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آینده پژوهی در سلامت: انتخاب بهترین مدل هوشمند مبتنی بر داده کاوی برای پیش بینی و تشخیص سرطان کبد در مراحل اولیه Futures Studies in Health: Choosing the Best Intelligent Data Mining Model to Predict and Diagnose liver Cancer in Early Stage 1 1 مقدمه: سرطان اولیه کبد HCC)) پنجمین سرطان شایع در دنیا و سومین عامل مرگ و میر در جهان می­باشد. علائم سرطان کبد پس از بروز به سرعت پیشرفت کرده و در صورت عدم تشخیص به موقع متأسفانه بقای عمر بیمار بسیار کم می­ گردد. یکی از مشکلات اصلی پیش روی متخصصین گوارش، پیش بینی و تشخیص زود هنگام سرطان کبد است. داده کاوی از روش­هایی است که در این زمینه  مورد استفاده واقع می­ گردد. هدف از انجام این مطالعه معرفی بهترین مدل هوشمند مبتنی بر داده کاوی برای پیش­ بینی و تشخیص سرطان کبد در مراحل اولیه می­باشد. روش: در مقاله حاضر با استفاده از روش مطالعه گذشته نگر، پرونده 516 بیمار مبتلا به سرطان کبد اولیه و ثانویه و 22 ریسک فاکتور، از هر بیمار، مورد بررسی قرار گرفت. داده ­های جمع ­آوری شده با استفاده از 5 مدل داده کاویVFI Classifier ،Regression Classifier ،HyperPipes Classifier ، Functional trees with logistic regression  و  Meta Muti Class Classifier تحلیل شدند. این مدل­ها با  یکدیگر مقایسه شدند. نتایج: دقت، ویژگی، حساسیت و سطح زیر منحنی  Rocمـدل VFI Classifier به تـرتیب  71/29%،  49%،  50% و 63/31% می­ باشد و  این مدل به عنوان بهترین مدل هوشمند مبتنی برداده کاوی برای پیش بینی و تشخیص سرطان کبد در مراحل اولیه شناخته شد. نتیجه ­گیری: در صورتی که مدل داده­ کاوی VFI Classifier به صورت صحیح طراحی شود، می­تواند سرطان کبد را پیش بینی نماید یا آن را در مراحل اولیه تشخیص دهد. 2 Introduction: Primary liver cancer (­HCC), is the fifth most common type of cancer and the third leading cause of death in the world. Symptoms of liver cancer will progress rapidly after the onset of the disease, and unfortunately, the patients' survival rate is very low. One of the main problems for gastroenterologists is the prediction and early detection of liver cancer. Data mining techniques can be used to understand and predict cancer. The aim of this study was to identify the best model based on intelligent data mining to predict and diagnose liver cancer in an early stage. Method: In the present article, a retrospective study was conducted on 516 cases of primary and secondary liver cancer, and 22 risk factors were examined. Data were collected from the patients' files and analyzed using 5 data mining models including VFI Classifier, Regression Classifier, Hyper Pipes Classifier, Functional trees with logistic regression, and Meta Multi Class Classifier with the highest precision (Precision). These models were compared. Results: The precision, sensitivity, specificity, and the area under the curve of VFI Classifier model were respectively 71.29%, 49%, 50%, and 63.31%, and VFI Classifier model is the best model based on intelligent data mining to predict and diagnose liver cancer in an early stage. Conclusion: If properly designed, data mining model VFI Classifier can predict liver cancer or detect it in an early stage. 133 140 2015/02/17 1393/11/28 2015/09/16 1394/6/25 فائزه افضلی Faezeh Afzali کارشناس فناوری اطلاعات سلامت، مرکز تحقیقات مدل سازی در سلامت، پژوهشکده آینده پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران f.afzali90@gmail.com زهره حیدری Zohreh Heidari کارشناس فناوری اطلاعات سلامت، مرکز تحقیقات مدل سازی در سلامت، پژوهشکده آینده پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران zheydari8809@yahoo.com میترا منتظری Mitra Montazeri کارشناس ارشد هوش مصنوعی، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران montazeri_mitra@yahoo.com لیلا احمدیان Leila Ahmadian دکترای تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران ahmadianle@yahoo.com محمد جواد زاهدی Mohammad Javad Zahedi فوق تخصص گوارش، استاد، مرکز تحقیقات گوارش و کبد، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران zahedimj@yahoo.com Liver Cancer Hepatocellular Carcinoma (HCC) Prediction and Detection Data Mining Futures studies on Health سرطان کبد هپاتوسلولار کارسینوما (HCC) پیش‌بینی و تشخیص داده‌کاوی آینده پژوهی در سلامت 1. Nobahar M, Vafai A. General Medicine. 2th ed. Tehran: Boshra; 2011##2. Jamali R, Jamali A. Non-alcoholic fatty liver disease Feyz. 2010; 14(2):169-81. Persian.##3. Fazelipour S, Toutian Z, Davoudi P. The effect of the heroin on histological structure of liver in mouse (Balb/C). Medical Sciences Journal of Islamic Azad University Tehran Medical Branch. 2006; 16‌(2)‌: 91-4. Persian.##4. Shamsi-Baghbanan H, Sharifiyan A, Kamali H, Saghebi R, Ghobanifar Z, Minaei M. Assessment of phlebotomy in liver diseases in Iranian traditional medicine. Journal of Islamic and Iranian Traditional Medicine. 2013; 3 (4) :461-76. Persian.##5. Makhdoumi P. Evaluation of anti carcinogenicity effects of new analogues of selective COX II inhibitor in human liver carcinoma cell line; 2012 Sep 4-6; Mashhad: Mashhad University of Medical Sciences; 2011. ##6. Goodarzi Z, Jazayeri SM. Hepatitis B virus pres variant and hepatocellular carcinoma. Hepatitis Monthly. 2008;8(2):129-33.##7. Bosch FX, Ribes J, Cleries R, Diaz M. Epidemiology of hepatocellular carcinoma. Clin Liver Dis. 2005;9(2):191-211. ##8. Bosch FX, Ribes J, Diaz M, Cleries R. Primary liver Cancer: worldwide incidence and trends. Gastroenterology. 2004;127(5): 5-16.##9. Montazeri M, Bahaadinbeigy K, Rahnama Z, Montazeri M. Comparison of the accuracy of digital image-based and patient visit-based diagnoses in an Iranian dermatology clinic. J Basic Appl Sci Res. 2013; 3(11):28-33.##10. Montazeri M, Bahrololoum A, Nezamabadi-pour H, Soleymani Baghshah M, Montazeri M. Cooperating of local searches based hyperheuristic approach for solving traveling salesman problem. 3th International Conference on Evolutionary Computation Theory and Applications; 2011 Oct 24-26; Paris: Sci TePress; 2011. ##11. Bahrampour A, Montazeri M. Prediction of breast cancer mortality by hidden markov model [dissertation]. Kerman: Kerman University of Medical Sciences, Kerman; 2015.##12. Montazeri M, Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh. 2014; 16(1): 53-9. Persian.##13. Montazeri M, Nezamabadipour H. Automatic extraction of eye field from a gray intensity image using intensity filtering and hybrid projection function. Communications, Computing and Control Applications (CCCA), 2011 International Conference on; 2011 Mar 3-5; Hammamet: IEEE; 2011. p. 1-5.##14. Montazeri M, NezamabadiPour H, Montazeri M. automatically eye detection with different gray intensity image conditions. Computer Technology and Application. 2012; 3: 525-32.##15. Montazeri M, Montazeri M‌, Montazeri M, Beigzadeh A. Machine learning models in breast cancer survival prediction. Technol Health Care. 2015. ##16. Lin RH. An intelligent model for liver disease diagnosis. Artif Intell Med. 2009;47(1):53-62.##17. Montazeri, M, Montazeri M, Beygzadeh A, Zahedi MJ. Identifying efficient clinical parameters in diagnose of liver disease. HealthMED. 2014; 1115.##18. Montazeri M, Naji HR, Faraahi A. A novel memetic feature selection algorithm. Information and Knowledge Technology (IKT). 5th Conference on; 2013 May 28-30; Shiraz: IEEE; 2013.## 19. Montazeri M, Naji HR, Montazeri M. Memetic feature selection algorithm based on efficient filter local search. J Basic Appl Sci Res. 2013; 3(10): 126-33.##20. Avci E, Turkoglu I. An intelligent diagnosis system based on principle component analysis and ANFIS for the heartvalve diseases. Expert Systems with Applications. 2009;36(2):2873-8.##21. Castanho MJ, Hernandes F, De Ré AM, Rautenberg S, Billis A. Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems with Applications. 2013;40(2):466-7.##22. Pinheiro F, Kuo MH, Thomo A, Barnett J. Mining Association Rules in the BCCA Liver Cancer Data Set. Stud Health Technol Inform. 2015;208:308-13.##23. Jamarani SM, Moradi MH, Behnam H, Rad GR. Intelligent system for breast cancer prognosis using multiwavelet packets and neural network. International Enformatika Conference, IEC'05; 2005 Aug 26-28; Prague, Czech Republic; 2005. ##24. Alipoor M, Haddadnia J. An accurate intelligent breast cancer diagnosis system. Iranian Journal of Breast Disease. 2009;2(2):33-40. Persian.##25. Demiröz G, Güvenir HA. Classification by voting feature intervals. Machine Learning: Springer Berlin Heidelberg; 1997.##26. Frank E, Wang Y, Inglis S, Holmes G, Witten IH. Using model trees for classification. Machine Learning. 1998;32(1):63-76.##27. Webb S, Caverlee J, Pu C. Predicting web spam with HTTP session information. Proceedings of the 17th ACM conference on Information and knowledge management; 2008 Oct 26-30; Napa Valley, California, USA: ACM; 2008.##28. Gama J. Functional trees. Machine Learning. 2004;55(3):219-50.##29. Landwehr N, Hall M, Frank E. Logistic model trees. Machine Learning. 2005;59(1-2):161-205.##30. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter. 2009;11(1):10-8.##31. Montazeri M, Bahrololoum A, Enhesari A, Hyper-heuristic algorithm for finding efficient features in diagnose of lung cancer disease. J Basic Appl Sci Res. 2013; 3(10): 134-40.##32. Guvenir HA, Demiroz G, Ilter N. Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif Intell Med. 1998;13(3):147-65. ##33. Reddy A, Dash C, Leerapun A, Mettler TA, Stadheim LM, Lazaridis KN, et al. Hypothyroidism: a possible risk factor for liver cancer in patients with no known underlying cause of liver disease. Clin Gastroenterol Hepatol. 2007;5(1):118-23.##34. McGlynn KA, London WT. Epidemiology and natural history of hepatocellular carcinoma. Best Pract Res Clin Gastroenterol. 2005;19(1):3-23.##35. Somi M, Fakhrjo A, Shakeri M, Farhang S. Skull metastasis as the first presentation of hepatocellular carcinoma. Govaresh. 2005; 10(4): 208-49.##36. Chan CH, Trost N, McKelvie P, Rophael JA, Murphy MA. Unusual case of skull metastasis from hepatocellular carcinoma. ANZ J Surg. 2004;74(8):710-3.##37. Qureshi SS, Shrikhande SV, Borges AM, Shukla PJ. Chest wall metastases from unknown primary hepatocellular carcinoma. J Postgrad Med. 2005;51(1):41-2. ##38. Sabnani SV. Computer security: A machine learning approach [dissertation]. England: Department of Mathematics Royal Holloway, University of London Egham; 2008.## ##

