patient-centered care in chronic disease services within hospitals. METHODS AND MATERIAL: This study was cross-sectional in nature, carried out in 2023 within a sample of hospitals in Shiraz, a city in the southwestern region of Iran. To ensure the questionnaire's validity and reliability, the authors utilized a variety of methods, including face, content, and construct validity, as well as a reliability assessment. The construct validity and questionnaire reliability were confirmed using exploratory factor analysis and Cronbach's alpha. An exploratory factor analysis and Cronbach's alpha were utilized to conduct the psychometrics. RESULTS: The item construction stage resulted in an initial questionnaire comprising a total of 70 individual items. Following the completion of the face and content validity assessment, seven items were removed due to not meeting the required validity criteria. The remaining questionnaire items had an average impact score of 3.25. Additionally, the content validity ratio (CVR) was 0.51, and the content validity index (CVI) was 0.96. The exploratory factor analysis yielded 12 constructs, which accounted for 63.873% of the variance, with an average factor loading value of 0.646. The final questionnaire comprised 57 items with a 6-point Likert scale. The reliability assessment revealed that the questionnaire had a Cronbach's alpha of 0.938 and an intraclass correlation coefficient of 0.936 in total. CONCLUSIONS: This study developed a robust measure for evaluating patient-centered care in inpatient services for chronic diseases. The findings can inform future research and policy development. Future researchers conducting analogous studies utilizing comparable methodologies in various regions of Iran have the potential to elevate the precision and comprehensiveness of the implications derived for the beneficiaries.

INTRODUCTION/OBJECTIVES: Primary healthcare (PHC) is a comprehensive and efficient system that promotes people's health by providing a range of services in easily accessible locations. Integrating pharmacists into PHC has emerged as a promising approach to enhance healthcare delivery. This study aimed to explore the barriers to integrating community pharmacists into the various components of PHC. METHODS: A scoping review was conducted in 2024 reviewing the evidence published between 2000 and 2024. For such purpose, multiple databases were searched including PubMed, Scopus, ProQuest, Web of Science, and the Google Scholar. RESULTS: A total of 90 studies were found to be eligible for inclusion. The study identified key challenges to pharmacists' integration in PHC across 6 areas: governance issues such as lack of trust and regulatory barriers; financial constraints and payment problems; workforce shortages and inadequate training; infrastructure and technology limitations; restricted access to health information; and service delivery problems including poor collaboration and fragmented care. CONCLUSIONS: The study provided several barriers to pharmacists integration in PHC. In such context, the study proposed policymakers to fund sustainably, strengthen infrastructure, enact supportive policies, and promote collaboration, coordination, and expanded pharmacist roles.

RATIONALE: Research on patient-centeredness in managing chronic diseases like cancer, diabetes, stroke, and heart disorders is gaining prominence. This approach in chronic disease management involves various dimensions and elements, the importance of which has been presented to differ. AIMS AND OBJECTIVES: The present research, carried out in the year 2023, aimed to identify and prioritize existing dimensions and elements of patient-centered care for chronic diseases. METHODS: Firstly, multiple databases including the Cochrane Database of Systematic Reviews, Scopus, PubMed and ProQuest were searched. Secondly, 35 chronic patients were interviewed; Finally, a sample of 30 experts familiar with the context was asked to rank the dimensions and elements of patient-centered care using Best-Worst method. RESULTS: The study identified 6 dimensions and 13 elements of patient-centered care in chronic diseases. The findings indicated that accessibility, quality, education, timeliness and affordability were the five elements that ranked highest in importance for designing and implementing patient-centered care interventions for chronic diseases. CONCLUSION: The results presented that Iranian patients with chronic diseases exhibit a positive attitude towards paternalistic behavior and often prefer others to make the best therapeutic decisions on their behalf. Our research revealed that the concept of patient-centered care differs across regions and cultures, highlighting the need for policymakers and researchers to adapt strategies to the specific characteristics of local populations around the globe.

Lack of transparency in healthcare systems is a critical challenge that undermines accountability, fuels corruption, and erodes public trust. While transparency has been shown to generate multiple positive outcomes, the Iranian healthcare system continues to suffer from significant deficits in this area. To investigate this issue, a qualitative approach was employed, which involved conducting 35 in-depth interviews with experts and social activists engaged in healthcare system transparency at all levels, recruited through purposive stratified sampling method. This was followed by a thematic analysis aimed at exploring the causes and consequences of the phenomenon. The results indicated that the causes of the lack of transparency in the Iranian healthcare system can be categorized into four main domains: sociopolitical, psychological and behavioral, organizational, and ethical and legal factors. Additionally, the consequences of the lack of transparency can be classified into three primary categories: ethical, organizational, and social factors. The findings of this study provide needed data to enhance transparency and cultivate a culture of openness and accountability in the Iranian healthcare system and other similar contexts.

