ORIGINAL_ARTICLE
Challenges of Information Systems in Healthcare Organizations
Introduction: Information systems enable the managers to access the proper information needed to make decisions. Management decision making based on real information leads to increased efficiency and effectiveness. Problems related to information system will lead to incorrect information and consequently incorrect decisions. Therefore, this study was conducted to investigate and identify the challenges of ISs in healthcare organizations. Methods: This study is a systematic review. PubMed/Medline, Scopus, PMC and Iranian databases such as Magiran and SID were searched from 2013 to 2018. English and Persian studies were searched using keywords including IS, Hospital Information System, Health Information Technology, Health Information System, Medical Information Reporting System, Electronic Medical Record, Electronic Health Record, Medical Informatics, Health Informatics, Nursing Information System, Nursing Informatics, hospital Management Information System, Nursing Clinical Information System, Pharmacy Information System, and Electronic Medical Record System. In the initial search, 300 studies were identified. After screening the studies using the exclusion criteria, 101 of them were selected. Then, through complete reviews of full texts of the studies, 54 of them were excluded from the study. The rest of the articles were coded by Esterberg method and 6 themes of challenges were extracted. Results: The results showed that the challenges of IS in the health system included structural, manpower, financial/support, security, process, and organizational challenges. Conclusion: To achieve the success and effectiveness of IS and make the right decisions based on the proper information, it is necessary to eliminate the issues that lead to problems in these systems.
https://jhmi.sums.ac.ir/article_47408_8bd10030a7a37aa017cbf78a9e19c4ea.pdf
2020-10-01
187
195
health information system
Medical Informatics
Electronic Health Records
Hospital Information System
Nursing Informatics
Challenge
Behnam
Talebi
btalebi1351@yahoo.com
1
Department of Educational Sciences, Tabriz Branch, Islamic Azad University, Tabriz, Iran
AUTHOR
Nayer
Seyednazari
n.seyednazari@yahoo.com
2
Medical Education Research Center, Health Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
LEAD_AUTHOR
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ORIGINAL_ARTICLE
Designing and Developing the Model of Talent Management Process at Ahvaz Jundishapur University of Medical Sciences
Introduction: Talent management assures the organizations that competent people with the right skills and right job position are employed. Moreover, paying attention to the performance of universities and the strategic role that they play in advancing the country's goals is important Is of utmost significance The talent management process of faculty members in talent-based universities is essential to be designed and implemented in a way that the best individuals are considered and employed.The present study was conducted to design the model of talent management process fo Faculty Members for the Universities of Medical Sciences. Methods: This is an applied and mixed method study. In the qualitative part, using semi-structured interviews with 17 experts of Ahvaz Jundishapur University of Medical Sciences, the components of talent management were identified and analyzed by content analysis method .Using this interview method, the researcher asked all the respondents the same questions, and the sequence of the questions were predetermined based on the theoretical foundation. In the quantitative part, the statistical population consisted of 615 faculty members of Ahvaz Jundishapur University of Medical Sciences. The sample size was estimated 240 people using Cochrane method(1977) randomly . Results: Findings of the quantitative section confirmed the structure of talent management process which includes five distinct dimensions, identification and determination of talent needs, acquisition, development of potential abilities, strategic utilization, and maintenance of talents. Finally, a model for talent management process was proposed at Ahvaz University of Medical Sciences based on data from qualitative and quantitative research. According to the main findings of this study, 5 main categories and 14 subcategories show that the cases proposed by the experts are appropriate with the talent management process.
https://jhmi.sums.ac.ir/article_47401_11bd7adf5f7bcd72f8f1ed409e37e1ce.pdf
2020-10-01
196
205
Keywords: Talent Management
Faculty Members
Ahvaz University of Medical Sciences
Fatemeh
Taheri
fatemeh.taheri8462@gmail.com
1
Department of Educational Administration, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
AUTHOR
Mohammad
Hosseinpour
hosseinpour6@yahoo.com
2
Department of Educational Administration, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
LEAD_AUTHOR
Seyed Ali
Siadat
s.a.siadat@edu.ui.ac.ir
3
Department of Educational Science and Psychology, Isfahan University, Isfahan, Iran
AUTHOR
1. Armstrong M. A handbook of human resource management practice. New York: Kogan Page Publishers; 2006.
1
2. Nunn N. Historical legacies: A model linking Africa’s past to its current underdevelopment. Journal of development economics. 2007;83(1):157- 75. doi: 10.1016/j.jdeveco.2005.12.003.
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3. Collings DG, Mellahi K. Strategic talent management: A review and research agenda. Human resource management review. 2009;19(4):304-13. doi: 10.1016/j. hrmr.2009.04.001.
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4. Daigle SL, Jarmon CG. Building the Campus Infrastructure that Really Counts. Educom Review. 1997;32(4):35-8.
