ORIGINAL_ARTICLE
Prediction of Protein Thermostability by an Efficient Neural Network Approach
Introduction: Manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. Various data mining techniques exist for prediction of thermostable proteins. Furthermore, ANN methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate approach to mapping the non-linear input-output relationships and massive parallel computing.Method: An Extreme Learning Machine (ELM) was applied to estimate thermal behavior of 1289 proteins. In the proposed algorithm, the parameters of ELM were optimized using a Genetic Algorithm (GA), which tuned a set of input variables, hidden layer biases, and input weights, to and enhance the prediction performance. The method was executed on a set of amino acids, yielding a total of 613 protein features. A number of feature selection algorithms were used to build subsets of the features. A total of 1289 protein samples and 613 protein features were calculated from UniProt database to understand features contributing to the enzymes’ thermostability and find out the main features that influence this valuable characteristic.Results:At the primary structure level, Gln, Glu and polar were the features that mostly contributed to protein thermostability. At the secondary structure level, Helix_S, Coil, and charged_Coil were the most important features affecting protein thermostability. These results suggest that the thermostability of proteins is mainly associated with primary structural features of the protein. According to the results, the influence of primary structure on the thermostabilty of a protein was more important than that of the secondary structure. It is shown that prediction accuracy of ELM (mean square error) can improve dramatically using GA with error rates RMSE=0.004 and MAPE=0.1003.Conclusion: The proposed approach for forecasting problem significantly improves the accuracy of ELM in prediction of thermostable enzymes. ELM tends to require more neurons in the hidden-layer than conventional tuning-based learning algorithms. To overcome these, the proposed approach uses a GA which optimizes the structure and the parameters of the ELM. In summary, optimization of ELM with GA results in an efficient prediction method; numerical experiments proved that our approach yields excellent results.Keywords: Protein Stability, Primary and secondary structures, Extreme learning machine, Neural networks, Genetic algorithm
https://jhmi.sums.ac.ir/article_42674_64b9597a5cd618804a580b44fb7bac85.pdf
2016-10-01
102
110
Jalal
Rezaeenour
j.rezaee@qom.ac.ir
1
LEAD_AUTHOR
Mansoureh
Yari Eili
2
AUTHOR
Zahra
Roozbahani
roozbahani2@gmail.com
3
AUTHOR
Mansour
Ebrahimi
ebrahimi@qom.ac.ir
4
AUTHOR
Asial I, Cheng YX, Engman H, Dollhopf M, Wu B, Nordlund P, et al. Engineering protein thermostability using a generic activity-independent biophysical screen inside the cell. Nat Commun. 2013;4:2901.
1
Chitturi B, Shi S, Kinch LN, Grishin NV. Compact Structure Patterns in Proteins. J Mol Biol. 2016 Aug 4.
2
Kumwenda B, Litthauer D, Bishop OT, Reva O. Analysis of protein thermostability enhancing factors in industrially important thermus bacteria species. Evol Bioinform Online. 2013;9:327-42.
3
Meysman P, Zhou C, Cule B, Goethals B, Laukens K. Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns. BioData Min. 2015;8:4.
4
Movahedi M, Zare-Mirakabad F, Arab SS. Evaluating the accuracy of protein design using native secondary sub-structures. BMC Bioinformatics. 2016;17(1):353.
5
Pucci F, Dhanani M, Dehouck Y, Rooman M. Protein thermostability prediction within homologous families using temperature-dependent statistical potentials. PLoS One. 2014;9(3):e91659.
6
Ebrahimi M, Ebrahimie E, Ebrahimi M, Deihimi T, Delavari A, Mohammadi-dehcheshmeh M. Application of neural networks methods to define the most important features contributing to xylanase enzyme thermostability. CEC 2009: IEEE Congress on Evolutionary Computation. 2009:18-21, 5-2891.
