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نقش هوش مصنوعی و انفورماتیک پیشرفته در تحول آموزش ژنتیک نوین The Role of Artificial Intelligence and Advanced Informatics in the Evolution of Modern Genetics Education 1 1  مقدمه:  با گسترش فناوری‌های توالی‌یابی نسل جدید (NGS) و تحلیل داده‌های اُمیک، آموزش ژنتیک وارد مرحله‌ای نوین شده که با حجم بالای داده‌های پیچیده سروکار دارد. در این شرایط، روش‌های سنتی آموزشی کارآمدی خود را از دست داده‌اند. بهره‌گیری از هوش مصنوعی (AI) و انفورماتیک زیستی می‌تواند به‌عنوان راهکاری نوآورانه، آموزش ژنتیک را به سطحی تعاملی، داده‌محور و تحلیل‌محور ارتقاء دهد. ضرورت این مطالعه در پاسخ به نیاز روزافزون به آموزش‌های داده‌محور و تحلیلی در حوزه ژنتیک است. با توجه به حجم عظیم داده‌های ژنومی و پیچیدگی تحلیل‌های مورد نیاز، استفاده از ابزارهای AI و انفورماتیک زیستی می‌تواند به‌عنوان راه‌حلی مؤثر در ارتقاء کیفیت آموزش و پژوهش در این حوزه عمل کند. هدف این مطالعه نقش مؤثر هوش مصنوعی و بیوانفورماتیک پیشرفته در بالابردن کیفیت آموزش ژنتیک نوین می‌باشد. روش کار: این مطالعه به‌صورت یک مرور روایتی (narrative review) انجام شده است. منابع علمی منتشرشده در پایگاه‌های PubMed، Scopus و Web of Science همچنین موتور جستجوگر Google scholar بین سال‌های 2005 تا 2025 مورد بررسی قرار گرفتند. مقالات مرتبط با کاربرد AI و انفورماتیک در آموزش ژنتیک انتخاب و تحلیل محتوایی شدند. یافته‌ها: نتایج مرور نشان دادند که ابزارهای مبتنی بر AI، از جمله الگوریتم‌های یادگیری ماشین، مدل‌های زبانی ژنومی و سیستم‌های آموزش تطبیقی، نقش چشمگیری در شخصی‌سازی آموزش، شبیه‌سازی فرآیندهای زیستی، و تحلیل واریانت‌های ژنتیکی ایفا می‌کنند. همچنین، آموزش مهارت‌های عملی در انفورماتیک زیستی شامل کار با پایگاه‌های داده ژنتیکی، نرم‌افزارهای تحلیلی، برنامه‌نویسی زیستی و آمار زیستی کاربردی، موجب توانمندسازی دانشجویان در تحلیل داده‌های پیچیده ژنومی می‌شود. کمبود منابع آموزشی دیجیتال و مربیان متخصص، از چالش‌های کلیدی در مسیر آموزش داده‌محور است. نتیجه‌گیری: ترکیب هوش مصنوعی و انفورماتیک زیستی با آموزش ژنتیک، می‌تواند به‌عنوان رویکردی نوین برای تربیت متخصصان توانمند در علوم ژنومی عمل کند. توسعه محتواهای بومی‌سازی‌شده، دوره‌های آموزشی مجازی، و سیاست‌گذاری در جهت همگام‌سازی نظام آموزش با پیشرفت‌های فناوری، از راهکارهای مؤثر برای ارتقاء کیفیت آموزش ژنتیک در ایران و کشورهای مشابه است. 2 Introduction: With the expansion of next-generation sequencing (NGS) technologies and omics data analysis, genetics education has entered a new phase characterized by large volumes of complex data. In this context, traditional teaching methods have become less effective. Utilizing artificial intelligence (AI) and bioinformatics offers an innovative approach to elevate genetics education to an interactive, data-driven, and analysis-focused level. This study responds to the growing demand for data-driven and analytical training in genetics. Given the vast amount of genomic data and the complexity of the required analyses, employing AI and bioinformatics tools can significantly enhance the quality of education and research in this field. The aim of this study is to investigate the impactful role of advanced AI and bioinformatics in improving modern genetics education. Method: This study was conducted as a narrative review. Scientific sources published in PubMed, Scopus, Web of Science, and Google Scholar between 2005 and 2025 were reviewed. Articles related to the use of AI and informatics in genetics education were selected and analyzed using content analysis. Results: The review results indicated that AI-based tools, including machine learning algorithms, genomic language models, and adaptive training systems, significantly contribute to personalizing education, simulating biological processes, and analyzing genetic variants. Furthermore, practical training in bioinformatics skills—such as working with genetic databases, analytical software, biological programming, and applied biostatistics—empowers students to analyze complex genomic data. However, the lack of digital educational resources and specialized instructors continues to pose a major challenge in data-driven education. Conclusion: The integration of AI and bioinformatics into genetics education offers an innovative approach to training specialists in modern genetics. Developing localized content, virtual training courses, and policies that align the education system with technological advancements are effective strategies for enhancing the quality of genetics education in Iran and similar countries. 105 116 2025/04/30 1404/2/10 2025/08/28 1404/6/6 احمدرضا بشارت نیا Ahmadreza Beshratania دانشجوی کارشناسی ارشد ژنتیک پزشکی، مرکز تحقیقات سرطان، دانشگاه علوم پزشکی شهرکرد، شهرکرد، ایران ایران a.rezabesharatnia@gmail.com ثریا قاسمی Sorayya Ghasemi دکتری ژنتیک پزشکی، دانشیار، مرکز تحقیقات سرطان، دانشگاه علوم پزشکی شهرکرد، شهرکرد، ایران ایران sorayya.ghasemi@gmail.com Modern Genetics Artificial Intelligence Learning Data-Driven Learning Omics Informatics Deep Learning ژنتیک نوین هوش مصنوعی آموزش داده‌محور انفورماتیک زیستی یادگیری عمیق تحلیل داده‌های ژنومی [1]. Feng Q, Li J, Zhang Q. Exploring the Unknown: The Application and Prospects of Artificial Intelligence in Genomics and Bioinformatics. Health 2024;16(9):837-48. doi: 10.4236/health.2024.169059.##[2]. 