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5. Mohammadi M, Norouzi-kouhdasht R, Marzoughi R, Torkzadeh J, Salimi G. Evaluating talent management process of faculty members in Lorestan university of medical sciences: Mixed method research. Research in Medical Education. 2018;10(3):35-46. doi: 10.29252/rme.10.3.35.
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6. Corcella G, Knowles IG, Marchesini G, Moretti S, Odagiri K, Richardson P, et al. HERWIG 6: an event generator for hadron emission reactions with interfering gluons (including supersymmetric processes). Journal of High Energy Physics. 2001;2001(01):010. doi: 10.1088/1126-6708/2001/01/010.
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8. Schweyer A. Talent management systems: Best practices in technology solutions for recruitment, retention and workforce planning. New Jersey: John Wiley & Sons; 2010.
8
9. Walker JW, LaRocco JM. Talent pools: The best and the rest.(Perspectives). Human Resource Planning. 2002;25(3):12-5.
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10. Hughes JC, Rog E. Talent management: A strategy for improving employee recruitment, retention and engagement within hospitality organizations. International Journal of Contemporary Hospitality Management. 2008;20(5):743-57. doi: 10.1108/09596110810899086.
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11. Davenport TH, Harris J, Shapiro J. Competing on talent analytics. Harvard business review. 2010;88(10):52-8.
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12. Hartmann E, Feisel E, Schober H. Talent management of western MNCs in China: Balancing global integration and local responsiveness. Journal of world business. 2010;45(2):169-78. doi: 10.1016/j.jwb.2009.09.013.
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13. Sweem SL. Leveraging employee engagement through a talent management strategy: optimizing human capital through human resources and organization development strategy in a field study: Benedictine university; 2009.
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14. Phillips DR, Roper KO. A framework for talent management in real estate. Journal of Corporate Real Estate. 2009;11:7-16. doi: 10.1108/14630010910940525.
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15. Lewis RE, Heckman RJ. Talent management: A critical review. Human resource management review. 2006;16(2):139-54. doi: 10.1016/j. hrmr.2006.03.001.
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16. Oehley A-M. The development and evaluation of a partial talent management competency model. Stellenbosch: University of Stellenbosch; 2007.
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17. Haddadi E, Keikha A, Galavi M. Study of effect of talent management on the performance of teachers. International Journal of Biology, Pharmacy and Allied Sciences. 2015;4(12):1183-98.
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ORIGINAL_ARTICLE
The Nurses' Perception of Nurse-Physician Professional Relationship in Abu-Ali-Sina Organ Transplantation in Shiraz: A Descriptive Study in Iran
Background and Objective: Highly professional communication could result in the improvement of the quality of care such as reduced mortality, medical error, length of stay, and unplanned readmission. Therefore, the purpose of this study was to determine the nurses' perception of nurse-physician professional relationship in Abu-Ali-Sina organ transplant hospital of Shiraz. Methods: This is a descriptive study performed cross-sectionally in Abu-Ali-Sina hospital in Shiraz in 2020. The sampling method was the census. All nurses working at Abu-Ali-Sina Organ Transplant Hospital (N=295) participated in the study, so no sampling was applied. A questionnaire was used to assess the nurses’ perception of the nurse-physician professional relationship that consisted of demographic data and 25 questions with three-point Likert scale about nurse-physician communication. This tool was used in a previous study(1). Data were analyzed using Statistical Package for Social Sciences (SPSS) version 24. Results: The finding of this study showed that the level of professional nurses- physician relationship was moderate from the nurses' point of view. Reporting of patient issues to physicians (72.2%), physicians' respect (67.8%), and trust (66.1%) to nurses were highly desirable at Abu-Ali-Sina Hospital. However, the nurse’s assertive skills to tell the physicians’ errors (14.2%) and their encouragement by physicians (10.2%) were not favorable. Conclusion: From the results, it is possible to conclude that the nurse-physician professional relationship is at a moderate level in Abu-Ali Sina Transplant Center. Further studies are need to implement intervention for improving the level of physician and nurse’s relationship.
https://jhmi.sums.ac.ir/article_47402_02e8f0bcdba819faf6f42c50d6f666b3.pdf
2020-10-01
206
212
Keywords: Nurse-Physician Professional Relationship
Nurses' Perception
hospital
Transplant
Reza
Nikandish
nikandishr@sums.ac.ir
1
M.D. Associate Professor of Anesthesiology, Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
AUTHOR
mehrdad
karajizadeh
mehrdad.karaji@gmail.com
2
PhD Candidate, School of Management & Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
LEAD_AUTHOR
Razieh
Rasekh
rasekhr@sums.ac.ir
3
Critical Care Nursing, Shiraz Organ Transplant Center, Avicenna Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Mohammadbagher
Soleimanijafarbiglo
soleimani.mmd@gmail.com
4
MSc, Computer Engineering, Shiraz Organ Transplant Center, Avicenna Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Laleh
Golafshan
golafshan@gmail.com
5
MSc, Computer Engineering, Department of Computer Engineering, Faculty of Engineering, Marvdasht Branch, Islamic Azad University, Fars, Iran
AUTHOR
Mahsa
Roozrokh Arshadi Montazer
mahsaroozrokh@yahoo.com
6
MSc, Medical Informatics, Department of Health Information Management, School of Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
1. Shokri A, Yazdan Panah A, Vahdat S. The professional relationship between the nurses and physicians from their own point of view. Journal of Health and Care. 2013;15(1):76-69.