7
Ebrahimi M, Ebrahimie E. Sequence-Based Prediction of Enzyme Thermostability Through Bioinformatics Algorithms. Current Bioinformatics. 2010;5(3):195-203.
8
Satpathy R, Konkimalla V, Ratha J. Propensity based classification: Dehalogenase and non-dehalogenase enzymes. Journal of AI and Data Mining. 2015;3(2):209-15.
9
Zhao W, Wang X, Deng R, Wang J, Zhou H. Discrimination of thermostable and thermophilic lipases using support vector machines. Protein Pept Lett. 2011 Jul;18(7):707-17.
10
Ebrahimie E, Ebrahimi M, Deihimi T, Ebrahimi M. Using neural networks expert system to predict protein thermostability. 2011.
11
Huang L-T, Wu C-C, Lai L-F, Gromiha MM, Wang C-S, Chen Y-R. Data mining application in biomedical informatics for probing into protein stability upon double mutation. Appl Math. 2014;8(1L):125-32.
12
Zhang G, Fang B. Application of amino acid distribution along the sequence for discriminating mesophilic and thermophilic proteins. Process Biochemistry. 2006;41(8):1792-8.
13
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14
Xu J, Chen Y. Discrimination of Protein Thermostability Based on a New Integrated Neural Network. 2011;7062:107-12.
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Wu L-C, Lee J-X, Huang H-D, Liu B-J, Horng J-T. An expert system to predict protein thermostability using decision tree. Expert Systems with Applications. 2009;36(5):9007-14.
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Gromiha MM, Suresh MX. Discrimination of mesophilic and thermophilic proteins using machine learning algorithms. Proteins. 2008 Mar;70(4):1274-9.
20
Amini M, Rezaeenour J, Hadavandi E. Effective intrusion detection with a neural network ensemble using fuzzy clustering and stacking combination method. Journal of Computing and Security. 2015;1(4).
21
Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1):489-501.
22
Luo J, Vong CM, Wong PK. Sparse Bayesian extreme learning machine for multi-classification. IEEE Trans Neural Netw Learn Syst. 2014 Apr;25(4):836-43.
23
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Marvi H, Esmaileyan Z, Harimi A. Estimation of LPC coefficients using evolutionary algorithms. Journal of AI and Data Mining. 2013;1(2):111-8.
25
Eftekhari M, Eftekhari M, Majidi M. Securing interpretability of fuzzy models for modeling nonlinear MIMO systems using a hybrid of evolutionary algorithms. Iranian Journal of Fuzzy Systems. 2012;9(1):61-77.
26
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32
ORIGINAL_ARTICLE
Adjustment and Development of Health User’s Mental Model Completeness Scale in Search Engines
Introduction: Users’ performance and their interaction with information retrieval systems can be observed in development of their mental models. Users, especially users of health, use mental models to facilitate their interactions with these systems and incomplete or incorrect models can cause problems for them . The aim of this study was the adjustment and development of health user’s mental model completeness scale in search engines.Method: This quantitative study uses Delphi method. Among various scales for users’ mental model completeness, Li’s scale was selected and some items were added to this scale based on previous valid literature. Delphi panel members were selected using purposeful sampling method, consisting of 20 and 18 participants in the first and second rounds, respectively. Kendall’s Coefficient of Concordance in SPSS version 16 was used as basis for agreement (95% confidence).Results:The Kendall coefficient of Concordance (W) was calculated to be 0.261(P-value<0.001) for the first and 0.336 (P-value<0.001) for the second round. Therefore, the study was found to be statistically significant with 95% confidence. Since the increase in the coefficient in two consecutive rounds was very little (equal to 0.075), surveying the panel members were stopped based on second