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تقویت خطوط ملی و مراکز تماس سلامت برای مدیریت بحران: یک خلاصه سیاستی Strengthening National Health Hotlines and Call Centers for Crisis Management: A Policy Brief 1 1 همه‌گیری کووید-۱۹ فشار زیادی بر نظام سلامت وارد کرده و نیاز جامعه به اطلاعات معتبر و حمایت روانی-اجتماعی را افزایش داده است. جهت پاسخ به این بحران، مرکز تماس ۴۰۳۰ به‌صورت شبانه‌روزی خدمات اطلاع‌رسانی و مشاوره روانی را ارائه نموده است. این خلاصه سیاستی بر پایه مطالعه کیفی که در سال ۲۰۲۳ انجام شده و در آن با ۶۴ نفر از کارکنان مرکز تماس، مشاوران و تماس‌گیرندگان مصاحبه صورت گرفته تدوین شده است. در این مطالعه تحلیل داده‌ها با روش تحلیل چارچوبی انجام شد. یافته‌ها نشان داد که نقاط قوت این سامانه دربرگیرنده غربالگری گسترده تلفنی، ارائه مشاوره روانشناسی به‌موقع، انتشار اطلاعات معتبر و دسترس‌پذیری گسترده بوده است. در مقابل، کمبود نیروی انسانی، ضعف آموزشی، محدودیت فناوری، نبود ادغام کافی با نظام سلامت و فقدان سازوکار بازخورد از چالش‌های اصلی این مرکز تماس بودند. اقدامات راهبردی پیشنهادی شامل تقویت زیرساخت فناورانه، آموزش و ارزیابی مستمر، توسعه دامنه خدمات، ایجاد نظام بازخورد و تثبیت منابع انسانی است. این مداخلات می‌تواند کیفیت، عدالت و تاب‌آوری خطوط ملی و مراکز تماس سلامت را در بحران‌های آینده ارتقاء دهد. 2 The COVID-19 pandemic placed significant pressure on Iran’s health system and increased public demand for reliable information and psychosocial support. In response, the 4030 call center provided 24/7 guidance, psychological counseling, and health information to the public. This policy brief is based on a qualitative study conducted in 2023, which involved interviews with 64 participants, including call center staff, consultants, and callers. Data were analyzed using framework analysis to identify key themes related to the call center's performance. The results indicated major strengths, such as large-scale telephone screening, timely psychological support, credible information dissemination, and broad accessibility. However, several challenges were identified, including workforce shortages, training gaps, technological limitations, insufficient integration with the formal health system, and a lack of structured feedback mechanisms. Recommended strategic actions include strengthening technological infrastructure, institutionalizing training, evaluating performance, expanding the service scope, establishing systematic feedback, and stabilizing the workforce. These measures aim to enhance the quality, equity, and resilience of national health hotlines and call centers during future health crises. 117 123 2025/04/302025/08/25 1404/6/3 2025/08/282025/09/3 1404/6/12 مریم اسلامی جهرمی Maryam Eslami Jahromi دکترای مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران maryam.e246@gmail.com هاله آیت اللهی Haleh Ayatollahi دکترای انفورماتیک پزشکی، استاد، مرکز تحقیقات علوم مدیریت و اقتصاد سلامت، پژوهشکده مدیریت سلامت، دانشگاه علوم پزشکی ایران، تهران، ایران ایران ayatollahi.h@iums.ac.ir علی ابرازه Ali Ebrazeh دکترای مدیریت خدمات بهداشتی، دانشیار، گروه بهداشت عمومی، دانشکده بهداشت، دانشگاه علوم پزشکی قم، قم، ایران ایران ebraze1880@yahoo.com Call center COVID-19 Hotline Policy Brief مرکز تماس کووید-19 خط تلفن خلاصه سیاستی [1]. Krausz M, Westenberg JN, Vigo D, Spence RT, Ramsey D. Emergency response to COVID-19 in Canada: Platform development and implementation for ehealth in crisis management. JMIR Public Health Surveill 2020;6(2):e18995. doi: 10.2196/18995. ##[2]. Willems SH, Rao J, Bhambere S, Patel D, Biggins Y, Guite JW. Digital solutions to alleviate the burden on health systems during a public health care crisis: COVID-19 as an opportunity. JMIR Mhealth Uhealth 2021;9(6):e25021. doi: 10.2196/25021. ##[3]. Eaton J, Rahman A, Gater R, Saxena S, Hammerich A, Saeed K. From adversity to resilience in the COVID-19 era: Strengthening mental health systems in the Eastern Mediterranean Region. East Mediterr Health J 2020;26(10):1148-50. doi: 10.26719/2020.26.10.1148. ##[4]. Gao Y, Liu R, Zhou Q, Wang X, Huang L, Shi Q, et al. COVID-19 Evidence and recommendations working group. application of telemedicine during the coronavirus disease epidemics: A rapid review and meta-analysis. Ann Transl Med 2020;8(10):626. doi: 10.21037/atm-20-3315.##[5]. Ravindran S, Channaveerachari NK, Seshadri SP, Kasi S, Manikappa SK, Cherian AV, et al. Crossing barriers: Role of a tele-outreach program addressing psychosocial needs in the midst of COVID-19 pandemic. Asian J Psychiatr 2020;53:102351. doi: 10.1016/j.ajp.2020.102351##[6]. Song R, Choi YS, Ko JY. Operating a national hotline in korea during the COVID-19 pandemic. Osong Public Health Res Perspect 2020;11(6):380-2. doi: 10.24171/j.phrp.2020.11.6.06##[7]. Cher BAY, Wilson EA, Pinsky AM, Townshend RF, Wolski AV, Broderick M, et al. Utility of a telephone triage hotline in response to the COVID-19 pandemic: Longitudinal observational study. J Med Internet Res 2021;23(11):e28105. doi: 10.2196/28105##[8]. Eslami Jahromi M, Ayatollahi H, Ebrazeh A. Covid-19 hotlines, helplines and call centers: A systematic review of characteristics, challenges and lessons learned. BMC Public Health 2024;24(1):1191. doi: 10.1186/s12889-024-18702-8. ##[9]. Keykhaei M, Koolaji S, Mohammadi E, Kalantar R, Saeedi Moghaddam S, Aminorroaya A, et al. Dissection of non-pharmaceutical interventions implemented by Iran, South Korea, and Turkey in the fight against COVID-19 pandemic. J Diabetes Metab Disord 2021;20(2):1919-31. doi: 10.1007/s40200-021-00877-1##[10]. Azizi H, Davtalab-Esmaeili E. Iranian first-line health care providers practice in COVID-19 outbreak. Iran J Public Health 2020;49(Suppl 1):119-21. doi: 10.18502/ijph.v49iS1.3681. ##[11]. Asadzadeh A, Mohammadzadeh Z, Fathifar Z, Jahangiri-Mirshekarlou S, Rezaei-Hachesu P. A framework for information technology-based management against COVID-19 in Iran. BMC Public Health 2022;22(1):402. doi: doi: 10.1186/s12889-022-12781-1##[12]. Jahromi ME, Ayatollahi H, Ebrazeh A. Covid-19 hotlines/helplines and call centers: A responsive public health strategy. In: Ordonez de Pablos P, Lytras M, editors. Digital tools and data for innovative healthcare: A vision for healthier and more inclusive societies. Academic Press; 2025. p. 147-72.##[13]. Ashoorkhani M, Mirzania M, Maleki M, Taghdisi MH, Shakibazadeh E. Lessons learned from the establishment of a public response telephone system in the COVID-19 epidemic crisis in Iran. Hakim Health Systems Research 2020;20(3):306-19. [In Persian]## ##