1
2. Agarwal R, Sands DZ, Schneider JD. Quantifying the economic impact of communication inefficiencies in U.S. hospitals. J Healthc Manag. 2010;55(4):265-81; discussion 81-2.
2
3. Vermeir P, Vandijck D, Degroote S, Peleman R, Verhaeghe R, Mortier E, et al. Communication in healthcare: a narrative review of the literature and practical recommendations. Int J Clin Pract. 2015;69(11):1257-67. doi: 10.1111/ijcp.12686.
3
4. Kang XL, Brom HM, Lasater KB, McHugh MD. The Association of Nurse-Physician Teamwork and Mortality in Surgical Patients. West J Nurs Res. 2020;42(4):245-53. doi: 10.1177/0193945919856338.
4
5. Beaird G. CE: A Historical Review of Nurse-Physician Bedside Rounding. Am J Nurs. 2019;119(4):30-8. doi: 10.1097/01. NAJ.0000554525.02127.3b.
5
6. Tan TC, Zhou H, Kelly M. Nurse-physician communication - An integrated review. J Clin Nurs. 2017;26(23-24):3974-89. doi: 10.1111/ jocn.13832.
6
7. Hailu FB, Kassahun CW, Kerie MW. Perceived Nurse-Physician Communication in Patient Care and Associated Factors in Public Hospitals of Jimma Zone, South West Ethiopia: Cross Sectional Study. PLoS One. 2016;11(9):e0162264. doi: 10.1371/journal.pone.0162264.
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ORIGINAL_ARTICLE
Identification and ranking of cardiovascular diseases risk factors with MICMAC managerial approach
Introduction: Cardiovascular diseases are one of the most important causes of mortality worldwide, and their prevention needs recognition of the factors affecting its occurrence and prospective planning. The aim of this study was to identify and rank the risk factors of cardiovascular diseases using MICMAC managerial approach. Methods: The present study was conducted in two phases. In the first phase, a comprehensive overview of cardiovascular disease risk factors was performed. In the second phase, the identified factors were ranked using MICMAC managerial approach. Results: In the literature review, 16 cardiovascular diseases risk factors including stress, anxiety and depression, nutrition and an unhealthy diet, low physical activity, smoking and drug consumption, hypertension, high blood lipids, overweight and obesity, age, gender, diabetes, family history, alcohol consumption, air and noise pollution, socioeconomic status, ethnicity and race and genetic factors were identified. According to the MICMAC approach and direct effects, three factors including unhealthy diet, obesity, and socioeconomic status were ranked first to third, as the most influential risk factors of cardiovascular diseases, respectively. Conclusion: According to the findings and focusing on the three factors of unhealthy diet, obesity and economic and social status, appropriate educational interventions, notification, and awareness raising among the community using the mass media are suggested.
https://jhmi.sums.ac.ir/article_47403_e2dc4aabf4e1cefd6e6eeb3777aa2837.pdf
2020-10-01
213
227
Risk factors
Disease
cardiovascular
Ali
Yusefi
alirezayusefi67@gmail.com
1
Assistant Professor of Healthcare Services Management,School of Health, Jiroft University of Medical Sciences, Jiroft, Iran
AUTHOR
Narjes Alsadat
Nasabi
2
PhD in Human Resource Management, Human Resource Manager, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Javad
Shahmohammadi
j.shahmohamadi@gmail.com
3
Ph.D. Health Services Management, Student Research Committee, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Maryam
Radinmanesh
maryam.radinmanesh@yahoo.com
4
4. Ph.D. Health Economics, Health Management and Economics Researcher Center, Iran University of Medical Sciences Tehran, Iran
AUTHOR
zahra
kavosi
zhr.kavosi@gmail.com
5
Associate Professor, Health Human Resources Research Center, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Roghayeh
Khabiri
6
Assistant professor, Tabriz Health Services Management Research Center. Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
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77. Hu FB, Rimm EB, Stampfer MJ, Ascherio A, Spiegelman D, Willett WC. Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr. 2000;72(4):912- 21. doi: 10.1093/ajcn/72.4.912.
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80. Akesson A, Larsson SC, Discacciati A, Wolk A. Low-risk diet and lifestyle habits in the primary prevention of myocardial infarction in men: a population-based prospective cohort study. J Am Coll Cardiol. 2014;64(13):1299-306. doi: 10.1016/j. jacc.2014.06.1190.