Schmidt criterion and Delphi method was stopped after the second round. Finally, the dimensions of Li’s scale (existence and nature, search characteristics and levels of interaction) were confirmed again, but “indexing of pages or websites” was eliminated and “Difference between results of different search engines”, “possibility of access to similar or related webpages”, and “possibility of search for special formats and multimedia” were added to Li’s scale.Conclusion: In this study, the scale for mental model completeness of health users was adjusted and developed; it can help the designers of information retrieval systems in systematic development of these systems and can also help librarians and informatics experts in recognizing the necessary trainings for users in order to improve their information retrieval skills. Also, as a valid and adapted scale for Iranian universities of medical sciences, it can be used for investigating completeness level of health information users’ mental models of search engines.Keywords: Mental Model Completeness Scale, Health users, Search engines
https://jhmi.sums.ac.ir/article_42676_d852a15293d0b3bb4d7062b6b50a4a2f.pdf
2016-10-01
111
119
Maryam
Nakhoda
nakhod@ut.ac.ir
1
LEAD_AUTHOR
Zahra
Kazempour
zahrakazempour@ut.ac.ir
2
AUTHOR
Nader
Naghshineh
naghshin@ut.ac.ir
3
AUTHOR
Mahdieh
Mirzabeigi
mirzabeig@shirazu.ac.ir
4
AUTHOR
Sinkinson C, Alexander S, Hicks A, Kahn M. Guiding Design: Exposing Librarian and Student Mental Models of Research Guides. portal: Libraries and the Academy. 2012;12(1):63-84.
1
Zhang Y. The construction of mental models of information-rich web spaces: The development process and the impact of task complexity: University of North Carolina at Chapel Hill; 2009.
2
Zhang Y. The development of usersâ mental models of MedlinePlus in information searching. Library & Information Science Research. 2013;35(2):159-70.
3
Muramatsu J, Pratt W. Transparent Queries. 2001:217-24.
4
Thatcher A, Greyling M. Mental models of search engines: How do search engines work. Human-centred Computing: Cognitive, social and ergonomics aspects Lawrence Erlbaum Associates Inc, Mahaw. 2003.
5
Li P, Beheshti J, editors. Doctoral Studentsâ Mental Models of a Web Search Engine. CANADIAN JOURNAL OF INFORMATION AND LIBRARY SCIENCE-REVUE CANADIENNE DES SCIENCES DE L INFORMATION ET DE BIBLIOTHECONOMIE; 2005: CANADIAN ASSOC INFORMATION SCIENCE PO BOX 6174, STATION J, OTTAWA, ONTARIO K2A 1T2, CANADA.
6
Mlilo S. Mental models: have usersâ mental models of search engines improved in the last ten years? 2011.
7
Alexandra D. Mental models and error behavior in an interactive bibliographic retrieval system: Ph. D. dissertation, 9, Ann Arbor, Mich.: University Microfilms International; 1990.
8
Borgman CL. The userâs mental model of an information retrieval system: Effects on performance: Stanford University; 1984.
9
Saxon SA. Seventh-grade students and electronic information retrieval systems: An exploratory study of mental model formation, completeness and change1997.
10
Hasson F, Keeney S, McKenna H. Research guidelines for the Delphi survey technique. J Adv Nurs. 2000 Oct;32(4):1008-15.
11
Powell C. The Delphi Technique: Myths and Realities. Methodological Issues in Nursing Research. 2003;41(4):376-82.Delphi survey technique. J Adv Nurs. 2000 Oct;32(4):1008-15.
12
Dalkey N, Helmer O. An Experimental Application of the DELPHI Method to the Use of Experts. Management Science. 1963;9(3):458-67.
13
Dalkey NC, Brown BB, Cochran S. The Delphi method: An experimental study of group opinion: Rand Corporation Santa Monica, CA; 1969.
14
Woudenberg F. An Evolution of Delphi. Technological Foreeasting and Social Change. 1991.
15
Faeizi K, Irandoost M. Delphi method: A method for Research and decision making. Tehran: Industrial Management Organization; 2013.
16
Afshari B, al. e. [Internet in Libraries]. Tehran: Ketabdar; 1999.