شناسایی قطعات الیگونوکلئوتیدی شاخص در ژنوم ویروس هپاتیت سی در فرار از مکانیسم اینترفرونی با استفاده از روش‌های وزن دهی در داده کاوی Identification of Key Oligonucleotide Fragments in the Hepatitis C Virus Genome in Evasion of Interferon Mechanisms Using Weighting Algorithms 1 1 مقدمه: حضور قطعات کوتاه الیگونوکلئوتیدی تشکیل دهنده ژنوم ویروس‌ها، منحصر به فرد بوده و تفاوت در فراوانی آن‌ها در بین گونه‌های مختلف، نقش بسزایی در بیماری‌زایی گونه ویروسی‌، فرار از سیستم ایمنی و پاسخ به درمان ضد ویروسی  ایفا می‌کند. داده‌کاوی و استفاده از مدل‌های وزن‌دهی از جمله روش‌هایی است که به نظر می‌رسد در شناسایی این قطعات شاخص می‌تواند کمک کننده باشد. با توجه به اهمیت حضور قطعات الیگونوکلئوتیدی شاخص در توالی ژنومی ویروس هپاتیت C در مقاومت به درمان اینترفرونی و فرار از سیستم ایمنی ذاتی میزبان در این مطالعه تصمیم گرفته شد این قطعات شاخص، با به کارگیری مدل‌های وزن‌دهی بر روی توالی ژنومی ویروس هپاتیت C مورد شناسایی قرار گیرد. روش کار: در این مطالعه تصمیم گرفته شد برای اولین بار، با به کارگیری ده مدل مختلف وزن‌دهی‌، قطعات الیگونوکلئوتیدی کوتاه از جمله، توالی‌های دی نوکلئوتیدی، تری نوکلئوتیدی و تترا نوکلئوتیدی  متمایز کننده دو گروه افراد آلوده به ویروس هپاتیت C (HCV)، با پاسخ درمانی متفاوت  به داروی اینترفرون (INF) مورد شناسایی  قرار گیرد. برای این هدف، ده مدل الگوریتم وزن‌دهی (‌Weighting algorithms)، بر روی فراوانی نسبی قطعات الیگونوکلئوتیدی کوتاه تشکیل دهنده ژنوم HCV‌، در ژنوتیپ 1a,1b و 2b در دو گروه از بیماران مقاوم و حساس به درمان اینترفرونی با استفاده از نرم افزار Rapid Miner اجرا و مورد ارزیابی قرار گرفت. یافته‌ها: در ادامه تعدادی از قطعات الیگونوکلئوتیدی‌، از جمله UU, UA, UC در بین توالی‌های دی نوکلئوتیدی و  ,GUA, CUA, CGUA در بین توالی‌های تری نوکلئوتیدی و تترانوکلئوتیدی به طور معنی‌داری در بین دو گروه از افراد متفاوت بودند، که با توجه به مطالعات قبلی حضور این توالی‌ها نقش مهمی در پاسخ اینترفرونی بر علیه ویروس‌ها ایفا می‌کند.  نتیجه‌گیری: نتایج این مطالعه نشان داد که  با به کارگیری مدل‌های مختلف وزن‌دهی در داده کاوی می‌توان شاخص‌های مهم ژنومی ویروس را که در فرار سیستم ایمنی میزبان و درمان‌های  ضد ویروسی  مؤثر هستند، در مراحل اولیه پیش‌بینی کرده و در ادامه، در مطالعات تجربی، برای طراحی دارو و واکسن آن‌ها را مورد هدف قرار داد.    2 Introduction: The presence of short oligonucleotide fragments that constitute the viral genome is unique, and the variation in their abundance among different species significantly influences the pathogenicity of viral strains, evasion of the immune system, and response to antiviral treatment. Data mining and the use of weighting models are effective methods for identifying these characteristic fragments. Given the importance of oligonucleotide fragments in the genomic sequence of the hepatitis C virus (HCV) concerning resistance to interferon treatment and evasion of the host's innate immune system, this study aims to identify these characteristic fragments using weighting models applied to the HCV genomic sequence. Method: In this study, we employed ten different weighting models for the first time to identify short oligonucleotide fragments, including distinguishing dinucleotide, trinucleotide, and tetranucleotide sequences, between two groups of individuals infected with HCV who exhibit different therapeutic responses to interferon (INF) treatment. Ten weighting algorithms were executed and evaluated based on the relative abundance of short oligonucleotide fragments constituting the HCV genome in genotypes 1a, 1b, and 2b from two groups of patients resistant and sensitive to interferon treatment using Rapid Miner software. Results: Several oligonucleotide fragments, including UU, UA, and UC among dinucleotide sequences, and GUA, CUA, and CGUA among trinucleotide and tetranucleotide sequences, showed significant differences between the two groups. Previous studies indicate that the presence of these sequences plays a crucial role in the interferon response against viruses. Conclusion: The results of this study suggest that employing various weighting models in data mining can help predict important genomic indicators of the virus that are effective in evading the host's immune system and antiviral treatments at early stages. These indicators can subsequently be targeted in experimental studies for drug and vaccine design. 124 131 2025/04/302025/08/252025/06/26 1404/4/5 2025/08/282025/09/32025/09/3 1404/6/12 زهرا عرب بافرانی Zahra Arab‐Bafrani دانشیار، دکترای فیزیک پزشکی، مرکز تحقیقات اختلالات متابولیک ، دانشگاه علوم پزشکی گلستان، گرگان، ایران ایران arabbafrani@goums.ac.ir مجید نیکوبین بروجنی Majid Nikoubin‐Boroujeni کارشناسی ارشد مهندسی نرم افزار، گروه مهندسی کامپیوتر، واحد گرگان، دانشگاه آزاد اسلامی، گرگان، ایران ایران m.nikoubinboroujeni@iau.ir الهام موسوی Elham Mousavi استادیار، دکترای ویروس‌شناسی پزشکی، مرکز تحقیقات قارچ شناسی و باکتری‌شناسی پزشکی‌، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران e.mousavi@kmu.ac.ir Weighting algorithms Data mining Genome sequences Oligonucleotide fragments مدل‌های وزن‌دهی داده‌کاوی توالی ژنومی قطعات الیگونوکلئوتیدی [1]. Sanjuán R, Nebot MR, Chirico N, Mansky LM, Belshaw R. Viral mutation rates. J Virol 2010;84(19):9733–48. doi: 10.1128/JVI.00694-10##[2]. 