نیاز سنجی و تعیین اولویت بیمارستان‌های آموزشی کرمان در خصوص پیاده‌سازی فناوری RFID Need and Priority Assessment of Kerman's Teaching Hospitals to Implementation of RFID Technology 1 1 مقدمه: فناوری شناسایی به کمک امواج رادیویی (RFID) یکی از دستاوردهای فناوری اطلاعات می­باشد که می­تواند به صورت همزمان کار مدیریت اطلاعات، احراز هویت و ردیابی را به طور کامل مورد پوشش قرار دهد. پژوهش حاضر برآن است تا به نیازسنجی و تعیین اولویت بیمارستان­های آموزشی کرمان در خصوص پیاده سازی فناوری  RFID بپردازد. روش: پژوهش حاضر از نوع توصیفی- مقطعی بود که به صورت کمی در سال 1394 در شش بخش بیمارستان­های آموزشی کرمان، انجام گرفت. ابزار گردآوری اطلاعات، پرسشنامه­ ای بود که برای هر بخش به طور مجزا طراحی شد. برای تجزیه و تحلیل داده­ها از نرم افزار SPSS نسخه 20  استفاده شد. نتایج: یافته ­های پژوهش حاضر نشان داد که بخش اورژانس بیشترین ضرورت پیاده ­سازی فناوری RFID را بر اساس وضعیت عملکردی این بخش در بیمارستان­های مورد مطالعه داشت. همچنین، از دیدگاه افراد مورد مطالعه، اولویت پیاده ­سازی RFID به ترتیب در بخش­های اورژانس، تجهیزات پزشکی بیشتر از سایر بخش­ها بود. نتیجه­گیری: از آنجایی که بخش­های مورد بررسی در بیمارستان­های آموزشی کرمان دارای مشکلات بسیاری بودند و تداوم در وقوع هر کدام از این مشکلات می­تواند موجب ناکارآمدی فعالیت­های بیمارستان­ها، افزایش هزینه ­ها و در نهایت به خطر افتادن جان بیماران شود، بنابراین پیاده­ سازی سیستم RFID جهت رفع این مشکلات، ضروری می­باشد. با توجه به اهمیت زمان در بخش اورژانس و نیز بررسی دیدگاه پرسنل مورد مطالعه، بخش اورژانس بیشترین نیاز جهت پیاده ­سازی فناوری RFID را دارا بود. 2 Introduction: Radio frequency identification technology (RFID) is one of the information technology achievements that can simultaneously cover the information management, authentication, and tracking. This study is intended to determine the needs and priority assessment of Kerman's teaching hospitals to implement RFID technology. Method: This study was a cross-sectional and quantitative one carried out in six wards of teaching hospitals in Kerman city in 2015. The data collection tool was a questionnaire that was separately designed for each ward. SPSS software version 20 was used for data analysis. Results: The results showed that the emergency department, based on its functional status, had the most urgent need to implement RFID technology. Also, from the perspective of the study subjects, the priority of RFID implementation respectively in emergency departments and medical equipment was more than other wards. Conclusion: Since the studied wards of Kerman teaching hospitals have a lot of problems, and prevalence of any of these problems can lead to ineffectiveness of activity in hospitals, increase in costs, and ultimately endangering the lives of patients, implementation RFID system is essential to solve these problems. Considering the importance of time in the emergency department and also given the studied personnel point of view, emergency department had the greatest need to RFID Implementation technology. 141 148 2015/02/172015/10/24 1394/8/2 2015/09/162015/11/14 1394/8/23 رضوان انصاری Rezvan Ansari دانشجوی کارشناسی ارشد فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی‌، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران ansari.rezvan20@gmail.com اسماعیل مهرآیین Esmaeil Mehraeen دانشجوی دکترای مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران. ایران es.mehraeen@gmail.com اعظم صباحی Azam Sabahi دانشجوی کارشناسی ارشد فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی‌، دانشگاه علوم پزشکی کرمان، کرمان، ایران. ایران sabahiazam@yahoo.com Needs Assessment Radio Frequency Identification Technology Implementation Hospital نیازسنجی فناوری شناسایی به کمک امواج رادیویی پیاده سازی بیمارستان 1. Mehraeen E, Ayatollahi H, Ahmadi M. A Study of Information Security in Hospital Information Systems. Health Inf Manage. 2014; 10(6):788-79. Persian.##2. Mehraeen E, Safdari R, Ghazi Saeedi M. The security challenges of hospital information system. Indian Journal of Applied Research. 2015; 5(7):312-14. ##3. Safdary R, Dargahi H, Mahmoodi M, Torabi M, Mohammad Zadeh N. Assessing the viewpoint of faculty members of medical record departments in Iran about the impact of Information Technology on health system 2004. Iranian South Medical Journal. 2006; 9 (1): 93-101. Persian. ##4. Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011; 30(3):464-71.##5. Dargahi H, Mohammad Zadeh N, Rezaian H. E-Business in Health Care Systems. Payavard Salamat. 2011; 5‌ (3):39-48. Persian.##6. AHIMA. Embracing the Future: New Times, New Opportunities for Health Information Managers. [cited 2015 June 21] Available from: URL: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_027397.hcsp?dDocName=bok1_027397/##7. Correa FA, Gil MJ, Redín LB. Benefit of connecting RFID and Lean principle in health care. Journal of Business Economic. 2005; 7(8):‌44-65.##8. Karkkainen M, Ala-Risku T.Automatic identification, applications and technologies 2002; [cited 2015 June 21] Available from: URL: http://lrg.tkk.fi/logistics/publications/LRN_Auto-ID.pdf##9. Wu Y, Ranasinghe DC, Sheng QZ, Zeadally S, Yu J. RFID enabled traceability networks: A survey. Distrib Parallel Databases. 2011; 29(5-6):397-443.##10. Yao W, Chao HC, Zang L. The Use of RFID in Healthcare: Benefits and Barriers, RFID-Technology and Applications (RFID-TA), 2010 IEEE International Conference on; 17-19 June 2010; Guangzhou: IEEE; 2010: 128 – 34.##11. Jones P, Clarke-Hill C, Shears P, Comfort D, Hillier D. Radio frequency identification in the UK: opportunities and challenges. International Journal of Retail & Distribution Management. 2004; 32‌‌(3):164-71.##12. Radio Frequency Identification (RFID) In Healthcare. [cited 2015 Jun 21]. Available from: http://www.hibcc.org/publication/view/understanding-rfid-in-healthcare/##13. Vanany I, Shaharoun ABM. Barriers and critical success factors towards RFID technology adoption in South-East Asian Healthcare Industry. Proceedings of the 9th Asia Pacific Industrial Engineering - Management Systems Conference, Bali-Indonesia; 2008. p. 148 –55.##14. Fuhrer P, Guinard D. Building a Smart Hospital using RFID technologies. Proceedings of the 1st European Conference on eHealth (ECEH06); 2006; Oct 12-13; Fribourg, Switzerland. p. 131-42.##15. Bendavid Y, Boeck H. Using RFID to improve hospital supply chain management for high value and consignment items. Procedia Computer Science. 2011; 5: 849–56.##16. Hosseini SM, Tabibi SJ, Jasbi J, Nasiri Pour AA, Sepehri MM. Designing a model to assess the readiness of hospitals to implement RFID. Healthcare Management 2010; 2(3-4): 43-7. Persian##17. Sharma N, Youn J, Shrestha N, Ali H. Direction finding signage system using RFID for health care Application. Bio Medical Engineering and Informatics, 2008. BMEI 2008. International Conference on; 27-30 May 2008‌; Sanya: IEEE; 2008. p. 900 –4. ##18. Tzeng S, Chen HW, Pai FY. Evaluating the business value of RFID: Evidence from five case studies. International Journal of Production Economics. 2008;2(112): 601-13.##19. Cangialosi A, Monaly J, Yang S, editors. Applying RFID to patient care: Challenges and opportunities. Proceeding of the Information Resources Management Association (IRMA) International Conference; 2007.##20. Sepehri M, Mollabagher M. A model for implementing radio frequency identification technology in hospitals a case study: surgery wards in Firouzgar hospital. J Health Adm. 2011; 14 (44):33-40. Persian.##21. Fisher JA, Monahan T. Tracking the social dimensions of RFID systems in hospitals. Int J Med Inform. 2008; 77(3):176-83.##22. Peris-Lopez P, Orfila A, Mitrokotsa A, van der Lubbe JC. A comprehensive RFID solution to enhance inpatient medication safety. Int J Med Inform. 2011; 80 (1):13-24.##23. Schuerenberg B. Bar codes vs. RFID: a battle just beginning. Health Data Manag. 2006;14(10):32-4.##24. Kim DS, Kim J, Kim SH, Yoo SK. Design of RFID based the Patient Management and Tracking System in hospital. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1459-61.##25. Cole SL, Siddiqui J, Harry DJ, Sandrock CE. WiFi RFID demonstration for resource tracking in a statewide disaster drill. Am J Disaster Med. 2011; 6(3):155-62.##26. Nikodijevic A, Pichler P, Forjan M, Sauermann S. Bedside patient data viewer using RFID and e-Ink technology. Stud Health Technol Inform. 2014;198:1-8.##27. Ajami S, Rajabzadeh A. Radio Frequency Identification (RFID) technology and patient safety. J Res Med Sci. 2013; 18(9):809-13.##28. Mirzaee M, Soltani A, Ilati S, Masdar L. Survey of Application area of RFID technology in healthcare. Journal of Health Information Management Association. 2012; 7(2). Persian.## ##