17
Asemi A, Bahraloo G. [Access to information in digital systems]. Tehran: Ketabdar; 2000.
18
Clyde A. Search engines. Journal of National Studies on Librarianship and Information Organization. 1999;55:142-52.
19
Johnson FC, Griffiths JR, Hartley RJ. DEVISE: a framework for the evaluation of internet search engines: CERLIM (Centre for Research in Library and Information Management); 2001.
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Mohamadifar D, Niakan S. Tools and skills for search in Web. Tehran: Chapar Publication; 1998.
21
Soleymani H. Internet and Information. Tehran: Soleymani Publication; 2000.
22
Baeza-Yates R, Berthier R. Modern Information Retrieval. Tehran: Chapar Publivation; 2005.
23
Fahimnia F. A survey of Academic Information consertiums in Iran and other countries: The University of Tehran; 2000.
24
Holman L. Millennial Studentsâ Mental Models of Search: Implications for Academic Librarians and Database Developers. The Journal of Academic Librarianship. 2011;37(1):19-27.
25
Hochstotter N, Koch M. Standard parameters for searching behaviour in search engines and their empirical evaluation. Journal of Information Science. 2008;35(1):45-65.
26
Marchionini G, Shneiderman B. Finding facts vs. browsing knowledge in hypertext systems. Computer. 1988;21(1):70-80.
27
Zhang Y, Wang P. Measuring mental models: Rationales and instruments. Proceedings of the American Society for Information Science and Technology. 2005;42(1).
28
ORIGINAL_ARTICLE
Services quality in emergency department of Nemazee Hospital: Using SERVQUAL model
Introduction: Patient satisfaction is crucial to the long-run success in health care center. With regard to the highest patients’ referral to the emergency department and the existing challenges due to the patient’s need to urgent care, we aimed to evaluate health care services quality in this unit to find out whether the patients have different expectations from health care providers and if they perceive some dimensions of care more important than others.Method: The SERVQUAL scale method was used in this cross-sectional study on 100 patients in June 2015. Patient satisfaction questionnaire based on SERVQUAL model was evaluated with high content validity and the reliability was 0.97 and 0.81. The data collected were analyzed using SPSS, version 20.0 (IBM, USA). Statistical analyses included descriptive statistics, paired and independence sample t-test and ANOVA at the significance level 0.05.Results:The results showed that the quality gap in all dimensions was significant (P<0.001). The largest quality gap was related to responsiveness (-1.08) and the lowest belonged to assurance (-0.8). Demographic characteristics were analyzed and the number of referrals was significant in tangibility and assurance dimensions (P = 0.04); also, in all cases the patients’ expectations (total Mean=4.35) were higher than their perception (total Mean = 3.295).Conclusion: In order to improve emergency services, it is recommended that the hospital management should provide appropriate facilities, reduce waiting time, increase in attention to ordering system based on the patients’ condition, and improve the behavior of health care personnel to patient is placed on the agenda of hospital management.Keywords: Management, Quality of service, Emergency department, SERQUAL model
https://jhmi.sums.ac.ir/article_42677_7abe7a355c164aa4d4c673495f343ca0.pdf
2016-10-01
120
126
Maryam
Gholami
ghom5@yahoo.com
1
Manager of Clinical Research Development Center,, Shiraz University of Medical Sciences, Nemazee HospitalShiraz, Iran,postal code: 71937-11351
LEAD_AUTHOR
Zahra
Kavosi
zhr.kavosi@gmail.com
2
AUTHOR
Marziye
khojastefar
3
AUTHOR
Gilbert FW, Dent RP. Adaptation anaâC astomer Expectations of Health Care. 1992.
1
Oyatoye EO, Amole BB, Adebiyi SO. Patientsâ perception of quality service delivery of public hospitals in Nigeria using analytical hierarchy process. Journal of Health Management and Informatics. 2016;3(3):66-73.