ObjectiveThis study aimed to assess the performance of Artificial Intelligence (AI) compared to human experts in healthcare policymaking.MethodsThis was a mixed-methods cross-sectional study conducted in Iran during the years 2024-2025, comparing, and analyzing the responses of multiple AI Large Language Models (LLMs) including Bing AI Copilot and Gemini and a sample of 15 human experts-using confusion matrix analysis. This analysis provided comprehensive data on the respondents' ability to answer context-specific questions regarding healthcare policy making, evaluated through multiple parameters including sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and overall accuracy.ResultsCopilot demonstrated a sensitivity of 0.867, specificity of 0, PPV of 0.722, NPV of 0, and accuracy of 0.65. In comparison, Gemini exhibited a sensitivity of 0.733, specificity of 0.4, PPV of 0.786, NPV of 0.333, and also an accuracy of 0.65. Additionally, the human experts' responses indicated a sensitivity of 0.5808, specificity of 0.2571, PPV of 0.7189, NPV of 0.1579, and an accuracy of 0.5050.ConclusionThe AI LLMs outperformed human experts in responding to the study questionnaire. The findings demonstrated the considerable potential of the LLMs in enhancing healthcare policy-making, particularly by serving as complementary tools and collaborators alongside humans.

This study aimed to explore the factors that contribute to the presence of paternalism within the Iranian healthcare system. The study was conducted in 2024, conducting 15 semi-structured interviews. The study sample comprised chronic disease patients discharged from a major public hospital in southwestern region of Iran. The interview sample consisted of approximately 54% males with a mean age of 46 years. About 46% of the participants had diabetes, while the remainder had cardiovascular disorders. After completion of the interviews and the subsequent data gathering, Braun and Clarke's method was utilized in order to conduct a thematic analysis on the acquired data. The thematic analysis presented several themes including 'patient trust in physicians', 'lack of patient empowerment', 'patient psychological and communication barriers', and 'healthcare system and infrastructure limitations'. While, the latter theme was the most frequently addressed theme of factors by the study participants, 'limitations in governmental hospitals' was the most frequently addressed item among the sub-themes of the study findings. The presence of paternalism in Iran's healthcare system was delineated to be primarily influenced by the existing limitations in the Iranian healthcare system and its infrastructure. Further research is necessary to investigate and elaborate on this phenomenon in greater detail.

BACKGROUND: The COVID-19 outbreak at the end of 2019 severely impacted global healthcare systems, especially primary healthcare services. This paper aimed to identify the implications derived from the strengths and weaknesses observed in Iran's primary healthcare (PHC) programmes during the pandemic. METHODS: This was a qualitative study conducted in 2021. 13 semistructured interviews were held with Iranian healthcare policymakers and executive managers, selected via snowball sampling, using the World Health Organization's analytical framework. Finally, a thematic analysis was conducted on the interview data. RESULTS: The thematic analysis of the findings yielded five major themes: revision of healthcare financing, redefining education and research in primary healthcare, redefinition of primary healthcare, development of a new model for family medicine, and community engagement. CONCLUSION: Addressing vertical inequality in Iran's healthcare system was delineated to be crucial. Meanwhile, multiple strategies including enhancing family physicians' knowledge and skills, decentralising decision-making, empowering them and involving communities in healthcare planning were presented to improve PHC and family medicine. Further empirical research is needed.

INTRODUCTION/OBJECTIVES: Primary healthcare (PHC) is a comprehensive and efficient system that promotes people's health by providing a range of services in easily accessible locations. Integrating pharmacists into PHC has emerged as a promising approach to enhance healthcare delivery. This study aimed to explore the barriers to integrating community pharmacists into the various components of PHC. METHODS: A scoping review was conducted in 2024 reviewing the evidence published between 2000 and 2024. For such purpose, multiple databases were searched including PubMed, Scopus, ProQuest, Web of Science, and the Google Scholar. RESULTS: A total of 90 studies were found to be eligible for inclusion. The study identified key challenges to pharmacists' integration in PHC across 6 areas: governance issues such as lack of trust and regulatory barriers; financial constraints and payment problems; workforce shortages and inadequate training; infrastructure and technology limitations; restricted access to health information; and service delivery problems including poor collaboration and fragmented care. CONCLUSIONS: The study provided several barriers to pharmacists integration in PHC. In such context, the study proposed policymakers to fund sustainably, strengthen infrastructure, enact supportive policies, and promote collaboration, coordination, and expanded pharmacist roles.