بررسی ویژگی‌های بیماران مبتلا به سل با استفاده از روش خوشه‌بندی K-Means The investigation of TB patients features with K-Means clustering 1 1 مقدمه: به گزارش سازمان سلامت جهانی، بیماری سل بیشترین عامل مرگ و میر در بیماری­های عفونی است. با توجه به بالا بودن درصد افراد مبتلا به سل و تعداد زیاد مرگ و میر در بین این بیماران، این تحقیق با هدف دسته بندی و پیدا کردن ارتباط بین ویژگی­های بالینی و دموگرافیک بیماران مختلف انجام شده است. روش: این پژوهش مطالعه ­ای توصیفی، تحلیلی بوده که به روی 600 بیمار مرکز تحقیقات سل بیمارستان مسیح دانشوری انجام شده است. برای انجام دسته ­بندی و تعیین شاخص­های مشترک بین بیماران از الگوریتم­های داده کاوی خوشه­ بندی K-Means و قوانین باهم آیی Apriori به کمک نرم افزار SPSS Clementine  نسخه 14 استفاده شده است. نتایج: به کمک شاخص دان، تعداد 3خوشه به عنوان خوشه بهینه انتخاب شده­ اند. عوامل مشترک بین خوشه ­ها به تفصیل در بخش نتایج آورده شده است. با توجه به ویژگی­های هر خوشه، می­توان بیماران را بر اساس میزان تأثیرگذاری عوامل مختلف بر روی آن­ها دسته بندی کرد. نتیجه­ گیری: با توجه به نتایج حاصل از این مطالعه، مهم­ترین عوامل شناسایی شده با استفاده از خوشه ­بندی عبارت بودند از: هموگلوبین، سن، جنسیت، مصرف سیگار، مصرف الکل و کراتینین. همچنین با توجه به قوانین با هم­ آیی، بیشترین ارتباط بین سرفه­، کاهش وزن و سرعت رسوب گلبول­های قرمز یافت شده است. 2 Introduction: According to the World Health Organization, TB is the largest cause of death among infectious diseases. Due to the high percentage of tuberculosis infection and the high number of death among these patients, this study was carried out to categorized and find the relationship between different clinical and demographical characteristics. Method: This descriptive analytical study was done on 600 patients from Masih Daneshvari hospital tuberculosis research center. K-means clustering, Apriori association rules, and data mining algorithms (SPSS Clementine software) were used for clustering and determining the common characteristics among patients. Results: Based on DUNN index, 3 clusters were chosen as optimal cluster. The common factors between clusters have been described in details in findings section. According to the characteristics of each cluster, patients can be classified based on the effectiveness of various factors Conclusion: According to the results of this study, the most important identified factors by the use of clustering are Hemoglobin, age, sex, smoking, alcohol and Creatinine. Based on the association rules the highest rate of relationship is found between cough, weight loss, and ESR. 149 159 2015/02/172015/10/242015/10/14 1394/7/22 2015/09/162015/11/142015/11/16 1394/8/25 فرزاد فیروزی جهانتیغ Farzad Firuzi Jahantigh دکتری مهندسی صنایع، استادیار،گروه مهندسی صنایع، دانشگاه سیستان و بلوچستان، زاهدان، ایران. ایران firouzi@eng.usb.ac.ir حکیمه عامری Hakimeh Ameri کارشناس ارشد فناوری اطلاعات، تجارت الکترونیک، دانشکده مهندسی صنایع، دانشگاه خواجه نصیرالدین طوسی تهران، تهران، ایران. ایران ha.amery@gmail.com Tuberculosis Clustering Association rules Data mining سل خوشه بندی قوانین باهم آیی داده کاوی 1. Rusdah U, Winarko E. Review on data mining methods for tuberculosis diagnosis. Information Systems International Conference (ISICO); 2013 Dec 2‌–‌4; Surabaya, Indonesia: Institut Teknologi Sepuluh Nopember (ITS); 2013.##2. Nasehi M, Mirhaghani L. National Guidelines TB. 2th ed. Tehran: Andishmand; 2008. Persian.##3. Ameri H, Alizadeh S, Barzegari A. Knowledge Extraction of diabetics' data by decision tree method. J Health Adm. 2013; 16‌(53):58-72. Persian. ##4. Al Jarullah AA. Decision tree discovery for the diagnosis of type II diabetes. Innovations in Information Technology (IIT), 2011 International Conference on; 2011 Apr 25-27; Abu Dhabi: IEEE; 2011.p. 303-7.##5. Khajehei M, Etemady F. Data mining and medical research studies. Computational Intelligence, Modelling and Simulation (CIMSiM). 2th International Conference on; 2010 Sep 28-30; Bali: IEEE; 2010. p. 119 –22. ##6. Jayalakshmi T, Santhakumaran A. A novel classification method for diagnosis of diabetes mellitus using artificial neural networks. Data Storage and Data Engineering (DSDE), 2010 International Conference on; 2010 Feb 9-10; Bangalore: IEEE; 2010. p. 159-63.##7. Ameri H, Alizadeh S, Barzegari A. Identification of influencing factors for heart attack in diabetic patients using C & R algorithm. Daneshvar Medicine. 2014; 21(112):1-13. Persian.##8. Sharifkhani M, Alizadeh S, Abbasi M, Ameri H. Providing a model for predicting the risk of osteoporosis using decision tree algorithms. J Mazandaran Univ Med Sci. 2014; 24‌(116):110-18. Persian.##9. Uçar T, Karahoca A, Karahoca D. Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Computing and Applications. 2013; 23(2): 471-83.##10. Nagabhushanam D‌, Naresh N, Raghunath A, Praveen Kumar K. Prediction of Tuberculosis Using Data Mining Techniques on Indian Patient’s Data National Conference on Research Issues and Recent Trends in Computer Science & Information Technology (NCRRCSIT 2013), At Sir C R Reddy College of Engineering; 2013 Oct – Dec 4; Eluru, AP, India; 2013.##11. Uçar T, Karahoca A. Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches. Procedia Computer Science. 2011; 3:1404-11. ##12. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 2th ed. San Francisco: Morgan Kaufmann; 2006.##13. Chen G, Astebro TB. How to deal with missing categorical data: test of a simple Bayesian method. Organizational Research Methods. 2003; 6(3):‌309-27.##14. Witten I‌H, Frank E, Hall MA. Data Mining: Practical machine learning tools and techniques. 3th ed. USA: Morgan Kaufmann; 2011.##15. Tahmasbi H, Amoozgar M, Adine H. Replacement of missing values and its effect on the classification accuracy in medical data mining. Journal of Health and Biomedical Informatics. 2015; 2‌(1):24-32. Persian.##16. Burris CA, Ashwood ER, Burns DE. Tietz textbook of clinical chemistry and molecular diagnostics. 4th ed. Louis: Elsevier Saunders; 2006.##17. Mc Pherson RA, Mattew R, Princus MR. Henry’s clinical diagnosis and management by laboratory methods. 22th ed. Philadelphia: Elsevier Saunders; 2011.##18. Lujambio I, Sottolano M, Luzardo L, Robaina S, Krul N, Thijs L, et al. Estimation of glomerular filtration rate based on serum cystatin C versus creatinine in a uruguayan population. Int J Nephrol. 2014; 2014:837106.##19. Alizadeh S, Ghazanfari M, Teimorpour B. Data Mining and Knowledge Discovery. 2th ed. Tehran: Iran University of Science and Technology‌; 2011. Persian.##20. Alizadeh S, Malekmahmodi S. Data mining & knowledge discovery step by step clementine. Tehran: Khaje Nasir Toosi University of Technology; 2011. Persian.##21. Asha T, Natarajan S, Murthy KN. A data mining approach to the diagnosis of tuberculosis by cascading clustering and classification. Journal of Computing. 2011; 3(4):1-8.##22. Bakar AA, Febriyani F. Rough neural network model for tuberculosis patient categorization. Proceedings of the International Conference on Electrical Engineering and Informatics; 2007 Jun 17-19; Indonesia: Institut Teknologi Bandung; 2007.p. 765–8.##23. Abdallah TM, Ali AA. Epidemiology of tuberculosis in Eastern Sudan. Asian Pac J Trop Biomed. 2012;2(12):999-1001.#### ##

رضایت‌مندی مدیران و کارشناسان دانشکده‌ علوم پزشکی آبادان در توسعه بهره‌مندی از کامپیوتر و اینترنت Access and Satisfaction of Managers and Experts of Abadan Medical School in Capacity Utilization from Computer and the Internet 1 1 مقدمه: بهره­ مندی از ­فن­آوری اطلاعاتی یکی از عوامل مهم توسعه و بهبود عملکرد سازمان­ها محسوب می­شود. این مطالعه با هدف تعیین سطح رضایت­مندی مدیران و کارشناسان دانشکده­ علوم پزشکی آبادان در توسعه بهره­ مندی از کامپیوتر و اینترنت انجام گرفت. روش: این مطالعه مقطعی در شهریورماه 1393 انجام گرفت. جامعه پژوهش مدیران و کارشناسان دانشکده علوم پزشکی آبادان بودند که به صورت سرشماری انتخاب شدند. ابزار جمع­ آوری داده­ ها پرسشنامه خود ساخته شامل متغیرهای فردی با 8 سؤال و متغیرهای وابسته با 15 سؤال در مقیاس دو گزینه­ ای کافمن و 5 رتبه ­ای لیکرت بود که روایی و پایایی آن مورد تأیید قرار گرفت. داده­های جمع­ آوری شده در نرم­ افزار SPSS  نسخه 18 در سطح معنی­ داری 0/05>P تحلیل شدند. نتایج: از بین 139 مدیر و کارشناس مطالعه شده، 102 نفر (75%) در اتاق کار خود به کامپیوتر سالم و 77 نفر(75/5%) به اینترنت دسترسی داشتند که سطح رضایت از سرعت اینترنت از 5 نمره، 1/1±2/6 و از کیفیت برخورداری 1/0±2/7 بود. 57 نفر(41%) به شبکه اتوماسیون متصل بودند که سطح رضایت از سرعت اتوماسیون، 1/0±3/2، از کیفیت برخورداری 0/8±3/2، عدم پیچیدگی انجام کار 0/9±3/1، امنیت محرمانه بودن اطلاعات 1/1±3/3، قابلیت سیستم کامپیوتر مورد استفاده 0/8±2/7 بود. بین سطح تحصیلات و سطح رضایت از سرعت اینترنت، کیفیت برخورداری از اینترنت و سرعت اتوماسیون رابطه معنی­ دار وجود داشت (0/05>P). نتیجه­ گیری: نتایج این مطالعه نشان داد که میزان رضایت بهره ­مندی از کامپیوتر و اینترنت در سطح متوسط بود، پیشنهاد می­ گردد واحد IT دانشکده نسبت به توسعه بیشتر بهره­ مندی از کامپیوتر و اینترنت برنامه­ های مداخله­ ای طراحی و اجرا نماید. 2 Introduction: Information technology (IT) utilization  is one of the important factors of improving organizational performance. This study aimed to determine the access and satisfaction level of managers and  experts of Abadan Medical School incapacity utilization of computer and the internet. Method: This cross-sectional study was conducted in 2014. Community of this research was  all managers and  experts of Abadan Medical School that was chosen by census. The data collection tool was a researcher-made questionnaire containing demographics (8 questions) and dependent variables (15 questions) the validity and reliability of which was confirmed. Data was analyzed by SPSS version 18 (P<0.05). Results: From the 139 managers and experts who were studied, 102 persons (75%) had computers in their offices and 77 persons (75/5%). had access to internet that satisfaction level of internet speed and quality of enjoyment was 2/6±1/1, 2/7±1/0. 57 persons (41%) were connected to the automation network that satisfaction level of automation speed, quality of enjoyment, lack of complexity, confidentiality of information security, and the capability of computer was 3/2±1/0, 3/2±0/8, 3/1±0/9, 3/3±1/1, and 2/7±0/8. There was a significant relationship between education level and satisfaction level of internet speed, quality of enjoyment, and automation speed (P>0.05). Conclusion: The results of this study showed that access and satisfaction level were intermediate. It is suggested that designing and implementing intervention programs are necessary for capacity utilization of the computer and the internet by the IT department. 160 167 2015/02/172015/10/242015/10/142015/10/10 1394/7/18 2015/09/162015/11/142015/11/162015/12/8 1394/9/17 سیدداود نصرالله پور شیروانی Seyed Davoud Nasrollahpour Shirvani دکتری مدیریت خدمات بهداشتی درمانی، استادیار، مرکز عوامل اجتماعی مؤثر بر سلامت، پژوهشکده سلامت، دانشگاه علوم پزشکی بابل، بابل، ایران. ایران dnshirvani@gmail.com محمداسماعیل مطلق Mohammad Emaeil Motlagh متخصص بیماری‌های کودکان، استاد، دانشکده پزشکی، دانشگاه علوم پزشکی جندی شاپور اهواز، اهواز، ایران. ایران motlagh@health.gov.ir Information Technology Computer Internet Automation Abadan Medical School فن‌آوری اطلاعات کامپیوتر اینترنت اتوماسیون دانشکده‌ علوم پزشکی آبادان 1. Nasrollahpour Shirvani SD, Maleki MR, Motlagh ME, Kavosi Z, Tofighi S, Gohari MR. Benchmarking records in the health departments of the Universities of Medical Sciences of Iran in the years 2008-2010. Research Journal of Medical Sciences (Medwell Journals). 