Journal of Health Management and Informatics
Volume:3 Issue: 4, Oct 2016

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.
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.
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.
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.

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.
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).
The Kendall coefficient of Concordance (W) was calculated to be 0.261(P-value 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.

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.
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.
The results showed that the quality gap in all dimensions was significant (P 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.

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.
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.
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.
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.

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).