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A functional investigation of the suppression of CpG and UpA dinucleotide frequencies in plant RNA virus genomes. Sci Rep 2019;9(1):18359. doi: 10.1038/s41598-019-54853-0##[8]. Ficarelli M, Antzin-Anduetza I, Hugh-White R, Firth AE, Sertkaya H, Wilson H, et al. CpG dinucleotides inhibit HIV-1 replication through zinc finger antiviral protein (ZAP)-dependent and-independent mechanisms. J Virol 2020;94(6):e01337-19. doi: 10.1128/JVI.01337-19##[9]. Miyazato P, Matsuo M, Tan BJY, Tokunaga M, Katsuya H, Islam S, et al. HTLV-1 contains a high CG dinucleotide content and is susceptible to the host antiviral protein ZAP. Retrovirology 2019;16(1):38. doi: 10.1186/s12977-019-0500-3##[10]. Takata MA, Gonçalves-Carneiro D, Zang TM, Soll SJ, York A, Blanco-Melo D, et al. CG dinucleotide suppression enables antiviral defence targeting non-self RNA. Nature 2017;550(7674):124–7. doi: 10.1038/nature24039##[11]. Petruzziello A. 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معماری‌های رایانشی سلسله مراتبی و تحلیل خدمات در سیستم‌های مراقبت بهداشتی مبتنی بر مه‌: یک مرور ساختاریافته Hierarchical architectures and service analysis in fog-based healthcare systems: A systematic study 1 1 مقدمه: سیستم‌های مراقبت‌های بهداشتی برای بهبود دسترسی و کیفیت خدمات، بیشتر به مدل‌های رایانشی وابسته می‌شوند. اگرچه رایانش ابری مقیاس‌پذیری را فراهم می‌کند، اما محدودیت‌های ذاتی آن، مانند تأخیر و پهنای باند بر کاربردهای حیاتی تأثیر منفی می‌گذارد. رایانش مه خود را به عنوان یک جایگزین انقلابی معرفی می‌کند که پردازش و ذخیره‌سازی را به لبه شبکه می‌آورد. این مرور ساختاریافته، رایانش مه را در زمینه خدمات مراقبت‌های بهداشتی ارزیابی می‌کند و طبقه‌بندی خدمات، نیازهای معماری و مسائل مربوط به استقرار را پوشش می‌دهد. روش کار: این مرور ساختاریافته، ۳۵۱۹ مقاله مرتبط با مراقبت‌های بهداشتی مبتنی بر رایانش مه را از پایگاه‌های IEEE، Springer، ACM، ScienceDirect، Taylor & Francis و Willey در بازه‌ زمانی ۲۰۱۸ تا ۲۰۲۲ با کلیدواژه‌های مرتبط با «Fog Computing» و «Healthcare» شناسایی کرد. معیارهای انتخاب شامل طول بیش از شش صفحه، دسترسی به متن کامل و ارتباط محتوایی بودند و مواردی مانند انواع مرور، کتاب و مقاله‌های نامرتبط حذف شدند. در نهایت، ۱۲۵ مقاله با هدف شناسایی خدمات، ارزیابی کیفیت، تحلیل معماری‌ها و استقرار اجزاء بررسی و ویژگی‌ها، نیازمندی‌های عملکردی و غیرعملکردی آن ها تحلیل شد. یافته‌ها: این مطالعه نشان می‌دهد که اهمیت مدل‌های رایانشی در مراقبت‌های بهداشتی عمدتاً به تحرک بیماران و فاصله آن‌ها از مراکز درمانی برمی‌گردد، که نیاز به خدمات سیار و نظارت از راه دور را افزایش می‌دهد. در این خدمات، علائم حیاتی بیمار برای تصمیم‌گیری سریع به مراکز درمانی ارسال می‌شود. نیازهای عملکردی اصلی شامل اشتراک‌گذاری و پردازش داده‌ها و نیاز غیرعملکردی مهم، عملکرد حساس به تأخیر است. معماری‌های رایج مانند مدل‌های n- رده و لایه‌ای، با قابلیت استقرار مستقل طراحی شده‌اند. چالش‌ها شامل منابع محدود در لبه و ضعف در هماهنگی پویا بین خدمات است.  نتیجه‌گیری: رایانش مه با کاهش محدودیت‌های رایانش ابری، خدمات مراقبت بهداشتی را بهبود می‌دهد، اما چالش‌هایی مانند استانداردسازی و مدیریت پویای منابع پابرجا است. مطالعات آینده باید معماری‌های متناسب با نوع خدمات، استقرار تطبیقی برای کاربران سیار و ثبت وقایع دقیق را توسعه دهند. این بررسی بر لزوم راه‌حل‌های سفارشی و قابل‌تأیید برای ارائه خدمات بهداشتی بلادرنگ تأکید دارد و پیشنهاد می‌کند معماری‌های آگاه از زمینه در اولویت قرار گیرند.   2 Introduction: Introduction: Healthcare systems are increasingly reliant on computing models to improve the accessibility and quality of services. While cloud computing provides scalability, its inherent limitations—such as latency and bandwidth—negatively impact critical applications. Fog computing emerges as a transformative alternative, bringing processing and storage closer to the network edge. This systematic review evaluates fog computing within the context of healthcare services, addressing service classification, architectural requirements, and deployment challenges. Method: We identified articles between 2018 and 2022 by searching six electronic databases, using keywords related to “Fog Computing” and “Healthcare.” The inclusion criteria were full-text availability and relevance to the topic. Reviews, books, and unrelated studies were excluded. Ultimately, the included articles were analyzed to identify services, assess quality, examine architectures, and evaluate components along with their features, functional, and non-functional requirements. Results: The search generated 3,519 studies, of which 125 studies were included. This study highlights the significance of computing models in healthcare systems based on two main factors: patient mobility and their distance from medical facilities, which necessitate mobile-remote services. These services typically involve remote patient monitoring, wherein vital signs are transmitted to healthcare centers for timely decision-making by care providers. Core functional requirements focus on data manipulation, including sharing and processing, while the main non-functional requirement is performance, with sensitivity to delay. Common architectural models include n-tier and layered structures, where each tier can be deployed independently based on specific needs. Key deployment challenges include limited resources at edge tiers and inadequate dynamic coordination among services. Conclusion: Fog computing enhances healthcare services by reducing the limitations of cloud computing; however, challenges such as standardization and dynamic resource management persist. Future studies should focus on developing service-specific architectures, adaptive deployment strategies for mobile users, and precise event logging. This review highlights the necessity for customizable and verifiable solutions to provide real-time healthcare services, proposing context-aware architectures as a priority.   