2011; 5(3): 161-5.##2. Campbell B, Kay R, Avison D. Strategic alignment: a practitioner's perspective. Journal of Enterprise Information Management. 2005; 18(6): 653-64.##3. Hejazi SM, Movahedi F. Evaluating Educational portals in public health. Health Inf Manage. 2007; 4(1): 81-8. Persian. ##4. Garrison DR, Anderson T. E-Learning in the 21 st Century: A Framework for Research and Practice. London: Routledge; 2003.##5. Mohammadi Javid N, Ziapour A, Esfandnia A, Kianipour N, Rajabi Gilan N, Ghasemi SR. The study of the effectiveness of office automation deployment in the administrative staff’s productivity (a case study of the employees of the medical school of Kermanshah-Iran). Technical Journal of Engineering and Applied Sciences (TJEAS). 2015; 5‌(1): 32-41. ##6. Du L, Jiang Z. Analysis and design of library office automation system based on workflow. Advanced Materials Research. 2014: 889-890:1301-5.##7. Bates DW, Bitton A. The future of health information technology in the patient-centered medical home. Health Aff (Millwood). 2010; 29(4): 614-21.##8. Ghaderi Nansa L, Piri Z, Salmani E, Gholipour H, Sharghi R. Evaluation of Hospital information systems in University hospitals of Tabriz University of Medical Sciences, Iran: Nursesn Perspectives. Health Inf Manage. 2013; 10(2): 190-200. Persian. ##9. Luftman JN, Lewis PR, Oldach SH. Transforming the enterprise: The alignment of business and information technology strategies. IBM Systems Journal. 1993; 32(1): 198-221.##10. Kwei R. Aligning business and IT strategy. Health Manag Technol. 1998; 19(2): 72-4.##11. Poon EG, Blumenthal D, Jaggi T, Honour MM, Bates DW, Kaushal R. Overcoming barriers to adopting and implementing computerized physician order entry systems in U.S. hospitals. Health Aff (Millwood). 2004; 23(4):184-90.##12. Amatayakul M. EHR? Assess readiness first. Healthc Financ Manage. 2005; 59(5): 112-3.##13. Lippert S, Kverneland A. The danish national health informatics strategy. Stud Health Technol Inform. 2003; 95: 845-50.##14. Mohagegzadeh MS, Abdolahi M. Survey of review and how to use the member center of the Internet University of Medical Sciences Shiraz from feasibilities of center and its impact on jobs Research this Member. Information Sciences & Technology. 2002; 18(1-2): 1-11. Persian. ##15. Hajforosh A, Orangi A. Survey of results of application of information and communication technology in high schools in Tehran. Review Quarterly Journal of Educational Innovations. 2004; 3(9): 11-32. Persian. ##16. Hasanpour Dehkordi A. Knowlege With Internet and rate to use it in the care of patients by nursing staff. Journal of Gorgan Bouyeh Faculty of Nursing & Midwifery. 2006; 3(1): 36-42. Persian. ##17. Motlagh E, Nasrollahpour Shirvani SD, Maleki MR, Tofighi Sh, Kabir MJ, Jaferi N. Access to infrastructures of information technology by the managers and executive experts in the Vice-chancellery for health of Iranian Universities of Medical Sciences. Health Information Management 2012; 9(2): 1-9. Persian. ##18. Amiri M, Raei M, Sadeghi E, Naderi S. The Effect of automation on Performance and work processes in Shahroud University of Medical Sciences. Journal of Health and Biomedical Informatics. 2014; 1(1): 10-8. Persian. ##19. Kenny C. Information, comunications and poverty, information in Frastucture Economist. Word Bank. 2003; 3(1): 2-4.##20. Bello IS, Arogundade FA, Sanusi AA, Ezeoma IT, Abioye-Kuteyi EA, Akinsola A. Knowledge and utilization of Information Technology among health care professionals and students in Ile-Ife, Nigeria: a case study of a university teaching hospital. J Med Internet Res. 2004; 6(4): e45.##21. Hier DB, Rothschild A, LeMaistre A, Keeler J. Differing faculty and house staff acceptance of an electronic health record one year after implementation. Stud Health Technol Inform. 2004; 107(Pt 2):1300-3.##22. Jebraeily H, Ahmadi M, Hajavi A, Gohari MR, Sedghi Jahromi M, Zareh Fazlolahi Z. Electronic Health Records: Personnel Readiness Assessment. J Health Adm. 2010; 13(39): 17-24. Persian. ##23. Rashidtorabi M, Ahanchian MR, Saeedi Rezvani M. The attitude of department heads, faculty members, and physicians of Mashhad University of Medical Sciences toward continuing medical education through internet: Investigating some related factors. Iran J Med Educ. 2007; 7(2): 279-87. ##24. Hayavi Haghighi MH, Dehghani M, Hosseni Teshizi S, Asgari B, Reisi M. Attitudes of medical record department staff in hospitals affiliated to Hormozgan university of medical sciences about the importance of hospital information system and its implementation prerequisites and selection criteria. Health Inf Manage. 2012; 9(4):471-8. Persian. ##25. Imani E, Khademi Z, Yusefi P, Bahrami Z, Naghizadeh F. Experiences of nursing managers about hospital information system: a qualitative study. J Hormozgan Univ Med Sci. 2012;‌16(3):‌223-32. Persian. ##26. Mehraeen E, Ayatollahi H, Ahmadi M. A study of information security in Hospital information systems. Health Inf Manage. 2014; 10(6): 779-88. Persian. ## ##

خطاهای موجود در مستندسازی گواهی فوت: یک مطالعه موردی Errors in the Documentation of the Death Certificate: A Case Study 1 1 مقدمه: آمار مرگ و میر، حاصل اطلاعات ثبت شده در گواهی­ های فوت می­ باشد. این اطلاعات جهت اهداف ارزشمند از جمله توسعه برنامه ­های بهداشت عمومی و اختصاص منابع مراقبت سلامت به کار می­ رود. بنابراین صحت داده­ های گواهی فوت اهمیت زیادی دارد. لذا این پژوهش، با هدف بررسی خطاهای موجود در تکمیل گواهی فوت در بیمارستان ­های شهر سبزوار انجام گردید. روش: این پژوهش از نوع گذشته­ نگر و  توصیفی بود و کلیه پرونده ­های بیماران فوت شده در بیمارستان­های شهر سبزوار در شش ماهه دوم سال 1389 (309 پرونده) بررسی شد. در این پژوهش، جمع آوری داده­ ها بر اساس چک لیستی محقق ساخته صورت گرفت .تحلیل داده ­ها با استفاده از نرم افزار SPSS نسخه 16 صورت پذیرفت. نتایج: نتایج نشان داد که در مجموع تنها 11/6 درصد گواهی­ های فوت مورد بررسی، بدون خطا بودند. در ثبت نام و نام خانوادگی متوفی هیچ خطایی مشاهده نشد. بیشترین نقص اطلاعاتی در ثبت شماره ملی (78/5 درصد) و ثبت شماره شناسنامه (26/2 درصد) دیده شد. 60 درصد گواهی­ ها توسط پزشک معالج متوفی صادر نشده بود و 38 درصد از صادرکنندگان گواهی­ ها پزشکان عمومی بودند. ثبت مکانیسم فوت به جای علت زمینه ­ای فوت در 20/5 درصد گواهی­ ها مشاهده شد. رابطه علیّتی بین علل ثبت شده در 27 درصد از گواهی­ ها، وجود نداشت و در ثبت علل فوت در  68 درصد گواهی­ ها از اختصارات استفاده شده بود. نتیجه ­گیری: در این بررسی توالی نامناسب علل فوت و به کارگیری اختصارات در ثبت علت فوت بیشتر مشهود است که ناشی از عدم درک اهمیت گواهی فوت و ناآشنایی پزشکان با قوانین و دستوالعمل های تعیین علل مرگ است و می­ تواند دلیل بروز خطاهای گواهی فوت باشد. برگزاری دوره­ های آموزش سالانه در مورد گواهی فوت و بحث درباره گواهی فوت هر بیمار، در ویزیت­ های پزشکان می ­تواند منجر به افزایش صحت این اطلاعات گردد. 2 Introduction: Mortality statistics are derived from the information recorded on death certificates. This information is used for many important purposes, such as development of public health programs and allocation of health care resources. Therefore, the accuracy of death certificate data is very important. The aim of this study was to evaluate the quality of documentation of death certificates in hospitals in Sabzevar, Iran. Method: In this retrospective and applied study, all death certificates of patients admitted and expired in hospitals (30 death) of Sabzevar, Iran during the second six months in 2010 were evaluated. Data was collected by a checklist Which validity was approved by experts. Obtained data analyzed using SPSS software and descriptive statistics. Results: The result shows that out of all death certificates reviewed, Only 11,6 percent of certificates were found written without error. Most frequent errors pertained to writing patients' social security number (78.5%) and national ID card (26.2%). Around 60% of death certificates were not issued by treating doctor of deceased and 38% of them were issued by a general practitioner (GP). Listing 'mechanism of death' at the place of 'underlying cause of death' was found in 20, 5 %of certificates. There was no causal relationship between the recorded causes in 27% of certificates. 68% of certificates disclosed 'use of abbreviations' in cause of death. Conclusion: In this study indicates improper sequence of causes and use of abbreviations in the recorded causes of death, due to the lack of understanding about the importance of the death certificate and unfamiliarity of physicians with rules and guidelines of determining mortality causes. Some annual courses in death certification and discussion of the death certificate for each deceased patient in physician, can increase accuracy of the documents. 168 175 2015/02/172015/10/242015/10/142015/10/102015/11/28 1394/9/7 2015/09/162015/11/142015/11/162015/12/82015/12/19 1394/9/28 مرضیه معراجی Marziyhe Meraji . دانشگاه علوم پزشکی مشهد، مشهد، ایران ، .دکترای مدیریت اطلاعات بهداشتی- درمانی، گروه مدارک پزشکی و فناوری اطلاعات سلامت، دانشکده علوم پیراپزشکی ایران merajim1@muma.ac.ir مریم برآبادی Maryam Barabadi . معاونت درمان، دانشگاه علوم پزشکی سبزوار، سبزوار، ایران ، اداره آمار و مدارک پزشکی، .کارشناس ارشد مدارک پزشکی ایران mahbarabadi@yahoo.com Documentation errors Causes of death Death certificate مستندسازی علل فوت گواهی فوت 1. Siqueira AA, Rio EM, Tanaka AC, Schor N, Alvarenga AT, Almeida LC. Female mortality in the Municipality of São Paulo: quality of medical death certificates. Rev Saude Publica. 1999;33(5):499-504. ##2. Lu TH, Lee MC, Chou MC. Accuracy of cause-of-death coding in Taiwan: types of miscoding and effects on mortality statistics. Int J Epidemiol. 2000;29(2):336-43.##3. Sibai AM. Mortality certification and cause-of-death reporting in developing countries. Bull World Health Organ. 