Lack of transparency in healthcare systems is a critical challenge that undermines accountability, fuels corruption, and erodes public trust. While transparency has been shown to generate multiple positive outcomes, the Iranian healthcare system continues to suffer from significant deficits in this area. To investigate this issue, a qualitative approach was employed, which involved conducting 35 in-depth interviews with experts and social activists engaged in healthcare system transparency at all levels, recruited through purposive stratified sampling method. This was followed by a thematic analysis aimed at exploring the causes and consequences of the phenomenon. The results indicated that the causes of the lack of transparency in the Iranian healthcare system can be categorized into four main domains: sociopolitical, psychological and behavioral, organizational, and ethical and legal factors. Additionally, the consequences of the lack of transparency can be classified into three primary categories: ethical, organizational, and social factors. The findings of this study provide needed data to enhance transparency and cultivate a culture of openness and accountability in the Iranian healthcare system and other similar contexts.

The decision-making process in healthcare services encounters numerous challenges. Artificial intelligence (AI) has significantly contributed to enhancing healthcare decision-making. There is a lack of validated instruments available in the literature to measure AI utilization across various healthcare domains. This study aimed to validate an instrument designed to assess the level of AI utilization across various healthcare domains within healthcare organizations. This study was conducted in Iran during the 2024-2025 period, utilizing a methodological design for the development of the study instrument. Initially, the authors formulated and constructed items for a preliminary questionnaire based on a previously published study within the relevant context. To ensure the instrument's validity and reliability, a comprehensive evaluation was performed using multiple methods, including assessments of face validity, content validity, construct validity, and reliability analysis. The final version of the study instrument consisted of 12 items. The instrument demonstrated excellent validity and reliability. The average factor loading across the instrument's items was 0.8, and the principal component accounted for 65.31% of the total variance. Additionally, both Cronbach's alpha and the intraclass correlation coefficient (ICC) values were 0.95, indicating high internal consistency and reliability. Furthermore, the findings indicated that the level of AI utilization in Iran was predominantly low across most assessed items. The study presented a validated instrument for assessing AI implementation across healthcare decision-making domains. Further research is needed to develop specialized instruments for each decision-making domain to enhance data comprehensiveness.

Introduction: In an era where geographical distance has become a barrier to equitable access to health services, an intelligent needs assessment of medical specialties is the key to enabling effective telemedicine. A systematic needs assessment of medical specialties is the cornerstone in designing a telemedicine roadmap. This study aimed to identify and prioritize key specialties based on needs-based criteria for the development of telemedicine in the community covered by Shiraz University of Medical Sciences. Methods: This study's methodology was modeled on the "need-based planning framework for telemedicine services" developed by the University of Queensland, Australia. A mixed approach was used in the study design. In the first three steps, quantitative and qualitative data were collected to identify the community's specialized medical needs. In the fourth step, the identified needs were prioritized. Results: A total of 11 specialties, including neurosurgery, cardiology, general surgery, general internal medicine, orthopedics, pediatrics, neurology, psychiatry, obstetrics and gynecology, imaging, and urology, were identified as key priority specialties needed for the development of telemedicine in the community covered by Shiraz University of Medical Sciences. These specialties were prioritized into eight ranks based on need-based criteria. Conclusion: By identifying and prioritizing 11 key medical specialties, this study has laid a valuable foundation for the development of telemedicine services. Its findings provide policymakers with an effective guide to strategically allocate resources toward prioritized specialties, enhance technological infrastructure, and implement relevant educational programs, thereby ensuring equitable access to services and taking a significant step toward advancing justice in healthcare.