132 158 2025/04/302025/08/252025/06/262025/07/5 1404/4/14 2025/08/282025/09/32025/09/32025/09/4 1404/6/13 رضا سوخت سرایی Reza Sookhtsaraei دانشجوی دکتری، دانشکده مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران ایران r.sookhtsaraei@eng.basu.ac.ir مهدی سخایی نیا Mehdi Sakhaei-nia استادیار، دانشکده مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران ایران sakhaei@basu.ac.ir فرشته آزادی پرند Fereshteh-Azadi Parand دانشکده آمار، علوم ریاضی و رایانه، دانشگاه علامه طباطبایی، تهران، ایران ایران parand@atu.ac.ir Fog computing Healthcare services Quality of services Functional requirements Non-functional requirements رایانش مه سرویس‌های مراقبت بهداشتی کیفیت سرویس نیازمندی‌های عملکردی نیازمندی‌های غیرعملکردی [1]. National Academies of Sciences E, Medicine, Utilization CoHC, Disabilities Aw. 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تحلیل علم‌سنجی و داده‌کاوی روندهای پژوهشی در بیماری مولتیپل اسکلروزیس با تمرکز بر طب مکمل و جایگزین (CAM) Bibliometric and Data Mining Analysis of Research Trends in Multiple Sclerosis with a Focus on Complementary and Alternative Medicine (CAM) 1 1 مقدمه:  مولتیپل اسکلروزیسل (MS) یک بیماری خودایمنی چندعاملی است که توجه زیادی به استفاده و اثربخشی درمان‌های طب مکمل و جایگزین (CAM) در مدیریت آن شده است. این بیماری به دلیل ماهیت پیچیده و مزمن خود، نیازمند رویکردهای درمانی جامع و چندوجهی است که طب مکمل می‌تواند نقش مکملی در بهبود کیفیت زندگی بیماران ایفا کند. روش کار: این مطالعه با استفاده از روش‌های علم‌سنجی و داده کاوی، به بررسی روندها، موضوعات کلیدی و توسعه مفهومی تحقیقات مرتبط با MS در زمینه CAM می‌پردازد و ساختار موضوعی، کلمات کلیدی و تحولات پژوهشی در این حوزه را ترسیم می‌کند. تحلیل داده‌های منتشرشده در پایگاه Web of Science با بهره‌گیری از ابزارهای پیشرفته مانند SciMAT و  Biblioshiny  انجام شد تا نقشه‌های علمی، خوشه‌بندی موضوعی و روندهای پژوهشی در دو دوره زمانی ۲۰۱۱-۲۰۱۵ و ۲۰۱۶-۲۰۲۰ ترسیم شود. همچنین تحلیل خوشه‌بندی سلسله‌مراتبی برای شناسایی موضوعات کلیدی و دندروگرام کلمات کلیدی به کار گرفته شد. یافته‌ها: نتایج نشان داد حوزه‌های مهمی مانند داروهای گیاهی، مداخلات غذایی، یوگا، ذهن‌آگاهی، اسیدهای چرب اشباع، اینترلوکین ۴، روغن گل مغربی، تمرینات تنفسی و کانابینوئیدها در تحقیقات MS و CAM برجسته هستند. درصد قابل توجهی از بیماران MS  از درمان‌های CAM استفاده می‌کنند که مزایای قابل توجهی داشته و مکمل درمان‌های متداول است. تحلیل خوشه‌بندی سلسله‌مراتبی سه خوشه موضوعی اصلی شامل اسیدهای چرب اشباع، التهاب و طب مکمل، و درمان‌های توانبخشی را نشان داد. همچنین، روند پذیرش و تأثیر درمان‌های CAM در مراقبت از بیماران رو به افزایش است و موضوعات نوظهوری مانند فیتواستروژن‌ها و الیگودندروسیت‌ها در دوره دوم ظهور کردند. نتیجه‌گیری: تحقیقات کتاب‌سنجی در حوزه MS و‌CAM ، نه تنها درک فعلی از شیوه‌های درمانی را بهبود می‌بخشد، بلکه مسیرهای جدیدی برای هدایت تحقیقات بالینی و دانشگاهی آینده فراهم می‌کند. این مطالعه اهمیت رویکردهای جامع، چندرشته‌ای و مبتنی بر بیمار را در مراقبت‌های بهداشتی برجسته می‌سازد و به پژوهشگران کمک می‌کند تا بر موضوعات نوظهور تمرکز کنند و درمان‌های مؤثرتری برای بیماران MS توسعه دهند. 2 Introduction: Multiple sclerosis (MS) is a multifactorial autoimmune disease that has garnered increasing attention for the use and efficacy of complementary and alternative medicine (CAM) in its management. Due to its complex and chronic nature, MS requires a comprehensive and multidimensional therapeutic approach in which CAM may play a supportive role in improving patients’ quality of life. Method: This study employs bibliometric and data mining methods to examine research trends, key topics, and conceptual developments in MS-related CAM studies. It aims to map the thematic structure, keyword evolution, and scholarly progress in this interdisciplinary field. Data were extracted from the Web of Science database and analyzed using advanced tools such as SciMAT and Biblioshiny. Scientific mapping, thematic clustering, and trend analysis were conducted across two time periods: 2011–2015 and 2016–2020. Hierarchical clustering and keyword dendrograms were utilized to identify core research themes. Results: The findings identified key areas of research, including herbal medicine, dietary interventions, yoga, mindfulness, saturated fatty acids, interleukin-4, evening primrose oil, breathing exercises, and cannabinoids. A significant number of MS patients report using CAM therapies alongside conventional treatments, citing notable benefits. Hierarchical clustering revealed three major thematic clusters: saturated fatty acids, inflammation and CAM, and rehabilitative therapies. Moreover, the adoption of CAM therapies and their impact on patient care have shown a growing trend, while emerging topics such as phytoestrogens and oligodendrocytes gained prominence. Conclusion: Bibliometric analysis in the fields of MS and CAM enhances the current understanding of therapeutic practices and paves the way for future clinical and academic research. This study highlights the importance of integrative, multidisciplinary, and patient-centered approaches in healthcare, guiding researchers toward emerging topics and more effective treatment strategies for MS patients. 