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81. Farzaneh R, Hoseini K, Vahedi S, Hamzeh N. Obesity and cardiovascular disease. Iranian Journal of Diabetes and Lipid. 2013;12(5):451-60.
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85. Agheli N, Assefzadeh S, Rajabi M. The prevalence of cardiovascular risk factors among population aged over 30 years in Rasht and Qazvin. Journal of Inflammatory Disease. 2005;9(2):59-65.
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101. Siddique I, Mitchell DA. The impact of a community-based health education programme on oral cancer risk factor awareness among a Gujarati community. Br Dent J. 2013;215(4):E7. doi: 10.1038/sj.bdj.2013.829.
101
ORIGINAL_ARTICLE
Residual Network of Residual Network: A New Deep Learning Modality to Improve Human Activity Recognition by Using Smart Sensors Exposed to Unwanted Shocks
Background and Objective: Recently, smartphones have been vastly utilized in monitoring the daily activities of people to check their health. The main challenge in this procedure is to distinguish similar activities based on signals recorded by using sensors mounted on smartphones and smartwatches.Although deep learning approaches have better addressed the above challenge than alternative methods, their performance may be severely degraded, especially when the mounted sensors receive disturbed signals due to smartphones and smartwatches not being in a fixed position.Methods: In this article, a new deep learning structure is introduced to recognize challenging human activities by using smartphones and smartwatches, even when the recorded signals are noisy due to the sensors being unstable. In the proposed structure, the residual network of residual network (i.e. ROR) is engaged as a new concept inside the deep learning architecture, which provides greater stability against either disturbed or noisy signals.Results: The performance of the proposed method is evaluated on recorded signals from smartphones and smartwatches and compared with the state of art techniques containing deep learning and classic (non-deep) schemes. The obtained results show that the proposed method may improve the recognition parameters at least 1.79 percent against deep alternatives in distinguishing challenging activities (i.e. downstairs and upstairs). These superiorities reach at least 32.86 percent for classic methods, which are applied on the same data. Conclusions: The effectiveness of the architecture in recognizing either challenging or non-challenging activities in the presence of unwanted cell phone shocks demonstrates its potential to be used as a mobile application for human activity recognition.
https://jhmi.sums.ac.ir/article_47404_1ea44dc42259e3f3d18c11c9d0ec98fa.pdf
2020-10-01
228
239
Human Activity Recognition
Smartphone
Deep Learning
Gradient Flow
Residual Networks of Residual Networks
Mohammad Javad
Beirami
beyramimj@gmail.com
1
Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran,
LEAD_AUTHOR
Seyed Vahab
Shojaedini
shojadini@irost.ir
2
Associate Professor of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran
AUTHOR
1. Castro D, Coral W, Rodriguez C, Cabra J, Colorado J. Wearable-based human activity recognition using an iot approach. Journal of Sensor and Actuator Networks. 2017;6(4):28. doi: 10.3390/jsan6040028.
1
2. Zhang S, Wei Z, Nie J, Huang L, Wang S, Li Z. A Review on Human Activity Recognition Using Vision-Based Method. J Healthc Eng. 2017;2017:3090343. doi: 10.1155/2017/3090343.
2
3. Chaquet JM, Carmona EJ, Fernández-Caballero A. A survey of video datasets for human action and activity recognition. Computer Vision and Image Understanding. 2013;117(6):633-59. doi: 10.1016/j.cviu.2013.01.013.
3
4. He Z, Jin L. Activity recognition from acceleration data based on discrete consine transform and SVM. 2009 IEEE International Conference on Systems, Man and Cybernetics. 2009:5041-4. doi: 10.1109/ICSMC.2009.5346042.
4
5. Wang S, Yang J, Chen N, Chen X, Zhang Q. Human activity recognition with user-free accelerometers in the sensor networks. 2005 International Conference on Neural Networks and Brain. 2005;2:1212-7.
5
6. Bao L, Intille SS. Activity recognition from user-annotated acceleration data. International conference on pervasive computing. 2004:1-17. doi: 10.1007/978-3-540-24646-6_1.
6
7. Ravi N, Dandekar N, Mysore P, Littman ML. Activity recognition from accelerometer data. Aaai. 2005;5(2005):1541-6.
7
8. Mantyjarvi J, Lindholm M, Vildjiounaite E, Makela S-M, Ailisto H. Identifying users of portable devices from gait pattern with accelerometers. Proceedings(ICASSP’05) IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. 2005;2:ii/973-ii/6 Vol. 2.
8
9. Ordonez FJ, Roggen D. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors (Basel). 2016;16(1). doi: 10.3390/s16010115.
9
10. Kwapisz JR, Weiss GM, Moore SA. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter. 2011;12(2):74- 82. doi: 10.1145/1964897.1964918.