2
Ramsaran-Fowdar RR. The relative importance of service dimensions in a healthcare setting. Int J Health Care Qual Assur. 2008;21(1):104-24.
3
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Andaleeb SS. Service quality perceptions and patient satisfaction: a study of hospitals in a developing country. Social Science & Medicine. 2001;52(9):1359-70.
6
Pakdil F, Harwood TN. Patient satisfaction in a preoperative assessment clinic: an analysis using SERVQUAL dimensions. Total Quality Management & Business Excellence. 2005;16(1):15-30.
7
Mirfakhradini H, Mirfakhradini F, Sadr Bafghi M. Investigating rate of iatric tourismsâ satisfaction and prioritizing the effective factors on it via Fuzzy TOPSIS approach. SSU_Journals. 2013;20(5):668-78.
8
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Ibarra LE, Casas V, Partida AL. Adapting the Servqual Scale to a Private Hospital Emergency Services: An Empirical Investigation. Chinese Business Review. 2014;13(5).
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Tan KC, Pawitra TA. Integrating SERVQUAL and Kanoâs model into QFD for service excellence development. Managing Service Quality: An International Journal. 2001;11(6):418-30.
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Rajabipoor Meybodi A, Farid D, Rajabipoor Meybodi T. EVALUATION OF REMEDIAL SERVICES QUALITY OF HOSPITALS BY USING SERVQUAL SCALE (CASE STUDY: EDUCATING HOSPITALS DEPENDENCE TO MEDICINE UNIVERSITY OF YAZD). Journal of Urmia Nursing And Midwifery Faculty. 2009;7(4):0-.
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Jnabady H, Abeli K, Nasti Zayi N, Yaghubi N. The gap between expectations and perception of the quality of health care in health care services provided in health care centers of Zahedan using SERVQUAL model. 2011;10(4):457-49.
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36
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37
ORIGINAL_ARTICLE
The effect of a well-designed computerized physician order entry on medication error reduction
Introduction: Paper-based prescription orders, commonly having numerous medication errors, can increase adverse drug events (ADEs) and threaten the patient’s safety. Computerized physician order entry (CPOE), as an appropriate alternative, can significantly reduce medication errors. This study aimed to investigate the effects of well-designed CPOE in reducing medication errors and ADEs.Method: Electronic databases including EBSCO Host, Web of Science, PubMed, SID, Google Scholar, Iranmedex, Irandoc were used to conduct the literature review. We reviewed all the papers published about CPOE and its impacts on medication errors from 1998 until 2015. Thus 56 articles were found. Considering the relevance of their title and abstract with the objectives of the study, and deleting repetitive cases, 32 articles were selected, among which 10 articles were directly related to the objectives of the study.Results:A number of studies indicate that CPOE can reduce the incidence of serious medication errors and ADEs. Nonetheless, there is evidence indicating that CPOE could negatively affect the patient’s health if the system is not well-designed.Conclusion: The replacement of conventional, paper-based prescription orders with well-designed CPOEs in hospitals could play a key role in minimizing medication errors and improving the patients’ safety. To this end, the CPOEs have to be designed according to recent standards and needs.Keywords: Paper-based Prescriptions, Well-Designed CPOE, Medication Errors.
https://jhmi.sums.ac.ir/article_42678_5e0ae9530df39411ae9e4b86c2740904.pdf
2016-10-01
127
131
Hamid
Moghaddasi
moghaddasi@sbmu.ac.ir
1
LEAD_AUTHOR
Samad
Sajadi
amanzadeh.m@gmail.com
2
AUTHOR
Masoud
Amanzadeh
m.amanzadeh@sbmu.ac.ir
3
AUTHOR
Moghaddasi H, Sheikhtaheri A, Hashemi N. Reducing medication errors: Role of computerized physician order entry system. Journal of Health Administration. 2007;10(27):57-67.