159 177 2025/04/302025/08/252025/06/262025/07/52025/06/25 1404/4/4 2025/08/282025/09/32025/09/32025/09/42025/09/4 1404/6/13 نجمه متقی Najmeh Motaghi دکترای تخصصی طب سنتی ایرانی، استادیار‌، مرکز تحقیقات بیماری‌های مغز و اعصاب، پژوهشکده نوروفاماکولوژی‌، دانشگاه علوم پزشکی کرمان، کرمان‌، ایران ایران n.motaghi34@gmail.com حمیده ارون Hamide Arvan دکتری تکمیلی تخصصی (فلوشیپ) / صرع‌، استادیار‌، مرکز تحقیقات بیماری‌های مغز و اعصاب‌، پژوهشکده نوروفاماکولوژی‌، دانشگاه علوم پزشکی کرمان، کرمان‌، ایران ایران hamide.md62@gmail.com بنت الهدی ضیاالدینی دشتخاکی Bentolhoda Ziaadini Dashtkhaki دکتری تکمیلی تخصصی (فلوشیپ) / نوروماسکولر‌، استادیار، مرکز تحقیقات بیماری‌های مغز و اعصاب، پژوهشکده نوروفاماکولوژی، دانشگاه علوم پزشکی کرمان‌‌، کرمان، ایران ایران dr.bentalhodaziaadini@gmail.com کاوه شفیعی Kaveh Shafiei دکتری تخصصی پزشکی‌، استادیار‌، مرکز تحقیقات بیماری های مغز و اعصاب‌، پژوهشکده نوروفاماکولوژی، دانشگاه علوم پزشکی کرمان‌، کرمان، ایران ایران kshafiee@gmail.com مهدیه عزیزیان Mahdieh Azizian دکترای تخصصی ریاضی کاربردی‌، استادیار‌، گروه آموزش عمومی، دانشکده پزشکی، دانشگاه علوم پزشکی کرمان‌، کرمان‌، ایران azizian11211@gmail.com هدی کمالی Hoda Kamali دکتری تکمیلی تخصصی (فلوشیپ) / مولتیپل اسکلروزیس، استادیار، مرکز تحقیقات بیماری های مغز و اعصاب ، پژوهشکده نوروفاماکولوژی‌، دانشگاه علوم پزشکی کرمان‌، کرمان، ایران ایران hoda_3303@yahoo.com علی اکبر خاصه Ali Akbar Khasseh دکترای تخصصی علم اطلاعات و دانش‌شناسی‌، دانشیار‌، گروه کتابداری و اطلاع‌رسانی، دانشگاه پیام نور تهران، تهران، ایران ایران khasseh@gmail.com مهدیه خزانه ها Madiyeh Khazaneha دکترای تخصصی علم اطلاعات و دانش‌شناسی‌، استادیار ، گروه آموزش عمومی، دانشکده پزشکی‌، مرکز تحقیقات بیماری‌های مغز و اعصاب‌، پژوهشکده نوروفاماکولوژی، دانشگاه علوم پزشکی کرمان‌، کرمان‌، ایران ایران khazaneha.m@gmail.com مهدیه شریف زاده Mahdieh Sharifzadeh دکترای تخصصی پزشکی بیهوشی، استادیار، گروه بیهوشی، دانشکده پزشکی‌، دانشگاه علوم پزشکی کرمان‌، کرمان، ایران ایران msharifzade78@gmail.com Multiple Sclerosis Inflammation Autoimmunity Bibliometrics Scientific Mapping مولتیپل اسکلروزیس التهاب خودایمنی علم ‌سنجی‌ تحلیل نقشه علمی [1]. von Kutzleben S, Pryce G, Giovannoni G, Baker D. 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مدیریت الکترونیک نیروی انسانی در سازمان‌های بهداشتی: یک مطالعه مقطعی Electronic Human Resource Management in Healthcare Organizations: A Cross-Sectional Study 1 1 مقدمه:  مدیریت الکترونیک کارکنان در سازمان‌های سلامت با چالش‌های متعددی از جمله مدیریت منابع مالی، استخدام الکترونیکی، آموزش آنلاین، ارزیابی عملکرد، سیستم پاداش الکترونیکی و ارتباطات دیجیتال روبه‌رو است. این مشکلات می‌تواند به کاهش کارایی و کیفیت خدمات بهداشتی منجر شوند؛ بنابراین اجرای استراتژی‌های مدیریت الکترونیک منابع انسانی با تکیه بر فناوری‌های روز، استانداردسازی فرآیندها و آموزش مستمر کارکنان می‌تواند کارایی و کیفیت خدمات بهداشتی را بهبود بخشد؛ لذا هدف از مطالعه حاضر بررسی چگونگی مدیریت الکترونیک کارکنان در سازمان‌های سلامت است. روش کار: این مطالعه توصیفی- مقطعی در سال 1404-1403 انجام شد. جامعه پژوهش 109 نفر از کارکنان  بیمارستان آموزشی- درمانی رازی شهر ایلام بودند. نمونه گیری به روش دسترسی آسان انجام شد. ابزار گردآوری داده‌ها پرسشنامه استاندارد مدیریت منابع انسانی الکترونیک بود. تجزیه و تحلیل داده‌ها با استفاده از آزمون آمار توصیفی - انجام شده است. یافته‌ها: مطابق با یافته‌ها «کاهش هزینه‌های آموزش» (0/09±4/09)‌، «جذب بهینه داوطلبان واجد شرایط» (0/83±3/60)، «ارائه اطلاعات دقیق به کارکنان از طریق برنامه‌های مرخصی الکترونیکی» (0/91 ±3/54) بیشترین میانگین را کسب کردند. کمترین نمره میانگین «‌ارسال بخشنامه‌ها، روش‌های کار، گزارش‌های کاری و غیره به صورت الکترونیک» (0/93 ±2/19)، «ارائه آموزش‌های الکترونیک از طریق برنامه‌ها و فیلم‌های آموزشی» ( 0/93 ±2/90) و «دسترسی راحت به آزمون‌های استخدامی به صورت الکترونیک» (1/07± 2/91) مربوط بود. با توجه به برتری نمره‌ها در موارد آموزشی و استخدامی به صورت الکترونیک، می‌توان بیان کرد که سازوکارهای دیجیتال محور برای اطلاع‌رسانی، آموزش و ارزیابی کارکنان کلیدی و حیاتی است. نتیجه‌گیری: این مطالعه به عنوان یک مرجع علمی می‌تواند به پژوهشگران و مدیران در زمینه مدیریت الکترونیک کارکنان در سازمان‌های سلامت کمک کند و راهکارهایی برای بهبود این فرآیند ارائه دهد. همچنین می‌تواند به طراحی و پیاده‌سازی استراتژی‌های مبتنی بر داده برای پایش و بهبود مستمر عملکرد کارکنان کمک کند. توصیه می‌شود در اجرای این سیستم‌ها به امنیت داده‌ها، آموزش مستمر کاربران و جمع‌آوری بازخوردهای منظم از کارکنان توجه شود تا کارایی و پذیرش سازمانی افزایش یابد. 2 Introduction: Electronic management of employees in health organizations faces several challenges, including financial resource management, electronic recruitment, online training, performance appraisal, electronic reward system, and digital communications. These problems can lead to a decrease in the efficiency and quality of health services. Therefore, the aim of the present study is to investigate how electronic management of employees in health organizations is carried out. Method: This descriptive cross-sectional study was conducted from 2024 to 2025. The research population consisted of 109 health workers at Razi Hospital in Ilam. Sampling was conducted using an easy access method. Data were collected using a standard electronic human resource management questionnaire. Data analysis involved descriptive statistics. Results: The findings revealed that "reducing training costs" (4.07 ± 0.90), "optimal recruitment of qualified candidates" (3.60 ± 0.83), and "providing accurate information to employees through electronic leave programs" (3.54 ± 0.91) obtained the highest average scores. In contrast, the lowest average scores were associated with "sending circulars, work methods, work reports, similar documents electronically" (2.19 ± 0.93), "providing electronic training through educational programs and videos" (2.90 ± 0.93), and "easy access to employment exams electronically" (2.91 ± 1.07). The superior ratings in educational and employment matters suggest that digital-based mechanisms for informing, training, and evaluating employees are crucial. Conclusion: This study can assist researchers and managers in the field of electronic HRM in health organizations by offering solutions to improve these processes. It can also guide the design and implementation of data-driven strategies for monitoring and continuous improvement of employee performance. It is recommended that data security, ongoing user training, and regular collection of feedback from employees be incorporated into the implementation of these systems to enhance efficiency and organizational acceptance. 