10
11. Ignatov A. Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. Applied Soft Computing. 2018;62:915-22. doi: 10.1016/j.asoc.2017.09.027.
11
12. Kim TW, Lee SM, Seong SC, Lee S, Jang J, Lee MC. Different intraoperative kinematics with comparable clinical outcomes of ultracongruent and posterior stabilized mobile-bearing total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2016;24(9):3036-43. doi: 10.1007/ s00167-014-3489-0.
12
13. Lee S-M, Yoon SM, Cho H. Human activity recognition from accelerometer data using Convolutional Neural Network. 2017 ieee international conference on big data and smart computing (bigcomp). 2017:131-4.
13
14. Alsheikh MA, Selim A, Niyato D, Doyle L, Lin S, Tan H-P. Deep activity recognition models with triaxial accelerometers. arXiv preprint arXiv:151104664. 2015.
14
15. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, et al. Human activity recognition using recurrent neural networks. International cross-domain conference for machine learning and knowledge extraction. 2017:267-74. doi: 10.1007/978-3-319-66808-6_18.
15
16. Shojaedini SV, Beirami MJ. Mobile sensor based human activity recognition: distinguishing of challenging activities by applying long shortterm memory deep learning modified by residual network concept. Biomed Eng Lett. 2020;10(3):419- 30. doi: 10.1007/s13534-020-00160-x.
16
17. Zhang Y, Chan W, Jaitly N. Very deep convolutional networks for end-to-end speech recognition. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017:4845-9.
17
18. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. European conference on computer vision. 2014:818-33. doi: 10.1007/978-3-319-10590-1_53.
18
19. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics. 2010:249-56.
19
20. Bengio Y, Simard P, Frasconi P. Learning longterm dependencies with gradient descent is difficult. IEEE Trans Neural Netw. 1994;5(2):157- 66. doi: 10.1109/72.279181.
20
21. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:770-8.
21
22. Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning. 2015:448-56.
22
23. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research. 2010;9:249-56.
23
24. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision. 2015:1026-34.
24
25. LeCun YA, Bottou L, Orr GB, Müller K-R. Efficient backprop. Neural networks: Tricks of the trade: Springer; 2012. p. 9-48.
25
26. He K, Sun J. Convolutional neural networks at constrained time cost. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015:5353-60
26
27. Srivastava RK, Greff K, Schmidhuber J. Highway networks. arXiv preprint arXiv:150500387. 2015.
27
28. Zhang K, Sun M, Han TX, Yuan X, Guo L, Liu T. Residual networks of residual networks: Multilevel residual networks. IEEE Transactions on Circuits and Systems for Video Technology. 2017;28(6):1303-14. doi: 10.1109/ TCSVT.2017.2654543.
28
29. Weiss GM, Yoneda K, Hayajneh T. Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access. 2019;7:133190-202. doi: 10.1109/ACCESS.2019.2940729.
29
30. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics. 2000;16(5):412-24. doi: 10.1093/ bioinformatics/16.5.412.
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31. Plötz T, Hammerla NY, Olivier PL, editors. Feature learning for activity recognition in ubiquitous computing. Twenty-second international joint conference on artificial intelligence; 2011.
31
32. Zeng M, Nguyen LT, Yu B, Mengshoel OJ, Zhu J, Wu P, et al. Convolutional neural networks for human activity recognition using mobile sensors. 6th International Conference on Mobile Computing, Applications and Services. 2014:197- 205. doi: 10.4108/icst.mobicase.2014.257786.
32
33. Witten IH, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques. (The Morgan Kaufmann Series in Data Management Systems). 3rd Edition. Amsterdam: Elsevier. 2011.
33
ORIGINAL_ARTICLE
The Effect of Organizational Structure Dimensions on the Spirituality in the Workplace
Background: In past research, spirituality is associated with several organisational behaviours, but the factors that generate and bolster spirituality in the workplace are rarely studied. To make the organisation and its staff spiritual, it is necessary to provide a set of conditions in which the employees can play their role peacefully and they can be in utmost alignment with organisational goals. The current research intends to investigate the effect of organisational structure on workplace spirituality. In other words, the main objective of this research is to examine the effect of organisational structure’s dimensions on the spirituality at Qom University of Medical Sciences. Methods: It is an applied cross-sectional research where organisational structure is the independent variable and spirituality in the workplace is the dependent variable. All the employees of Qom University of Medical Sciences, Qom, Iran, formed the population of the study and the whole population was studied. The main instrument of data collection was a questionnaire evaluating both independent and dependent variables. Results: The results highlighted that parameters like complexity and formalization (two organisational structure dimensions) had significant effects on workplace spirituality, while no relationship was observed between centralisation (another dimension of organisational structure) and workplace spirituality.According to the results, the organizational structure is an effective variable assisting the managers in creating a spiritual workplace. Conclusion: It is recommended that the organisations should encourage their employees to express spirituality, so as to benefit from advantages of spiritual employees and identify the remedy in their organisational structure.