2004 Feb;82(2):83.##4. Villar J, Pérez-Méndez L. Evaluating an educational intervention to improve the accuracy of death certification among trainees from various specialties. BMC Health Serv Res. 2007;7:183. ##5. National Center for Health Statistics (NCHS) Mortality Data International Classification of Disease; 2007. [cited 2013 Aug 24] Available from: http:// www.cdc.gov.mill1.sjlibrary.org/nchs/about/major/dvs/icd10des.htm ##6. World Health Organization (WHO). International statistical classification of diseases and health related problems. 2th ed. Geneva: World Health Organization; 2004.##7. Tangcharoensathien V, Faramnuayphol P, Teokul W, Bundhamcharoen K, Wibulpholprasert S. A critical assessment of mortality statistics in Thailand: potential for improvements. Bull World Health Organ. 2006;84(3):233-8. ##8. Rezvani Ardestani SF, Shahnavazi H, Ghorbani M. Survey on the accuracy of death certificated issued with WHO Standards & ICD10 Mortality codes in Sina hospital in Tehran in the second half of 2013. Scientific Journal of Forensic Medicine. 2015; 21(2):106-9. Persian ##9. Naghavei M. Broadcasting death in eighteen provinces in 2000. Tehran: Tandis; 2003. Persian.##10. Nakhaee N, GhadiPasha M. Instructions for death certificate completion. Kerman: Kerman University of Medical Sciences; 2009. ##11. Nojilana B, Groenewald P, Bradshaw D, Reagon G. Quality of cause of death certification at an academic hospital in Cape Town, South Africa. S Afr Med J. 2009 Sep;99(9):648-52. ##12. Pritt BS, Hardin NJ, Richmond JA, Shapiro SL. Death certification errors at an academic institution. Arch Pathol Lab Med. 2005;129(11):1476-9. ##13. Myers KA, Farquhar DR. Improving the accuracy of death certification. Canadian Medical Association Journal. 1998;158(10):1317-23. ##14. Jafari n, Naghavi M. Instructions of physician for record and classification of causes of death. Tehran:Arvij Publication.2006. Persian.##15. Ariaei M, Dortag E, Naderi N, Ebrahimi R. Consistency between death certificates and hospital records: a study from Kerman University of Medical Sciences. Payesh. 2011;2 (10): 167-73. Persian.##16. Keyvanara M, Zardoeigolanbar S, Karimi S, Saghaeiannejad Isfahani S. The Quality of Death Certificates Record in the Educational and non-Educational Hospitals in Kermanshah. Health Info Manage. 2011; 8(1): 23-32. Persian.##17. Razavi SM, Reiazi M, Akbari H, Dabiran S. Study of failure of the death certificate issued by the Shariati Hospital in Tehran and its effective factors. Journal of Medical Council of Islamic Republic of Iran 2002; 20(2): 89-94. Persian ##18. Health Information Systems Knowledge Hub. School of Population Health, Handbook for doctors on cause-of-death certification. The University of Queensland; 2012 Available from: http://www.uq.edu.au/hishub/docs/Handbook/HISHUB-Handbook-for-doctors.pdf##19. Khosravi A, Rao C, Naghavi M, Taylor R, Jafari N, Lopez AD. Impact of misclassification on measures of cardiovascular disease mortality in the Islamic Republic of Iran: a cross-sectional study. Bull World Health Organ. 2008;86(9):688-96. ##20. Sadeghijebeli M, Afariche H, Mojde S, Saghaeiannejad S, Mehrabi A. Survey on consistency surface between the recorded causes of death in death certificate and the recorded causes of death in medical record for cardiovascular patients in the educational hospitals of Isfahan. Scientific Journal of Forensic Medicine. 2010; 16‌(1):21-7. ##21. Mohsenisaravi B, Kabirzadeh A, Bagherianfarahabadi E, Zamanikiasari A. Expression of data doumentaion on medical death certificate: accordence WHO role in Mazandaran University of Medical Sciences. Health Info Manage. 2010; 7(1):7-15. Persian.##22. Lamoochi P, Aalipour E, Mir Behrisi H. Correlation causes of death in death certificates of Ahvaz Aboozar hospital in 2009 and March to September 2010. Jentashapir. 2013; 4(3):271-6. Persian. ##23. Einafshar B. Review how mortality statistics presented in Tehran hospitals [dissertation]. Tehran: Iran University of Medical Sciences; 2000. Persian.##24. Hayavi Haghighi M, Khorami F, Alipour J, Dehghani M. The impact of errors in completing death certificat e on coding, underlying the cause of death in Shahid Mohammadi hospital of Bandar Abbas. J Health Adm. 2013; 15‌(50):76-84. Persian.## ##

عوامل مؤثر بر پذیرش مدارک پزشکی الکترونیکی براساس تئوری‌های پذیرش فناوری اطلاعات: یک مطالعه مروری The Factors Affecting the Adoption of Electronic Medical Record based on Information Technology Adoption Theories: A Review Article 1 1 مقدمه: مدارک پزشکی الکترونیکی، یکی از فناوری­های نوین برای بهره­برداری از اطلاعات سلامت است که به عنوان سیستمی با ارزش جهت دسترسی به اطلاعات بیمار در بیمارستان­ها شناخته شده است و برای اجرای موفق آن مانند هر سیستم دیگری باید در ابتدا فاکتورهای مؤثر بر پذیرش و استفاده از آن را سنجید. هدف از این مطالعه بررسی نظام­ مند مقالات منتشر شده درباره عوامل مؤثر بر پذیرش مدارک پزشکی الکترونیکی براساس تئوری­های پذیرش فناوری اطلاعات و شناسایی و دسته­ بندی عوامل مؤثر بر پذیرش این سیستم می­باشد. روش: این مطالعه از نوع مطالعات مروری نظام­مند است که با جستجو در پایگاه­های اطلاعاتی معتبر نظیر، Google Scholar، Emerald، Science Direct، SID ،Mag­Iran ،­Iran Medex ، Pub­Med­، که در محدوده­ زمانی  2004 تا 2014 انجام گردید، اطلاعات لازم از مقالات نیز در یک چک لیست جمع ­آوری گردیدند. نتایج: در مطالعاتی که از تئوری­های فناوری اطلاعات جهت بررسی عوامل مؤثر بر پذیرش مدارک پزشکی الکترونیکی استفاده نموده بودند، مدل TAM بیشتر از سایر مدل­ها مورد استفاده قرار گرفته است و همچنین عواملی نظیر نگرش نسبت به آسانی استفاده و نگرش نسبت به سودمندی، استفاده از مدل TAM و تأثیرات اجتماعی از مدل UTAUT بر پذیرش مدارک پزشکی الکترونیکی مؤثر می­باشند. نتیجه ­گیری: با توجه به این که نگرش به آسانی استفاده، نگرش نسبت به سودمندی استفاده و تأثیرات اجتماعی از مهم­ترین عوامل مؤثر بر پذیرش مدارک پزشکی الکترونیکی می­ باشند، لذا پیشنهاد می­شود در برنامه­ ریزی در جهت اجرای آن مورد توجه قرار گیرند. 2 Introduction:  Electronic Medical Records, which is a valuable system to access the patients information at the hospital, is one of the innovative technologies for the utilization of health information. For its successful implementation, this system should be assessed regarding the factors affecting its adoption and use. The purpose of this study was to review the published articles on the factors influencing the adoption of electronic medical records systematically and according to Information Technology Adoption Theories and to identify and classify the factors affecting the adoption of this system. Method: The present study is a systematic review survey. Data were collected by searching strategy in valid databases such as Google Scholar, Emerald, Science Direct, SID, MagIran, IranMedex, Pubmed from  2004 to 2014, and then the data were recorded on checklist. Results: Among the studies that use information technology theories to survey the factors affecting adoption of electronic medical record, TAM model was used more than other models. Factors such as perceived ease of use, perceived usefulness, and social influence of TUATU are the most effective on adoption of electronic medical records. Conclusion: The results of the study showed, perceived ease of use, perceived usefulness, and social influence are effective factors in adopting EMR. Consequently these factors are recommended to be considered in planning to run systems. 176 183 2015/02/172015/10/242015/10/142015/10/102015/11/282015/02/17 1393/11/28 2015/09/162015/11/142015/11/162015/12/82015/12/192015/04/30 1394/2/10 محترم نعمت اللهی Mohtaram Nematollahi دکتری تخصصی مدیریت اطلاعات سلامت، استادیار ،گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی شیراز، شیراز، ایران. ایران علی گراوند Ali Garavand کارشناسی ارشد فناوری اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی شیراز‌، شیراز، ایران. ایران Virya67@yahoo.com حسین منعم Hossein Monem دکتری تخصصی سیستم‌های اطلاعاتی، استادیار، گروه فناوری اطلاعات، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شیراز، شیراز، ایران. ایران Electronic Medical Records Technology Acceptance Models Ease of Use Usefulness Social Influence مدارک پزشکی الکترونیکی‌ تئوری‌های پذیرش فناوری اطلاعات آسانی استفاده سودمندی تأثیرات اجتماعی 1. Torabi M, Safdari R. Electronic health record. Tehran: Jafari; 2009.##2. Toyoda K. Standardization and security for the EMR. Int J Med Inform. 1998;48(1-3):57-60.##3. Wang SJ, Middleton B, Prosser LA, Bardon CG, Spurr CD, Carchidi PJ, A cost-benefit analysis of electronic medical records in primary care. Am J Med. 2003;114(5):397-403.##4. Mills TR, Vavroch J, Bahensky JA, Ward MM.Electronic medical record systems in critical access hospitals: leadership perspectives on anticipated and realized benefits. Perspect Health Inf Manag. 2010;7:1.##5. Block BM. How we improved our practice and bottom line with a new EMR system. Fam Pract Manag 2008;15(7):25-30. ##6. Garavand A, Nematollahi M. Monem H. The role of electronic medical records (EMR) in reducing Hospital costs. International Conference on Sustainable Reform in Health Systems; 2015 Feb 24-25. Esfahan: Esfahan University of Medical Sciences; 2015.##7. Follen M, Castaneda R, Mikelson M, Johnson D, Wilson A, Higuchi K. Implementing health information technology to improve the process of health care delivery: a case study. Dis Manag. 2007;10(4):208-15.##8. Khalifa M. Barriers to health information systems and electronic medical records implementation. a field study of Saudi Arabian Hospitals. Procedia Computer Science. 2013;21:335-42.##9. Kijsanayotin B, Pannarunothai S, Speedie SM. Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model. Int J Med Inform. 2009;78(6):404-16.##10. Tsiknakis M, Kouroubali A. Organizational factors affecting successful adoption of innovative eHealth services: a case study employing the FITT framework. Int J Med Inform. 2009;78(1):39-52. ##11. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010;43(1):159-72.