178 188 2025/04/302025/08/252025/06/262025/07/52025/06/252025/07/2 1404/4/11 2025/08/282025/09/32025/09/32025/09/42025/09/42025/08/25 1404/6/3 رضا جوادیان Reza Javadian کارشناسی ارشد مدیریت تحول، دانشگاه ایلام، ایلام، ایران ایران fatemehdinari111@gmail.com خدیجه مولایی Khadijeh Moulaei دکتری انفورماتیک پزشکی، استادیار، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران moulaei.kh91@gmail.com زهرا محمودزاده ثاقب Zahra Mahmoudzadeh Saqeb دکترای مدیریت اطلاعات سلامت، استادیار، دانشکده مدیریت و اطلاع‌رسانی پزشکی، کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی شیراز، شیراز، ایران ایران zm_222@yahoo.com فاطمه دیناری Fatemeh Dinari دانشجوی دکترای مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی شیراز، شیراز، ایران ایران fatemehdinari67@yahoo.com Electronic Human Resource Management E-HRM Healthcare organizations E-recruitment E-learning مدیریت الکترونیک منابع انسانی e-HRM سازمان‌های بهداشتی استخدام الکترونیکی آموزش الکترونیکی [1]. 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تشخیص ناهنجاری کف پا با استفاده از یادگیری عمیق بهبود یافته Planter Deformity Detection Using Improved Deep Learning Model 1 1 مقدمه:  صافی کف پا و دیگر ناهنجاری‌های ساختاری کف پا از عوامل اصلی در بروز اختلالات اسکلتی–عضلانی به شمار می‌آیند و می‌توانند کیفیت زندگی افراد را به‌طور چشمگیری کاهش دهند. تشخیص زودهنگام این مشکلات نقش مهمی در پیشگیری از پیشرفت عوارض و انتخاب روش‌های درمانی مناسب ایفا می‌کند. در سال‌های اخیر، استفاده از روش‌های مبتنی بر یادگیری عمیق در حوزه بیومکانیک و مهندسی پزشکی رشد قابل توجهی داشته است. این روش‌ها با بهره‌گیری از قابلیت‌های تحلیل خودکار داده‌های تصویری و بالینی، امکان ارائه مدل‌های دقیق‌تر و کاربردی‌تر را فراهم می‌سازند. پژوهش حاضر با هدف ارائه مدلی نوین برای تشخیص ناهنجاری‌های کف پا طراحی و پیاده‌سازی شده است که هم‌زمان به تحلیل ابعاد ساختاری، زاویه‌ای و فشاری کف پا می‌پردازد. روش کار: در این مطالعه تصاویر کف پای افراد سالم و دارای ناهنجاری گردآوری و به‌عنوان داده ورودی به سیستم پردازش داده ارائه شد. ابتدا ناحیه قوس کف پا با بهره‌گیری از تکنیک‌های پیشرفته سگمنت‌بندی، استخراج گردید. سپس خروجی این مرحله به مدل YOLO بهبودیافته همراه با مکانیزم توجه CBAM وارد شد تا فرآیند شناسایی ناهنجاری‌ها با دقت و تمرکز بیشتری بر نواحی بحرانی انجام پذیرد. علاوه بر آن، به منظور تحلیل جامع‌تر، زوایای کف پا از تصاویر محاسبه شد و با الگوی توزیع فشار در کف پا ترکیب گردید تا یک نمایه چندوجهی از وضعیت کف پا حاصل شود. یافته‌ها: نتایج حاصل از ارزیابی مدل بر روی داده‌های آزمایشی نشان داد که روش پیشنهادی توانست به دقت 95/14 درصد در شناسایی ناهنجاری‌های کف پا دست یابد. مقایسه عملکرد مدل با روش‌های مرسوم و رقیب حاکی از آن بود که مدل ارائه‌شده نه‌تنها در سطح دقت، بلکه در سرعت پردازش و قابلیت تمرکز بر نواحی حساس کف پا نیز عملکرد بهتری ارائه کرده است. ترکیب اطلاعات زاویه‌ای و فشار در کنار تصاویر سگمنت‌بندی‌شده، موجب افزایش قابل توجه توان مدل در شناسایی انواع ناهنجاری‌ها گردید. نتیجه‌گیری: مدل معرفی‌شده با ساختاری ساده و در عین حال کارآمد، قابلیت شناسایی ناهنجاری‌های کف پا را با دقت بالا فراهم می‌سازد. این رویکرد می‌تواند به‌عنوان ابزاری ارزشمند در غربالگری‌های پزشکی، طراحی کفی‌های طبی و پایش روند درمان بیماران به کار گرفته شود. همچنین با توجه به سهولت پیاده‌سازی و سرعت پردازش، امکان استفاده از این مدل در محیط‌های بالینی و مراکز توانبخشی وجود دارد. بدین ترتیب، پژوهش حاضر می‌تواند زمینه‌ساز توسعه سامانه‌های هوشمندتر در حوزه سلامت دیجیتال و پزشکی پیش‌بینانه باشد. 2 Introduction: Flatfoot and other structural deformities of the foot are major causes of musculoskeletal disorders that can significantly impair quality of life. Early detection of these abnormalities is crucial for preventing the progression of complications and selecting appropriate treatment strategies. In recent years, the application of deep learning methods in biomechanics and medical informatics has gained significant momentum, providing powerful tools for the automated and accurate analysis of medical imaging data.  This study aims to develop a novel deep learning-based model for detecting foot abnormalities, addressing structural, angular, and plantar pressure aspects simultaneously. Method: Foot images from both healthy subjects and individuals with abnormalities were collected and preprocessed. Initially, the arch region of the foot was segmented using advanced segmentation techniques. The segmented images were then passed to an enhanced YOLO architecture integrated with the Convolutional Block Attention Module (CBAM), enabling the network to focus more effectively on critical regions. Additionally, angular measurements of the foot were extracted and combined with plantar pressure distribution data to achieve a more comprehensive assessment of foot abnormalities. Results: Experimental evaluations demonstrated that the proposed model achieved an accuracy of 95.14% in detecting foot abnormalities. Comparative analyses with other state-of-the-art methods revealed that the developed approach outperformed competing techniques, not only in classification accuracy but also in computational efficiency and its ability to focus on clinically relevant regions of the foot. The integration of angular and pressure-related features with segmented image data significantly enhanced the system's robustness and precision in identifying various types of abnormalities. Conclusion: The proposed model, with its relatively simple yet effective architecture, provides a reliable solution for the accurate identification of foot abnormalities. This approach can be applied in medical screening, orthopedic insole design, and patient monitoring during rehabilitation. Furthermore, given its computational efficiency and ease of deployment, the model can be integrated into clinical environments and rehabilitation centers. 