https://jhmi.sums.ac.ir/article_47407_3532e68fea49f1c58cd1a25fb42ba23c.pdf
2020-10-01
240
251
Spirituality
Organizational Structure
Structural Equation Modeling
SmartPLS
Ebrahim
Javaheri Zadeh
javaheri1390@gmail.com
1
Department of Management, Faculty of Management and Accounting, Allameh Tabatabaei University, Tehran, Iran
AUTHOR
Saeed
Nosratabadi
saeed.nosratabadi@gmail.com
2
Institute of Business Studies, Faculty of Economics and Social Sciences, Szent István University, Budapest, Hungary
AUTHOR
Parvaneh
Bahrami
bahrami_parvane@yahoo.com
3
Department of Management, Faculty of Management and Accounting, Allameh Tabatabaei University, Tehran, Iran
AUTHOR
Mohammad Reza
Fathi
reza.fathi@ut.ac.ir
4
Department of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
LEAD_AUTHOR
1. Klenke K. Introducing spirituality. International Journal of Organizational Analysis. 2005;13(1):4- 7. doi: 10.1108/eb028994.
1
2. Moghimi SM, Rahbar AH, Eslami H. Organizational Spirituality and its Effect on the Staff’s Creativity: An Adaptive Approach. Faslnameh Akhlagh dar Oloum Fanavari. 2007;2(3-4);89-98. Persian.
2
3. Charoensukmongkol P, Daniel JL, Chatelain- Jardon R. Enhancing workplace spirituality through emotional intelligence. Journal of Applied Management and Entrepreneurship. 2013;18(4):3. doi: 10.9774/gleaf.3709.2013.oc.00003.
3
4. Petchsawang P, Duchon D. Workplace spirituality, meditation, and work performance. Journal of management, spirituality & religion. 2012;9(2):189- 208. doi: 10.1080/14766086.2012.688623.
4
5. Winston B. Spirituality at workplace. International Journal on Spirituality and Organization Leadership. 2013;21-32.
5
6. Krishnakumar S, Neck CP. The “what”,“why” and “how” of spirituality in the workplace. Journal of managerial psychology. 2002;17(3):153-64. doi: 10.1108/02683940210423060.
6
7. Milliman J, Czaplewski AJ, Ferguson J. Workplace spirituality and employee work attitudes: An exploratory empirical assessment. Journal of organizational change management. 2003;16(4);426-47.doi: 10.1108/09534810310484172.
7
8. Daniel JL. The effect of workplace spirituality on team effectiveness. Journal of management development. 2010;29(5);442-56. doi: 10.1108/02621711011039213.
8
9. Tombaugh JR, Mayfield C, Durand R. Spiritual expression at work: exploring the active voice of workplace spirituality. International Journal of Organizational Analysis. 2011:19(2):146-70. doi: 10.1108/19348831111135083.
9
10. Fanggidae RE, Suryana Y, Efendi N. Effect of a spirituality workplace on organizational commitment and job satisfaction (study on the lecturer of private Universities in the Kupang city-Indonesia). Procedia-Social and Behavioral Sciences. 2016;219:639-46. doi: 10.1016/j. sbspro.2016.05.045.
10
11. De Klerk JJ. Spirituality, meaning in life, and work wellness: A research agenda. International Journal of Organizational Analysis. 2005;13(1);64- 8. doi: 10.1108/eb028998.
11
12. Williams WA, Brandon R-S, Hayek M, Haden SP, Atinc G. Servant leadership and followership creativity. Leadership and Organization Development Journal. 2017:38(2);178-93. doi: 10.1108/LODJ-02-2015-0019.
12
13. Wagner‐Marsh F, Conley J. The fourth wave: the spiritually‐based firm. Journal of Organizational Change Management. 1999:12(4);292-302. doi: 10.1108/09534819910282135.
13
14. Mirvis PH. Crossroads—“soul work” in organizations. Organization Science. 1997;8(2):192-206. doi: 10.1287/orsc.8.2.192.
14
15. Guillory JA, Sowell R, Moneyham L, Seals B. An exploration of the meaning and use of spirituality among women with HIV/AIDS. Alternative therapies in health and medicine. 1997;3(5):55-60.
15
16. Ashmos DP, Duchon D. Spirituality at work: A conceptualization and measure. Journal of management inquiry. 2000;9(2):134 45. d0i: 10.1177/105649260092008.
16
17. Kinjerski V, Skrypnek BJ. A human ecological model of spirit at work. Journal of management, Spirituality & religion. 2006;3(3):231-41. doi: 10.1080/14766080609518627.
17
18. Saneei M, Hasanpour A. The Basic Solution of Raising Spirituality in Organizations. Akhlagh dar Oloum va Fanavary. 2012:2(7);1-10. Persian.