##12. EL-Gayar OF, Wills M, Bennett D. Examining healthcare professionals' acceptance of electronic medical records using utaut. Issues in Information Systems 2008;5(2):396-401.##13. Amatayakul M. EHR Assess readiness first. Healthcare financial management: Journal of the Healthcare Financial Management Association. 2005;59(5):112-3.##14. Miller RH, Sim I. Physicians' use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004;23(2):116-26.##15. Yaghobi N, Shakeri R. Analytical comparison of technology acceptance models with emphasis on internet Banking adoption. Iranian Journal of Management Sciences. 2008;3(11):21-44. Persian.##16. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13(3):319-40.##17. Gupta B, Dasgupta S, Gupta A. Adoption of ICT in a government organization in a developing country: an empirical study. The Journal of Strategic Information Systems 2008;17(2):140-54.##18. Chau P, Hu PJ. Information technology acceptance by individual professionals: a model comparison approach. Decision Sciences. 2001;32(4):699-719.##19. Rogers EM. Diffusion of innovation. 5th ed. New York: Free Press; 2003.##20. Kahouei M, Babamohamadi H. Factors affecting information technology acceptance in clinical settings from Nurses' perspective‌. Payavard Salamat. 2013;7(4):262-77. Persian.##21. Haslina M, Sharifah M. Acceptance Model of Electronic Medical Record. J Adv Inform Manag Stud 2005;2:75–92. ##22. Bleich HL, Slack WV. Reflections on electronic medical records: when doctors will use them and when they will not. Int J Med Inform. 2010; 79(1):1-4.##23. Putzer GJ, Park Y. Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. Perspect Health Inf Manag. 2012;9.##24. Mekic E, Kürşad Ozlen M. Acceptance of smartphones by users in BiH through extended technology acceptance model. European Researcher. 2014;67(1-2):136-49.##25. Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in Hospitals. Int J Med Inform. 2009;78(2):115-26.##26. Venkatesh V, Thong JY, XU X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly. 2012;36(1):157-78.##27. Tavakoli N, Jahanbakhsh M, Shahin A, Mokhtari H, Rafiei M. Electronic medical record in central polyclinic of isfahan oil industry: a case study based on technology acceptance model. Acta Inform Med. 2013;21(1):23-5.##28. Abdekhoda M, Ahmadi M, Gohari M, Noruzi A. The effects of organizational contextual factors on physicians' attitude toward adoption of Electronic Medical Records. J Biomed Inform. 2015;53:174-9.##29. Garavand A, Ghanbari S, Ebrahimi S, Kafashi M, Ahmadzadeh F. The Effective factors in adopting picture archiving and communication system in Shiraz educational hospitals based on technology acceptance model. Journal of Health and Biomedical Informatics. 2015;‌1(2):‌76-82. Persian.##30. Holtz B, Krein S. Understanding Nurse perceptions of a newly implemented electronic medical record system. Journal of Technology in Human Services. 2011;29(4):247-62.##31. Sharifian R, Askarian F, Nematollahi M, Farhadi P. Factors influencing nurses acceptance of hospital information systems in Iran: application of the Unified Theory of Acceptance and Use Technology. Health Inf Manage. 2014; 43(3):‌23-8.##32. Wurster CJ, Lichtenstein BB, Hogeboom T. Strategic, political, and cultural aspects of IT implementation: improving the efficacy of an IT system in a large hospital. J Healthc Manag. 2009;54(3):191-206.##33. Ayers DJ, Menachemi N, Ramamonjiarivelo Z, Matthews M, Brooks RG. Adoption of electronic medical records: the role of network effects. Journal of Product & Brand Management. 2009;18(2):127–35.## ##

مطالعه تطبیقی سه استاندارد امنیت داده در نظام سلامت A Comparative Study of Three Standards of Data Security in Health Systems 1 1 مقدمه: سیستم‌های اطلاعاتی الکترونیک­، سهولت دسترسی و انتقال داده‌های ناشی از آن، اهمیت رعایت استانداردهای امنیت و حریم داده‌ها در این سیستم‌ها را دو چندان کرده ‌است. روش: ابتدا ترکیبات مختلف کلیدواژه‌ها مورد جسـتجو قرار گرفتند. از مقالات به دست آمده، تاریخچه استـانداردهای مورد استفاده در داده‌های حوزه سلامت، لزوم استفاده از این استانداردها و میزان کاربرد استانداردها در سطح دنیا مطالعه گردید. سپس پرکاربردترین و معتبرترین استانداردها انتخاب شدند و پس از مطالعه متن کامل استانداردهای انتخابی، خصوصیات مورد نظر هر استاندارد استخراج و چک لیست خصوصیات جزئی به دست آمد که با کمک آن سه استاندارد انتخابی با یکدیگر مقایسه و نقاط ضعف و قوت هر یک مورد بحث و بررسی قرار گرفت. نتایج: خصوصیات استانداردهای مورد بررسی در 8 گروه و 25 زیر گروه دسته‌بندی شدند. ISO-27799:2008  به همه ویژگی‌های گروه رمزنگاری توجه ‌کرده است. HIPAA به رمزگذاری در ذخیره‌سازی و کلید نامتقارن و PCI-DSS به رمزگذاری در ذخیره‌سازی، الگوریتم‌های Hash و کلید نامتقارن توجه داشته ‌است. امنیت سیستم‌ عامل را HIPAA به کار برده ‌بودند. به امنیت رادیو شناسه، DNS و تلفن همراه، فقط PCI-DSSfi  اشاره‌ و امنیت شبکه‌های بی­سیم را ISO-27799:2008 و PCI-DSS منظور ‌کرده بودند. نتیجه­ گیری: با توجه به شرایط، می‌توان از استانداردی که در زمینه موردنظر مناسب­تر است استفاده ‌کرد. برای سامانه‌ای که بر روی تلفن همراه یا PDAها استفاده می‌شودPCI-DSS و زمانی‌ که شبکه بی­سیم داریم ISO-27799:2008 یا PCI-DSS توصیه‌ می‌شود و یا برای امنیت سیستم عامل HIPAA مناسب می‌باشد. استاندارد ترکیبی که ویژگی‌های هر سه استاندارد مورد بررسی را دارا ‌باشد به عنوان امن‌ترین روش، مدنظر است. 2 Introduction: The increasing influence of ICT on health and changing information systems to electrical form makes using the information, data transmission, and also preparation printouts of information so easy that the importance of internal and external disclosure policy, data security, and confidentiality standards in these systems have been doubled. Method: At the beginning of research, all the combinations of key words were searched, then the history and importance of the health data security standards were studied. So the most prevalent and reliable standards were selected to perform the full text. For the next step the researchers extracted the properties which were used to be compared with the selected standards and finally the comparison was discussed. Results: PCI-DSS, HIPAA, and ISO-27799:2008 properties were classified in 8 groups and 25 subgroups.  ISO-27799:2008 was attended to all properties in Encryption group, but HIPAA was just attended to Encryption in storage, and asymmetric key, and PCI-DSS was considered on Encryption in storage, using Hash algorithm and use of asymmetric key. Operation system security was considered only in HIPAA. Only PCI-DSS standard considered RFID and DNS security and cell phone security, and PCI-DSS as well as ISO-27799:2008 considered wireless networks security. Conclusion: One can use the standard that is stronger in context. So, it is recommended to use PCI-DSS for cell phone or PDA systems, and ISO-27799:2008 or PCI-DSS for wireless networks. It is better for security in operation systems to use HIPAA. Combined standard with all these three standards aspects can be used as the safest approach. 184 194 2015/02/172015/10/242015/10/142015/10/102015/11/282015/02/172015/07/16 1394/4/25 2015/09/162015/11/142015/11/162015/12/82015/12/192015/04/302015/09/21 1394/6/30 حمید مقدسی Hamid Moghaddasi دکترای مدیریت اطلاعات سلامت، دانشیار ، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران. ایران moghaddasi@sbmu.ac.ir محمدمهدی قائمی Mohammad Mehdi Ghaemi دانشجوی دکتری انفورماتیک پزشکی‌، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران. ایران dr.mghaemi@gmail.com Local Cardiovascular Terms Coding Application ICD امنیت داده‌های سلامت استاندارد سیستم اطلاعات سلامت 1. Fichman RG, Kohli R, Krishnan R. The role of information systems in healthcare: current research and future trends. Information Systems Research. 2011;22(3):419-28.##2. Bauer J, Polakoff P. The growing importance of data in healthcare. Executive Briefing & Exchange. 2007;3(2).##3. Appari A, Johnson ME. Information security and Privacy in healthcare: current state of research. International Journal of Internet and Enterprise Management. 2010;6(4):279-314.##4. Haux R. Health information systems - past, present, future. Int J Med Inform. 2006;75(3-4):268-81.##5. Council of Europe. Training strategies for health. Information Systems: Council of Europe Publishing; 1996.##6. Huffman EK. Health Information Management. Berwyn, IL: Physicians' Record Company;1994.##7. Abdelhak M, Grostick S, Hanken MA, Jacobs E. Health information: management of a strategic resource: 4th ed. Saunders/Elsevier; 2011.##8. Richards-Kortum R. Biomedical engineering for global health. USA: Cambridge University Press; 2010.##9. Engstrom P. Technology outpaces privacy protection. Thursday; 2008. [cited 2013 Sep 3]. Available from: http://www.curetoday.com/publications/cure/2008/summer2008/Technology-Outpaces-Privacy-Protection##10. Houghton J. Information technology and the revolution in healthcare. 2002. [cited 2013 Sep 2]. http://www.eldis.org/vfile/upload/1/document/1201/Information%20Technology%20and%20Revolution%20in%20Healthcare.pdf##11. Nesbitt A, Kemeny C, Broshy E, Wurster T. Managing for a wired health care industry. IN VIVO: The Business and Medicine Report; 1996.##12. Olukunle O. Assessing innovative ICT for health information system in African rural communities. eldis; 2009.##13. Cotturri G, Inglese SA, Moro G, Roffiaen C, Scattolon C. European charter of patients’ rights. Rome: Active Citizenship Network; 2002.##14. Manson JE, Greenland P, LaCroix AZ, Stefanick ML, Mouton CP, Oberman A, et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. The New England Journal of Medicine. 2002;347(10):716-25.##15. The Office for Civil Rights of the U.S. Department of Health and Human Services. Protecting Your Privacy and Security [cited 2013 Sep 3]. Available from: http://www.healthit.gov/sites/default/files/pdf/health-information-privacy-and-security.pdf.##16. Your health information privacy: The Office for Civil Rights of the U.S. Department of Health and Human Services; 2013 [cited 2013 Sep 3]. Available from: http://www.hhs.gov/ocr/privacy/hipaa/understanding/consumers/consumer_rights.pdf.