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واکاوی علل کسورات بیمه‌ای ناشی از نقص مستندسازی بالینی: تمرکز بر ابعاد کیفیت داده‌ها Insurance Deductions from Clinical Documentation Gaps: A Data Quality Perspective 1 1 مقدمه: بخش مهمی از منابع مالی  بیمارستان‌ها از طریق عقد قرارداد با سازمان‌های بیمه‌گر تأمین می‌شود. رد مطالبات مالی و اعمال کسورات از سوی سازمان‌های بیمه‌گر به دلایل مختلف از جمله مستند‌سازی ضعیف پرونده‌های پزشکی بر بیمارستان‌ها تحمیل می‌شود. در این مطالعه ضمن بررسی علل و میزان کسورات بیمه تأمین اجتماعی، به بررسی ابعاد نقض شده کیفیت داده‌ها در پرونده‌های مشمول کسورات ناشی از نقص مستندسازی پرداخته شد. روش کار: این مطالعه توصیفی -مقطعی در سال 1403 در بیمارستان شهید صدوقی شهر یزد و در طی دو مرحله انجام شد. در مرحله اول گردآوری داده‌ها با استفاده از چک لیست انجام گرفت و خدمات مشمول کسورات در تعداد 893 پرونده بستری مشمول کسورات بیمه تأمین اجتماعی استخراج شد. پس از گروه‌بندی این خدمات،  علت و مبلغ هزینه کسورات برای هر گروه از خدمات تعیین شد. در مرحله دوم علل کسورات مربوط به نقص مستندسازی بر اساس ابعاد کیفیت داده‌ها دسته بندی شدند. تحلیل داده‌ها با استفاده از نرم افزار‌های آماری اکسل 2016 و  SPSS نسخه 16 و محاسبه شاخص‌های آمار توصیفی شامل فراوانی و درصد فراوانی انجام گرفت. یافته‌ها: بیشترین میزان کسورات مربوط به بخش اورژانس (1/24%) بود، در حالیکه بیشترین مبلغ کسورات به بخش  مراقبت‌های ویژه به ارزش 2,980,112,989 ریال اختصاص داشت. از نظر گروه خدمت بیشترین کسورات مربوط به خدمات مراقبت پرستاری و آزمایش‌های تشخیص طبی به ترتیب با 22/9% و 18% بود. بیشترین علت کسورات به نقص مستندسازی (45/3%) اختصاص داشت. همچنین از نظر ابعاد کیفیت اطلاعات نقض شده، بیشترین علل کسورات به کامل نبودن مستندات و اطلاعات ثبت شده (62/4%) اختصاص داشت. نتیجه‌گیری: نقص مستندسازی به ویژه در بعد کامل بودن داده‌ها، کسورات قابل توجهی را به بیمارستان تحمیل می‌کند. در این راستا سیستم‌های اطلاعات سلامت با ارائه قالب استاندارد به منظور ثبت الکترونیکی داده‌ها و ارائه هشدار جهت ثبت اطلاعات ضروری می‌توانند کمک کننده باشند؛ همچنین توصیه می‌شود پزشکان و سایر مستندسازان در زمینه آشنایی با اصول مستندسازی مطابق با دستورالعمل‌های پرداختی سازمان‌های بیمه‌گر آموزش ببینند. همچنین انجام ممیزی‌های منظم و ارائه بازخورد به مستندسازان به منظور اطمینان از تداوم حفظ کیفیت اطلاعات ثبت شده توصیه می‌گردد. 2 Introduction: A significant portion of hospitals' financial resources is acquired through contracts with insurance companies. Insurance companies often reject claims or impose deductions on hospitals for various reasons, with poor medical record documentation being a major factor. This study aimed to identify the causes and extent of social security insurance deductions while evaluating which dimensions of data quality in medical records were compromised due to inadequate documentation. Method: This descriptive cross-sectional study was conducted in 2024 at Shahid Sadoughi Hospital in Yazd, divided into two phases. In the first phase, data collection was carried out using a checklist to extract services subject to deductions from 893 medical records covered by social security insurance. After grouping these services, the causes and extent of deductions were identified for each service group. In the second phase, documentation-related deductions were categorized according to data quality dimensions. Data analysis was performed using Excel and SPSS, employing descriptive statistics. Results: The highest deduction rate was observed in the emergency department at 24.1%, while the intensive care units had total deductions amounting to 2,980,112,989 rials. By service group, the greatest deductions were related to nursing care services (22.9%) and diagnostic tests (18%). Overall, documentation deficiencies represented the largest proportion of deductions at 45.3%. Within the data quality dimensions, incomplete documentation was the most frequent cause (62.4%). Conclusion: Documentation deficiencies, particularly regarding data completeness, significantly contribute to hospital deductions. Health information systems can help by offering a standardized format for electronic data recording and alerts for essential information. Additionally, it is recommended that physicians and other healthcare providers receive training on documentation practices that align with insurance payment guidelines. Furthermore, regular audits and feedback should be implemented to ensure continuous improvement and maintain the quality of medical record data. 204 215 2025/04/302025/08/252025/06/262025/07/52025/06/252025/07/22025/05/72025/06/29 1404/4/8 2025/08/282025/09/32025/09/32025/09/42025/09/42025/08/252025/08/272025/09/1 1404/6/10 مهتاب کرمی Mahtab Karami دکترای مدیریت اطلاعات سلامت، دانشیار، مرکز توسعه تحقیقات بالینی، دانشگاه علوم پزشکی شهید صدوقی یزد، یزد، ایران ایران mkarami33@gmail.com شکوفه افخمی Shokoofeh Afkhami دکترای مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران ایران sh.afkhami122@yahoo.com یاسر غلمانی Yaser Ghelmani دانشیار، متخصص داخلی، گروه داخلی، دانشکده پزشکی، دانشگاه علوم پزشکی شهید صدوقی یزد، یزد، ایران ایران yaser.ghelmani58@gmail.com منیره صادقی جبلی Monireh Sadeqi Jabali دکترای مدیریت اطلاعات سلامت، استادیار، مرکز تحقیقات فناوری اطلاعات سلامت، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران msadeqi2005@gmail.com Hospital Documentation Social Security Insurance Data Accuracy بیمارستان مستندسازی تأمین اجتماعی بیمه صحت داده‌ها [1]. 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