1
Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. Journal of the American Medical Informatics Association. 2013;20(3):470-6.
2
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Colpaert K, Claus B, Somers A, Vandewoude K, Robays H, Decruyenaere J. Impact of computerized physician order entry on medication prescription errors in the intensive care unit: a controlled cross-sectional trial. Critical Care. 2006;10(1):R21.
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Kaushal R. Medication Errors and Adverse Drug Events in Pediatric Inpatients. Jama. 2001;285(16):2114.
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Colpaert K, Decruyenaere J. Computerized physician order entry in critical care. Best Practice & Research Clinical Anaesthesiology. 2009;23(1):27-38.
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Charles K, Cannon M, Hall R, Coustasse A. Can utilizing a computerized provider order entry (CPOE) system prevent hospital medical errors and adverse drug events? Perspectives in Health Information Management. 2014(Fall).
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van Doormaal JE, van den Bemt PMLA, Zaal RJ, Egberts ACG, Lenderink BW, Kosterink JGW, et al. The Influence that Electronic Prescribing Has on Medication Errors and Preventable Adverse Drug Events: an Interrupted Time-series Study. Journal of the American Medical Informatics Association. 2009;16(6):816-25.
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Devine EB, Hansen RN, Wilson-Norton JL, Lawless NM, Fisk AW, Blough DK, et al. The impact of computerized provider order entry on medication errors in a multispecialty group practice. Journal of the American Medical Informatics Association. 2010;17(1):78-84.
16
Kazemi A, Ellenius J, Tofighi S, Salehi A, Eghbalian F, Fors UG. CPOE in IranâA viable prospect?: Physiciansâ opinions on using CPOE in an Iranian teaching hospital. international journal of medical informatics. 2009;78(3):199-207.
17
Aarts J, Koppel R. Implementation Of Computerized Physician Order Entry In Seven Countries. Health Affairs. 2009;28(2):404-14.
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Ozdas A, Miller R. Care provider order entry (CPOE): a perspective on factors leading to success or to failure. Yearbook of medical informatics. 2006:128-37.
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Bates DW. Effect of Computerized Physician Order Entry and a Team Intervention on Prevention of Serious Medication Errors. Jama. 1998;280(15):1311.
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Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon C, et al. Adverse Drug Event Rates in Six Community Hospitals and the Potential Impact of Computerized Physician Order Entry for Prevention. Journal of General Internal Medicine. 2009;25(1):31-8.
21
Mir C, Gadri A, Zelger GL, Pichon R, Pannatier A. Impact of a computerized physician order entry system on compliance with prescription accuracy requirements. Pharmacy World & Science. 2009;31(5):596-602.
22
Mekhjian HS, Kumar RR, Kuehn L, Bentley TD, Teater P, Thomas A, et al. Immediate Benefits Realized Following Implementation of Physician Order Entry at an Academic Medical Center. Journal of the American Medical Informatics Association. 2002;9(5):529-39.
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Spalding SC, Mayer PH, Ginde AA, Lowenstein SR, Yaron M. Impact of computerized physician order entry on ED patient length of stay. The American journal of emergency medicine. 2011;29(2):207-11.
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Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH, editors. An unintended consequence of CPOE implementation: shifts in power, control, and autonomy. AMIA; 2006.
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Ash JS, Sittig DF, Dykstra RH, Guappone K, CarpenterJD, Seshadri V. Categorizing the unintended sociotechnical consequences of computerized provider order entry. international journal of medical informatics. 2007;76:S21-S7.
27
Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH. The Extent and Importance of Unintended Consequences Related to Computerized Provider Order Entry. Journal of the American Medical Informatics Association. 2007;14(4):415-23.
28
Campbell EM, Guappone KP, Sittig DF, Dykstra RH, Ash JS. Computerized Provider Order Entry Adoption: Implications for Clinical Workflow. Journal of General Internal Medicine. 2008;24(1):21-6.