##17. Confidentiality and Privacy- Personal Health Information Toronto: College of Nurses of Ontario; 2009.##18. Protecting Your Privacy & Security Your Health Information Rights: The Office for Civil Rights of the U.S. Department of Health and Human Services; [cited 2013 Sep 3]. Available from: http://www.healthit.gov/patients-families/your-health-information-rights.##19. Hiller J, McMullen MS, Chumney WM, Baumer DL. Privacy and security in the implementation of health information technology (electronic health records): US and EU compared. BUJ Sci & Tech L. 2011;17:1.##20. Collst G, Duquenoy P, George C, Hedström K, Kimppa, K, Mordini E. ICT in medicine and health care: assessing social, ethical and legal issues. Social Informatics: An Information Society for all? In Remembrance of Rob Kling: US: Springer; 2006. ##21. Rudowski R. Impact of Information and Communication Technologies (ICT) on Health Care. [cited 2013 Sep 3]. Available from: http://www.map.uniroma2.it/digital_evolution/papers/rudowski_paper.pdf: 2008.##22. Chetley A, Davies J, Trude B, McConnell H, Ramirez R. Improving health, connecting people: the role of ICTs in the health sector of developing countries. This paper is part of a study commissioned by the info Dev program Grant no; 2006.1-65.##23. Wilson I, Mahdavy E. ICT in Healthcare better care, sustainable costs: Orange and European policy; 2013. [cited 2013 Sep 3]. Available from: http://www.orange.com/en/content/download/11116/241913/version/4/file/ICT+in+healtcare_Orange.pdf##24. World Health Organization (WHO). Patients' rights. [cited 2013]. Available from: http://www.who.int/genomics/public/patientrights/en/.##25. National Association of Citizens Advice Bureaux. NHS patients' rights. [cited 2013 Sep 3]. Available from: http://www.adviceguide.org.uk/england/healthcare_e/healthcare_nhs_healthcare_e/nhs_patients_rights.htm.##26. Wager KA, Lee FW, Glaser JP. Health care information systems: a practical approach for health care management. 3th ed. Wiley Jossey-Bass; 2103.##27. Fisher E. The Impact of Health Care Fraud on the United States Healthcare System. School of Public and Environmental Affairs. 2008; 2(4):1-4.##28. Staman J. Health Care Fraud and Abuse Laws Affecting Medicare and Medicaid: An Overview.: Congressional Research Service; 2010. [cited 2013 Sep 3]. Available from: https://www.fas.org/sgp/crs/misc/RS22743.pdf##29. AAOMS Committee on Healthcare & Advocacy. A Review of Healthcare Fraud and Abuse in America. 2011.##30. Kökoğlu F, Leventoğlu A, Erdemoglu AK. Dorsal Sural Nerve Conduction Studies in Patients with Early Diabetic Neuropathy and Its Relations with plasma adiponectin and hs-CRP Levels. Journal of Neurological Sciences (Turkish). 2009;26(4):404-15.##31. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43(4):333-8. ##32. StevensWS. Health Insurance: Current Issues and Background. USA: Nova Science; 2003.##33. Chaikind HR, Redhead S, HearneJ, Stone J, Like B, Franco C. The Health Insurance Portability and Accountability Act (HIPAA). Overview and Analyses: NewYork: Nova Science; 2004.##34. Rudman WJ, Eberhardt JS, Pierce W, Hart-Hester S. Healthcare fraud and abuse. Perspectives in health information management/AHIMA, American Health Information Management Association. Perspect Health Inf Manag. 2009; 6. ##35. Lind KD (AARP Public Policy Institute‌) Attacking Waste, Fraud, and Abuse in Health Reform. Washington DC; 2011. [cited 2013 Sep 3]. http://assets.aarp.org/rgcenter/ppi/health-care/fs186-fraud.pdf##36. Goldman TR, King KM, Sparrow MK, Agres, T, Dentzer S. Health Policy Brief. [cited 2013 Sep 3]. Available from: 2012.http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_72.pdf##37. American Health Information Management Association (AHIMA). A study of health care fraud and abuse: Implications for professionals managing health information; 2010. [cited 2013 Sep 3]. Available from: http://www.ahimafoundation.org/downloads/pdfs/Fraud%20and%20Abuse%20-%20final%2011-4-10.pdf##38. Rodrigues J. Health information systems: concepts, methodologies, tools and applications. 1th ed. USA: IGI Global; 2009.##39. Kabashiki IR. Bumps in the road to better health care: Xlibris Corporation; 2012.##40. Chetley A, Davies J, Trude B, McConnell H, Ramirez R, Shields T, et al. Improving Health, Connecting People: The Role of ICTs in the Health Sector of Developing Countries: InfoDev; 2006 [cited 2013 Sep 3] Available from: http://www.infodev.org/infodev-files/resource/InfodevDocuments_84.pdf##41. Tamas P, Voyiatzis AG, Anastasiadou D, Jovanovich Z. SWOT Analysis on the theme ICT for Health. Hungary: 2012. [cited 2013 Sep 3]. Available from: http://forsee.eu/documents/Montenegro_OC_FORSEE_SWOT_Analysis_eHealth_5p_v1_short_161.pdf##42. British Medical Association(BMA). Confidentiality and disclosure of health information tool kit. [cited 2013 Sep 3]. Available from: http://bma.org.uk/-/media/files/pdfs/practical%20advice%20at%20work/ethics/confidentialitytoolkit_full.pdf. ##43. Kelly T. Public interest disclosure policy and procedure. MiddleSex University; 2008.##44. Clayton PD, Boebert W, Defriese G, Dowell S, Fennell M, Frawley K, et al. For the record: protecting electronic health information Washington DC: National Academy Press;1997.##45. Disclosure Policy Canada: Export Development Canada; 2010 [cited 2013 Sep 3]. Available from: http://www.edc.ca/EN/AboutUs/Disclosure/Documents/disclosure-policy.pdf.##46. Government of Western Australia Department of Health. Information Access and Disclosure Policy. [cited 2013 Sep 3]. Available from: http://www.health.wa.gov.au/CircularsNew/attachments/632.pdf.##47. The Government of the Hong Kong Special Administrative Region. An overview of information security standards Hong Kong: infosec; 2008. [cited 2013 Sep 3]. Available from: http://www.infosec.gov.hk/english/technical/files/overview.pdf.##48. Thomson SM. A standard-based security model for health information systems [dissertation]. Nelson: Mandela Metropolitan University; 2008.##49. Quinn J. HL7: Who, What & What’s New. cited 2013 Sep 3]. Available from: https://www2.uef.fi/documents/1020024/1430597/JQ-2011-05-23_HL7_Finland__HL7_Who_What_and_What_s_New.pdf/527a359f-a337-4569-a29f-30af7564c5cf##50. California Department of Mental Health. EHR and PHR standards and requirements. [cited 2013 Sep 3]. Available from: http://www.countyofplumas.com/DocumentCenter/Home/View/3120 ##51. International Organization for Standardization. Health security; [cited 2013 Sep 3]. Available from: http://www.iso.org/iso/home/search.htm?qt=health+security&published=on&active_tab=standards&sort_by=rel.##52. PCI Security Standards Council. PCI-DSS History; 2010. [cited 2013 Sep 3]. Available from: https://www.pcisecuritystandards.org/organization_info/index.php.##53. Wikipedia. Payment Card Industry Data Security Standard [cited 2013 Sep 3]. Available from: http://en.wikipedia.org/wiki/Payment_Card_Industry_Data_Security_Standard.##54. PCI Security Standards Council. Payment Card Industry (PCI) Data Security Standard; 2010 [cited 2013 Sep 3]. Available from: https://www.pcisecuritystandards.org/documents/pci_dss_v2.pdf.##55. Waegemann CP. Confidentiality and Security for e-Health; 2003 [cited 2013 Sep 3]. Available from: https://www.itu.int/itudoc/itu-t/workshop/e-health/s5-05.pdf##56. HIPAA Administrative Simplification Statute and Rules Department of Health and Human Services Office for Civil Rights; 2006. [cited 2013 Sep 3]. Available from: http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/adminsimpregtext.pdf##57. European Committee for Standardization. CEN/TC 251, Technical Bodies [cited 2013 Sep 3]. Available from: www.cen.eu/CEN/Sectors/TechnicalCommitteesWorkshops/CENTechnicalCommittees/Pages/Standards.aspx?param=6232&title=CEN/TC+251.##58. Witting K, Moehrke J. Health Information Exchange: Enabling Document Sharing Using IHE Profiles; 2012 [cited 2013 Sep 3]. http://www.ihe.net/Technical_Framework/upload/IHE_ITI_White-Paper_Enabling-doc-sharing-through-IHE-Profiles_Rev1-0_2012-01-24.pdf.##59. Wikipedia. ASTM International [cited 2013 Sep 3]. Available from: http://en.wikipedia.org/wiki/astm_international.##60. ASTM. ASTM International Technical Committee E31 on Healthcare Informatics. [cited 2013 Sep 3]. Available from: http://www.astm.org/COMMIT/SUBCOMMIT/E3125.htm.##61. Lovorn J. ASTM E31 Security Standards. National Committee on Vital and Health Statistics; 2013.##62. ASTM International Standards Worldwide. ASTM International Technical Committee E31 on Healthcare Informatics; 1970. [cited 2013 Sep 3]. Available from: http://www.astm.org/e31mail.htm.##63. ASTM. ASTM Healthcare Informatics Standard. 2003.[cited 2013 Sep 3]. Available from: http://www.astm.org/SNEWS/JULY_2003/hi_jul03.htm##64. Amatayakul M. Managing Information Privacy & Security in Healthcare Setting Standards in Healthcare Information. 2007.##65. Kanai T. Home-based Elderly People Support Information System. JAHIS News; 1996.##66. Gikas C. A General Comparison of FISMA, HIPAA, ISO 27000 and PCI-DSS Standards. Information Security Journal: A Global Perspective. 2010;19(3):132-41.##67. Health informatics - Information security management in health using ISO/IEC 27002 (ISO 27799:2008). British Standard Institution; 2008.## ##