18
19. Hungelmann J, Kenkel-Rossi E, Klassen L, Stollenwerk RM. Spiritual well-being in older adults: Harmonious interconnectedness. J Relig Health. 1985;24(2):147-53. doi: 10.1007/ BF01532258.
19
20. Polley D, Vora J, SubbaNarasimha P. Paying the devil his due: Limits and liabilities of workplace spirituality. International Journal of Organizational Analysis. 2005: 3(1);50-62. doi: 10.1108/eb028997.
20
21. Giacalone R, Iurkiewicz C. Toward a Science of Workplace Spirituality. Handbook of Workplace, 2003.
21
22. Chawla V, Guda S. Individual spirituality at work and its relationship with job satisfaction, propensity to leave and job commitment: An exploratory study among sales professionals. Journal of Human values. 2010;16(2):157-67. d0i: 10.1177/097168581001600203.
22
23. Torabi M, Ghochani MM, Zohoorian Nadali I, Fathi MR. The Mediating Role of Organization Engagement on Perceived Supervisor Support and Intention to Leave (Case study: Faghihi Hospital in Shiraz). Journal of Health Management & Informatics. 2019;6(4):138-44.
23
24. Raeesi Nafchi S, Fathi MR, Boroomand M. The Role of Employee Perceptions of Job Characteristics, Work Environment, Person- Organizations Fit Elements and in Creating Tendency toward Turnover. Journal of Health Management & Informatics. 2020;7(1):17-23.
24
25. Rahmanseresht H. Theories of Organization and Management; from Modernism to Postmodernism. Tehran: Nashr Doran; 2013. Persian.
25
26. Giao D. Report on Results of the Study Tour on Public Administration Reform.in China. Singapore and South Korea: Proceedings of Social Goverment Conference, 2004.
26
27. Daft R. Organization Theory and Design. Tehran: Cultural Research Office; 2006.
27
28. Shahidi M. Investigating the Relationship between Organizational Structure and Human Resource Motivation. Danesh Modiriat. 1998:43(11);100-25. Persian.
28
29. Hatch MJ. Organization theory: Modern, symbolic, and postmodern perspectives. Oxford: Oxford university press; 2012.
29
30. Fry LW, Slocum Jr JW. Technology, structure, and workgroup effectiveness: A test of a contingency model. Academy of management journal. 1984;27(2):221-46. doi: 10.5465/255923.
30
31. Marsh RM, Mannari H. Technology and size as determinants of the organizational structure of Japanese factories. Administrative science quarterly. 1981:33-57.
31
32. Martínez‐León IM, Martínez‐García JA. The influence of organizational structure on organizational learning. International Journal of Manpower. 2011.
32
33. Monavarian A, Kheirandish M, Asgari N. Development of Organizational Structural Dimensions in Accordance with Knowledge Management Approach. Modiriat Fanavari Etelaat. 2011:7(3);133-50. Persian.
33
34. Yadollahi Farsi J, Azizi Ziarat A, Khastar H. Investigating the Relationship between Organizational Structure and Entrepreneurship Case Study: Private Banks of Tehran. Tehran: Toseeh Karafarini; 2009. Persian.
34
35. Kordnaeij A, Moghimi S, Yazdani H. Investigating the Relationship Between the Elements of Organizational Structure and Entrepreneurial Culture in Tehran University. Tehran: Nasherie Modiriat Dolati; 2009. Persian.
35
36. Majidi A, Mohammadi Moghaddam Y, Ghasemi F. The Effect of Organizational Structure on Improving the Performance of the Vice- Chancellor of Education in the University of Oloum Entezami. Faslnameh Toseeh Modiriat Manabe Ensani. 2011;21(6);201-20. Persian.
36
37. Garcia‐Zamor JC. Workplace spirituality and organizational performance. Public administration review. 2003;63(3):355-63.
37
38. Lips‐Wiersma M, Mills C. Coming out of the closet: Negotiating spiritual expression in the workplace. Journal of managerial Psychology. 2002. 17(3), 183-202.doi: 10.1108/02683940210423097.
38
ORIGINAL_ARTICLE
Gender Equality among Nurses: Promotion Strategies for Gender Equality
Introduction: Nurses have a special position in hospitals due to their specialized, valuableand stressful job. Gender inequality together with the concerns about health inequalities needattention. Therefore, this study aimed to investigate gender equality among nurses in Farsprovince of Iran and to provide solutions to improve gender equality.Methods: This quantitative-qualitative study was conducted in 2020. In the quantitativephase, the study population included all nurses employed in public and private hospitals ofFars province as well as all nursing students at Fars universities during 2011-2018. In thequalitative phase, 16 semi-structured interviews were conducted. Using SPSS version 23.0,the quantitative data was analyzed by applying descriptive statistics and Chi-squared test.Qualitative data were analyzed with Max QDA by using the framework method.Results: The majority of hospital nurses (79.81%) and nursing students (65.86%) werefemale. There were statistically significant relationships between gender and hospital, thetype of employment, university, and the type of degree (P=0.00). In addition, 3 main themesincluding culture building, nursing education and employment as well as 17 sub-themes wereidentified to improve gender equality among nurses.Conclusion: Fars province faces challenges such as gender inequality among nurses andthe shortage of male nurses. It is essential to invest in the development and implementationof strategies and executive solutions for raising and maintaining the prestige of nursingprofession and training qualified nurses with a focus on creating healthcare job opportunitiesfor men and women equally.