29
Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of Unintended Consequences Related to Computerized Provider Order Entry. Journal of the American Medical Informatics Association. 2006;13(5):547-56.
30
Han YY. Unexpected Increased Mortality After Implementation of a Commercially Sold Computerized Physician Order Entry System. Pediatrics. 2005;116(6):1506-12.
31
Ormond C. Discussion Paper: Computer Physician Order Entry (CPOE). Institute for health policy, Muskie School of public service, Portland, Maine. 2005.
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41
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42
ORIGINAL_ARTICLE
Evaluation of the structure of websites of educational hospitals of Fars province in 2016
Introduction: In the recent years, evaluation of websites has become one of the main organizational instruments for the relationship between provision of services for customers and beneficiaries. Despite the high costs for design and implementation of websites, less attention has been paid to evaluation of these websites’ function. This might be due to the lack of appropriate instruments and frameworks for evaluation of websites. In order to assess hospitals’ information, their websites have to be evaluated regarding webometric criteria so as to identify their strength and weaknesses. Therefore, this study aimed to evaluate the websites of educational hospitals of Fars province in 2016.Method: In this descriptive study, the subjects included all the 43 active websites of educational hospitals of Fars province; they were evaluated by three experts in health information technology. The study data were collected using a checklist whose validity had been confirmed in the previous studies. After all, the data were analyzed using descriptive statistics, SPSS 21 and Excel 2013 software, and the results were presented through tables.Results:The mean scores obtained from three evaluators showed that out of the 43 hospitals under investigation, 35 and 8 hospitals were ranked as good and moderate hospitals, respectively. In addition, the lowest score was related to interactive exchange of views (30.25%), while the highest scores were related to information objectivity (100%), information accuracy (100%), and non-textual views (100%).Conclusion: The overall quality level of most of the hospital was relatively acceptable. But it is necessary that the hospitals improve their websites based on information updatedness, coverage of special addressees, navigation, efficiency and interactive exchange of views. The improvement in the latter criterion will help in reducing the number of daily referrals to the hospitals.Keywords: Evaluation, website, Internet
https://jhmi.sums.ac.ir/article_42679_d5ca3d26742fb65bc05141e09c68a311.pdf
2016-11-19
132
137
Mohtaram
Nematollahi
nematollahi@gmail.com
1
LEAD_AUTHOR
Elham
Fallahnejad
elham.fallahnejad@gmail.com
2
AUTHOR
Fatemeh
Niknam
f_niknam1369@yahoo.com
3
AUTHOR
khadije
Nadri
4
AUTHOR
Fatemeh
khademian
5
AUTHOR
Mofidi M. The evaluation of the state websites of Mazandaran province. National Journal of New Media and Education. 2014;1(2):51.
1
Ghalavand H, Eskrootchi R, Alibeyk M. The Importance of Health Website Assessment Criteria Based on the Opinions of Hospital Librarians. Director General. 2013;9(6).
2
Turner AM, Petrochilos D, Nelson DE, Allen E, Liddy ED. Access and use of the Internet for health information seeking: a survey of local public health professionals in the northwest. J Public Health Manag Pract. 2009 Jan-Feb;15(1):67-9.
3
MORADI GR, Ahmadi M, ZOHOUR A, EBADIFARD AF, Saberi M. Evaluation of Structure and Content of Websites of the Educational Hospitals in Iranâ2007. 2007.
4
Delone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems. 2003;19(4):9-30.
5
Feindt S, Jeffcoate J, Chappell C. Small Business Economics. 2002;19(1):51-62.
6
Ainscough TL. The Effect of Brand, Agent, and Price on Consumer Evaluation of Travel Services. Journal of Travel & Tourism Marketing. 2005;19(1):39-48.
7
Maifredi G, Orizio G, Bressanelli M, Domenighini S, Gasparotti C, Perini E, et al. Italian hospitals on the web: a cross-sectional analysis of official websites. BMC Med Inform Decis Mak. 2010;10:17.