BACKGROUND: With the global prevalence of chronic diseases increasing, healthcare systems designed primarily for acute conditions face challenges. It is indicated that patient-centered care can effectively manage chronic diseases, potentially improving health outcomes. The authors aimed to develop, assess, and validate a scale to measure patient-centered care in chronic disease services within hospitals. METHODS AND MATERIAL: This study was cross-sectional in nature, carried out in 2023 within a sample of hospitals in Shiraz, a city in the southwestern region of Iran. To ensure the questionnaire's validity and reliability, the authors utilized a variety of methods, including face, content, and construct validity, as well as a reliability assessment. The construct validity and questionnaire reliability were confirmed using exploratory factor analysis and Cronbach's alpha. An exploratory factor analysis and Cronbach's alpha were utilized to conduct the psychometrics. RESULTS: The item construction stage resulted in an initial questionnaire comprising a total of 70 individual items. Following the completion of the face and content validity assessment, seven items were removed due to not meeting the required validity criteria. The remaining questionnaire items had an average impact score of 3.25. Additionally, the content validity ratio (CVR) was 0.51, and the content validity index (CVI) was 0.96. The exploratory factor analysis yielded 12 constructs, which accounted for 63.873% of the variance, with an average factor loading value of 0.646. The final questionnaire comprised 57 items with a 6-point Likert scale. The reliability assessment revealed that the questionnaire had a Cronbach's alpha of 0.938 and an intraclass correlation coefficient of 0.936 in total. CONCLUSIONS: This study developed a robust measure for evaluating patient-centered care in inpatient services for chronic diseases. The findings can inform future research and policy development. Future researchers conducting analogous studies utilizing comparable methodologies in various regions of Iran have the potential to elevate the precision and comprehensiveness of the implications derived for the beneficiaries.

ObjectiveThis study aimed to assess the performance of Artificial Intelligence (AI) compared to human experts in healthcare policymaking.MethodsThis was a mixed-methods cross-sectional study conducted in Iran during the years 2024-2025, comparing, and analyzing the responses of multiple AI Large Language Models (LLMs) including Bing AI Copilot and Gemini and a sample of 15 human experts-using confusion matrix analysis. This analysis provided comprehensive data on the respondents' ability to answer context-specific questions regarding healthcare policy making, evaluated through multiple parameters including sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and overall accuracy.ResultsCopilot demonstrated a sensitivity of 0.867, specificity of 0, PPV of 0.722, NPV of 0, and accuracy of 0.65. In comparison, Gemini exhibited a sensitivity of 0.733, specificity of 0.4, PPV of 0.786, NPV of 0.333, and also an accuracy of 0.65. Additionally, the human experts' responses indicated a sensitivity of 0.5808, specificity of 0.2571, PPV of 0.7189, NPV of 0.1579, and an accuracy of 0.5050.ConclusionThe AI LLMs outperformed human experts in responding to the study questionnaire. The findings demonstrated the considerable potential of the LLMs in enhancing healthcare policy-making, particularly by serving as complementary tools and collaborators alongside humans.

This study aimed to explore the factors that contribute to the presence of paternalism within the Iranian healthcare system. The study was conducted in 2024, conducting 15 semi-structured interviews. The study sample comprised chronic disease patients discharged from a major public hospital in southwestern region of Iran. The interview sample consisted of approximately 54% males with a mean age of 46 years. About 46% of the participants had diabetes, while the remainder had cardiovascular disorders. After completion of the interviews and the subsequent data gathering, Braun and Clarke's method was utilized in order to conduct a thematic analysis on the acquired data. The thematic analysis presented several themes including 'patient trust in physicians', 'lack of patient empowerment', 'patient psychological and communication barriers', and 'healthcare system and infrastructure limitations'. While, the latter theme was the most frequently addressed theme of factors by the study participants, 'limitations in governmental hospitals' was the most frequently addressed item among the sub-themes of the study findings. The presence of paternalism in Iran's healthcare system was delineated to be primarily influenced by the existing limitations in the Iranian healthcare system and its infrastructure. Further research is necessary to investigate and elaborate on this phenomenon in greater detail.

BACKGROUND: The COVID-19 outbreak at the end of 2019 severely impacted global healthcare systems, especially primary healthcare services. This paper aimed to identify the implications derived from the strengths and weaknesses observed in Iran's primary healthcare (PHC) programmes during the pandemic. METHODS: This was a qualitative study conducted in 2021. 13 semistructured interviews were held with Iranian healthcare policymakers and executive managers, selected via snowball sampling, using the World Health Organization's analytical framework. Finally, a thematic analysis was conducted on the interview data. RESULTS: The thematic analysis of the findings yielded five major themes: revision of healthcare financing, redefining education and research in primary healthcare, redefinition of primary healthcare, development of a new model for family medicine, and community engagement. CONCLUSION: Addressing vertical inequality in Iran's healthcare system was delineated to be crucial. Meanwhile, multiple strategies including enhancing family physicians' knowledge and skills, decentralising decision-making, empowering them and involving communities in healthcare planning were presented to improve PHC and family medicine. Further empirical research is needed.