https://jhmi.sums.ac.ir/article_47799_d75c43a2102686e6d7204272a2bf4b00.pdf
2020-10-01
252
258
Gender equality
Nurses
Promotion Strategies
Fars province
Seyed Jalil
Masoumi
masoumi7415@gmail.com
1
Nutrition Research Center, Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Science, Shiraz, Iran Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Center for Cohort Study of SUMS Employees’ Health, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Narjes Alsadat
Nasabi
nargesnasabee@yahoo.com
2
Manager of Organizational Development and Administrative Transformation, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Mohsen
Varzandeh
mvarzandeh@hotmail.com
3
Secretary of HSR (Health System Research) Committee, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
najmeh
bordbar
nabordbar@gmail.com
4
Student Research Committee, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
1. Bamberger PA, Biron M, Meshoulam I. Human resource strategy: Formulation, implementation, and impact. New York: Routledge; 2014. doi: 10.4324/9780203075838.
1
2. Ozcan S, Taranto Y, Hornby P. Shaping the health future in Turkey: a new role for human resource planning. Int J Health Plann Manage. 1995;10(4):305-19. doi: 10.1002/hpm.4740100406.
2
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ORIGINAL_ARTICLE
Patient Process Mapping at the Emergency Department in Humber River Hospital: A Case report
Introduction: Benchmarking, a powerful management approach for implementing excellent practices at the best cost and quality, is a recent concept in the healthcare system. Aim: The ultimate goal of this research project was to describe and map patient care-flow process at the Emergency Department (ED) in the Humber River Hospital (HRH) as a benchmark and the first full digital hospital in Canada. The motivation of the researcher to select the ED as a research territory was the existence of a massive model and benchmark ED with four zones. Methods: This study was a cross-sectional, case report study. The population under the study was the staff who worked in the ED and were willing to participate in the research study. Informed written consent was obtained from the participants in the study. Several interviews were done to approve the validity of the questions with care providers that were co-investigators. Then, Staff in the ED were interviewed to get an understanding of the terminology and classifications used in the ED. Results: The hospital was designed and built on three core principles; Lean, Green and Digital. It uses the best possible technology to support hospital delivery, such as dynamic and smart glass, Ascom phone (connects to Humber Information System and Electronic Medical Record), smart bed technology; robotic technology for certain surgical procedures; automated laboratory processing; automated guided vehicles that deliver medical supplies; and bedside computer screens that allow the patients to control their environments.
https://jhmi.sums.ac.ir/article_47405_ba5420eb7efca8af3180295920ab74ab.pdf
2020-10-01
259
265
Emergency department
Information Technology
Care flow
Map
Sima
Ajami
simaajami@yahoo.com
1
Professor in Health Information Management; Department of Health Information Technology and Management, School of Medical Management and Information Sciences, Isfahan University of Medical Sciences, Hezarjerib Avenue, Isfahan, Iran
LEAD_AUTHOR
1. Ettorchi-Tardy A, Levif M, Michel P. Benchmarking: a method for continuous quality improvement in health. Healthc Policy. 2012;7(4):e101-19.
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2. Sower VE. Benchmarking in hospitals: More than a scorecard. Quality progress. 2007;40(8):58-60.
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3. Schull M, Vermeulen M, Guttmann A, Stukel T. Better performance on length-of-stay benchmarks associated with reduced risk following emergency department discharge: an observational cohort study. CJEM. 2015;17(3):253-62. doi: 10.1017/ cem.2014.39.
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4. Adler-Milstein J, Ronchi E, Cohen GR, Winn LA, Jha AK. Benchmarking health IT among OECD countries: better data for better policy. J Am Med Inform Assoc. 2014;21(1):111-6. doi: 10.1136/ amiajnl-2013-001710.
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5. Vermeulen MJ, Stukel TA, Boozary AS, Guttmann A, Schull MJ. The Effect of Pay for Performance in the Emergency Department on Patient Waiting Times and Quality of Care in Ontario, Canada: A Difference-in-Differences Analysis. Ann Emerg Med. 2016;67(4):496-505 e7. doi: 10.1016/j.annemergmed.2015.06.028.
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6. Lee FI, Williamson K. Edited by: Jennifer Robertson. Emergency Department Flow: What works, what does not work, and how can we improve? http://www.emdocs.net/emergencydepartment- flow-what-works-what-does-notwork-and-how-can-we-improve/
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