8
Manian A, Sohrabi urtachi B, Shadmehri N. Identify and prioritize factors in evaluating web based Document Analysis Research Papers. Management Research In Iran. 2014;18(1):224.
9
Nadri K, Azizi A. Comparative study of hospital websites in Khuzestan province 2013. Ahwaz Jundishapur University of medical Science; 2016; Available from: http://behsan.ajums.ac.ir/webdocument/load.action?webdocument_code=1000&masterCode=33006691.
10
Vara N, Zeraat kar N, Parto P. Assessment of English language publications, website rankings by the Ministry of Science, Research and Technology and the Ministry of Health and Medical Education. National studies on librarianship and information organization. 2011;88(27):37.
11
Mohamadesmaeil S, Oskouei MN. Interactivity, Changeability, and Non-Textual Content of Websites of Iranian Hospital Libraries. Director General. 2012;9(3).
12
Hamdipour A. Assessment Study of Library Website of Iranian Universities of Medical Sciences and Suggestions for Improvement. Director General. 2011;8(2).
13
ORIGINAL_ARTICLE
Injustice in Access to Health Information: The Difference between Health Professionals and Patients
The role of information is undeniable in promoting public health (1-3). “Access to health information for all” was the slogan of the World Health Organization in 2004 (4). The proving of this slogan requires access to health information by beneficiaries (health professionals and patients). Access to health information by specialists as partly been achieved, but access to health information for patients and their families is considered low (5-7), which could have adverse effects. Health professionals have quick and easy access to information through libraries and medical information centers, participation in seminars, exchange of scientific information with other professionals, as well as identifying ways to effectively access to health information, but patients and their families do not have access to such facilities and capabilities. Therefore, patients and their families are faced with a phenomenon known as “inequity in access to health information” and the continuation of the injustice leads to health information poverty. Thus, the main question now is what we should do? It seems that the government needs to develop a national policy in the field of health information and it is the most important step. In the next step, the government should expand the concept production via using potentials of different organizations like public media (TV and Radio), health ministry and press and increase the access of patients to health information in the easy language (level of health information between health professionals and patients is different).
https://jhmi.sums.ac.ir/article_42675_29bf3c1bd4b2a1ac4c36e48691ec4eb7.pdf
2016-11-19
138
139
Hasan
Ashrafi rizi
1
LEAD_AUTHOR
Fatemeh
Zarmehr
zarmeh2@gmail.com
2
AUTHOR
Adib-Hajbaghery M. Factors facilitating and inhibiting evidence-based nursing in Iran. J Adv Nurs. 2007 Jun;58(6):566-75.
1
Killeen MB, Barnfather JS. A Successful Teaching Strategy for Applying Evidence-based Practice. Nurse Educator. 2005;30(3):127-32.
2
McKenna HP, Ashton S, Keeney S. Barriers to evidence-based practice in primary care. Journal of Advanced Nursing. 2004;45(2):178-89.
3
Aminpour F. Health Information for all: Health Information Management.
4
Akbarinejad F, Soleymani M, Shahrzadi L. [A survey of the relationship between media literacy and health literacy among pregnant women under coverage urban health centers if Isfahan]. Isfahan: Isfahan University of Medical Sciences; 2014.
5
Sadoughi F, Ahmadi M, Gohari M, Rangrez Jeddi F. Attitude of inpatients about information technologies literacy. Journal of Health Administration. 2010;13(40):31-40.
6
Zamani M, Soleymani MR, Afshar M, Shahrzadi L, Zadeh AH. Information-seeking behavior of cardiovascular disease patients in Isfahan University of Medical Sciences hospitals. Journal of education and health promotion. 2014;3.
7
Naseriboor Abadi T. An Introduction to health Information Exchange. Heath Information Management. 2015;12(4):540-53.
8