بررسی ویژگیهای ابزارهای سنجش سواد سلامت با رویکرد مبتنی بر کامپیوتر: یک مطالعه مروری Health Literacy Instruments for Computer-Based Applications: A Review Article 1 1 مقدمه: سواد سلامت به مفهوم مهارت­های شناختی و اجتماعی است، که انگیزه و توانایی افراد برای دسترسی، درک و استفاده از اطلاعات در راستای ارتقاء و حفظ سلامت را تعیین می­ کند. پرداختن به سواد سلامت در محیط­های مراقبت بهداشتی غنی از فناوری کنونی، نیاز شدیدی به ابزارهای کامپیوتری معتبر برای ارزیابی سواد اطلاعاتی دارد. بنابراین، مطالعه حاضر با هدف تعیین ویژگی­های ابزارهای سنجش سواد سلامت برای انطباق با کاربرد مبتنی بر کامپیوتر انجام شد.  روش: این مقاله نوعی مطالعه مروری- نقلی است، که منابع حاصل از جستجوی ترکیبی کلمات کلیدی مرتبط با ابزارهای سنجش سواد سلامت عمومی در پایگاه­های داده مرتبط را با اعمال معیار ورودی زبان انگلیسی در بر می‌گیرد. نتایج: نتایج بررسی ابزارهای سنجش سواد سلامت نشان داد که این ابزارها شامل دو استاندارد طلایی TOFHLA (به ویژه تست آزمایشی نسخه مبتنی بر کامپیوتر) و برآورد  REALM و همچنین ابزارهای ارزیابی جدیدترین علائم حیاتی (NVS) و معیار سواد سلامت الکترونیک (eHEALTH) هستند. علاوه بر این، ابزار روش شناسی­ های سؤال غربالگری سواد سلامت (HLSQMs) نیز برای سنجش سواد سلامت مورد استفاده قرار می‌گرفت. نتیجه ­گیری: هر یک از ابزارهای سنجش و غربالگری سواد سلامت از قابلیت­ ها و ویژگی­های مفید متفاوتی بر اساس زمینه مورد استفاده برخوردار هستند. بنابراین، پیشنهاد می­شود که ابزار سواد سلامتی از ترکیب سؤالات روا و پایای HLSQMs و eHEALS با هدف توسعه ابزار مبتنی بر کامپیوتر ایجاد شود، تا امکان غربالگری افرادی که به برنامه‌های کاربردی سواد سلامت الکترونیک دسترسی دارند، فراهم شود. 2 Introduction: Health literacy has been defined as the cognitive and social skills which determine the individuals' motivation and ability to gain access to, understand, and use information in ways which promote and maintain good health.  To address health literacy in today’s technology-rich health care environments, there is a strong need for validated computer-based tools to assess health literacy. Therefore, this study aimed to present an overview of specifications of current health literacy screening instruments to adapt for computer-based usages. Method: In this narrative review study, relevant English publications were examined. The selected resources resultedfrom Boolean search in the related databases using key words concerning the Health Literacy Instruments. Results: The results of the overview of the health literacy measurement tools showed that these instruments include the two gold standards TOFHLA (pilot testing of a computer-based version) and the Rapid Estimate of Adult Literacy in Medicine (REALM) as well as, Newest Vital Sign (NVS) and eHealth Literacy Scale (eHEALS). Additionally, the tool for Health Literacy Screening Question Methodologies (HLSQM) was applied to measure the health literacy. Conclusion: Current health literacy screening tools demonstrate different beneficial properties depending on the context of use. Therefore, it's recommended that a health literacy instrument be developedfrom the combination of validated and reliable HLSQM and eHEALS questions for the purpose of developing a computer-based instrument to screen individuals accessing eHealth applications. 195 203 2015/02/172015/10/242015/10/142015/10/102015/11/282015/02/172015/07/162015/05/27 1394/3/6 2015/09/162015/11/142015/11/162015/12/82015/12/192015/04/302015/09/212015/07/5 1394/4/14 مهناز صمدبیک Mahnaz Samadbeik دکترای تخصصی مدیریت اطلاعات بهداشتی، استادیار، گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی لرستان، خرم آباد، ایران ایران mahbeik@yahoo.com سعیده گراوند Saeede Garavand دانشجوی کارشناسی فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی لرستان، خرم آباد، ایران ایران soodabeh_garavand@yahoo.com مریم سهرابی زاده Maryam Sohrabi Zadeh دانشجوی کارشناسی فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی لرستان، خرم آباد، ایران ایران msohrabi_712@yahoo.com نسیم کوشکی Nasim Koshki دانشجوی کارشناسی فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی لرستان، خرم آباد، ایران ایران nasimkoosh@yahoo.com زینب محمدی Zeynab Mohammadi دانشجوی کارشناسی فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی لرستان، خرم آباد، ایران ایران wwwunesmohamadi@mailfa.org Characteristics Health Literacy Computer Screening tools سواد سلامت کامپیوتر ابزار غربالگری 1. Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. ##2. Rootman I. Literacy and health in Canada: is it really a problem? Can J Public Health. 2003;94(6):405-6.##3. Baker DW, Williams MV, Parker RM, Gazmararian JA, Nurss J. Development of a brief test to measure functional health literacy. Patient Educ Couns. 1999;38(1):33-42. ##4. Nielsen-Bohlman L, Panzer A, Kindig D. Health literacy: A prescription to end confusion. Washington, DC: Committee on Health Literacy, Board on Neuroscience and Behavioral Health. National Academies Press; 2004.##5. Baker DW, Wolf MS, Feinglass J, Thompson JA, Gazmararian JA, Huang J. Health literacy and mortality among elderly persons. Arch Intern Med. 2007;167(14):1503-9.##6. Weiss BD, Mays MZ, Martz W, Castro KM, DeWalt DA, Pignone MP, et al. Quick assessment of literacy in primary care: the newest vital sign. Ann Fam Med. 2005;3(6):514-22.##7. Herndon JB, Chaney M, Carden D. Health literacy and emergency department outcomes: a systematic review. Ann Emerg Med. 2011;57(4):334-45.##8. Baker DW. The meaning and the measure of health literacy. J Gen Intern Med. 2006;21(8):878-83. ##9. Gilmour JA, Scott SD, Huntington N. Nurses and Internet health information: a questionnaire survey. J Adv Nurs. 2008;61(1):19-28.##10. Parker R. Health literacy: a challenge for American patients and their health care providers. Health Promotion International. 2000;15(4):277-83.##11. Kahn JS, Aulakh V, Bosworth A. What it takes: characteristics of the ideal personal health record. Health Aff (Millwood). 2009;28(2):369-76.##12. Miller EA, West DM. Where's the revolution? Digital technology and health care in the internet age. Journal of Health Politics, Policy and Law. 2009;34(2):261-84.##13. Bass PF, Wilson JF, Griffith CH, Barnett DR. Residents' ability to identify patients with poor literacy skills. Acad Med. 2002;77(10):1039-41.##14. Pleasant A, McKinney J. Coming to consensus on health literacy measurement: an online discussion and consensus-gauging process. Nurs Outlook. 2011;59(2):95-106##15. Morris NS, MacLean CD, Chew LD, Littenberg B. The Single Item Literacy Screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006 Mar 24;7:21.##16. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-94.##17. Collins SA, Currie LM, Bakken S, Vawdrey DK, Stone PW. Health literacy screening instruments for eHealth applications: a systematic review. J Biomed Inform. 2012 Jun;45(3):598-607. ##18. Brown CA, Dickson R. Healthcare students' e-literacy skills. J Allied Health. 2010;39(3):179-84.##19. Robinson C, Graham J. Perceived Internet health literacy of HIV-positive people through the provision of a computer and Internet health education intervention. Health Info Libr J. 2010;27(4):295-303.##20. Van der Vaart R, van Deursen AJ, Drossaert CH, Taal E, van Dijk JA, van de Laar MA. Does the eHealth Literacy Scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. Journal of Medical Internet Research. 2011;13(4):86.##21. Parker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults: a new instrument for measuring patients' literacy skills. J Gen Intern Med. 1995;10(10):537-41.##22. Chisolm DJ, Buchanan L. Measuring adolescent functional health literacy: a pilot validation of the test of functional health literacy in adults. Journal of Adolescent Health. 2007;41(3):312-4.##23. Lee SY, Bender DE, Ruiz RE, Cho YI. Development of an easy-to-use Spanish Health Literacy test. Health Serv Res. 2006;41(4 Pt 1):1392-412.##24. Yost KJ, Webster K, Baker DW, Choi SW, Bode RK, Hahn EA.. Bilingual health literacy assessment using the Talking Touchscreen/la Pantalla Parlanchina: Development and pilot testing. Patient Educ Couns. 2009;75(3):295-301. ##25. Aguirre AC, Ebrahim N, Shea JA. Performance of the English and Spanish S-TOFHLA among publicly insured Medicaid and Medicare patients. Patient Educ Couns. 2005;56(3):332-9. ##26. Bennett IM, Robbins S, Al-Shamali N, Haecker T. Screening for low literacy among adult caregivers of pediatric patients. Fam Med. 2003;35(8):585-90. ##27. Quinlan P. The relationship between health literacy, health knowledge and adherence to treatment in patients with rheumatoid arthritis. Columbia University; 2009.##28. Kooshyar H, Shoorvazi M, Dalir Z, Hosseini M. Health literacy and its relationship with medical adherence and health-related quality of life in diabetic community-residing elderly. J Mazanderan Univ Med Sci. 2014; 23(1):134-43. Persian.##29. Shea JA, Beers BB, McDonald VJ, Quistberg DA, Ravenell KL, Asch DA. Assessing health literacy in African American and Caucasian adults: disparities in rapid estimate of adult literacy in medicine (REALM) scores. Fam Med. 2004;36(8):575-81. ##30. Davis TC, Long SW, Jackson RH, Mayeaux EJ, George RB, Murphy PW, et al. Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med. 1993;25(6):391-5. ##31. Dumenci L, Matsuyama RK, Kuhn L, Perera RA, Siminoff LA. On the Validity of the Rapid Estimate of Adult Literacy in Medicine (REALM) Scale as a Measure of Health Literacy. Commun Methods Meas. 2013;7(2):134-43.##32. Bass PF, Wilson JF, Griffith CH. A shortened instrument for literacy screening. J Gen Intern Med. 2003; 18(12): 1036–8. ##33. Loueng JL, Fitz AL, Maack BJ, Miller DR. Evaluation of health literacy tools for correct prescription understanding. J Am Pharm Assoc. 2015;55(3):273-7.##34. Patel PJ, Joel S, Rovena G, Pedireddy S, Saad S, Rachmale R, et al. Testing the utility of the newest vital sign (NVS) health literacy assessment tool in older African-American patients. Patient Educ Couns. 2011;85(3):505-7. ##35. Shah LC, West P, Bremmeyr K, Savoy-Moore RT. Health literacy instrument in family medicine: the "newest vital sign" ease of use and correlates. J Am Board Fam Med. 2010;23(2):195-203. ##36. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-94.##37. Samadbeik M, Ahmadi M, Mohammadi A, Mohseni Saravi B. Health information on internet: quality, importance, and popularity of persian health websites. Iran Red Crescent Med J. 2014;16(4):e12866. ##38. Jeppesen KM, Coyle JD, Miser WF. Screening questions to predict limited health literacy: a cross-sectional study of patients with diabetes mellitus. Ann Fam Med. 2009;7(1):24-31.##39. Park H, Lee E. Self-reported eHealth literacy among undergraduate nursing students in South Korea: a pilot study. Nurse Educ Today. 2015;35(2):408-13.## ##