Using mobile health in primiparous women: effect on awareness, attitude and choice of delivery type, semi-experimental.

BACKGROUND: One of the reasons for the increase in cesarean section is the lack of knowledge of mothers in choosing the type of delivery. The present study aimed to determine the effect of education through pregnancy application during pregnancy on awareness and attitude and choice of delivery type in primiparous women at Shahid Alavi Specialized Medical Center clinic in Mashhad. METHODS: This study was a semi-experimental type. Seventy primiparous pregnant women who had no restrictions for birth was selected. Sampling was randomized and purpose-based. The data collection tool was a questionnaire. The starting time of the training was considered from the end of the 27th week of pregnancy and continued with regular intervals until the end of the pregnancy. The questionnaires were completed once before the training and once after the training as a pre-test and post-test by the research units. SPSS software (version 26) and MacNemar test and descriptive statistics were used for data analysis. RESULTS: According to the results of the MacNemar test, a significant statistical difference was seen between women's knowledge and attitude after the training compared to before (p-value < 0.01). Choosing the type of birth was preferred before and after the training. But following the performance of women showed that only 62.86% of them chose natural birth. In fact, before the training, 40% and after 72.86% of women had chosen birth. CONCLUSION: Pregnancy education and application during pregnancy is effective in reducing the choice of cesarean section, so this application with the topic of birth and cesarean section has improved the level of attitude and also the positive attitude of pregnant women towards birth. Creation of facilities and promotion of different methods of painless childbirth and training of maternity staff to perform birth is expected.

Predicting coronary artery disease: a comparison between two data mining algorithms.

BACKGROUND: Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals. METHODS: The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing. RESULTS: The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test's result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model. CONCLUSION: According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.

Hospital Real-Time Location System (A Practical Approach in Healthcare): A Narrative Review Article.

BACKGROUND: The Hospital Real-time Location Systems (HRTLS), deal with monitoring the patients, medical staff and valuable medical equipment in emergency situations. Therefore, the study aimed to propose Hospital Real-Time Location Systems based on the novel technologies in Iran. METHODS: In this narrative-review, the articles and official reports on HRTLS, were gathered and analyzed from related textbooks and indexing sites with the defined keywords in English or Persian. The search of databases such as IDTechEx, IEEE, PubMed Central, Science Direct, EMBASE/Excerpta Medica, Scopus, Web of Science, Elsevier journals, WHO publications and Google Scholar was performed to reconfirm the efficiency of HRTLS from 2006 to 2017. RESULTS: Various technologies have been used in the current systems, which have led to the reduced error rate, costs and increased speed of providing the healthcare services. Applications of these systems include tracking of patient's, medical staff and valuable medical assets. Besides, achieving the patient & staff satisfaction is among other basic applications of these Systems. The accurate data exchange and processes control are considered as positive aspects of this technology. CONCLUSION: HRTLS has great importance in healthcare systems and its efficiency in medical centers is reliable; hence, it seems necessary to determine the organization's requirements, apply novel technologies such as cloud computing and Internet of things, and integrate them to get access to maximum advantages in Iranian healthcare centers.

The design and application of an e-health readiness assessment tool.

BACKGROUND: To date, several models have been developed to evaluate e-health readiness in healthcare organisations; however, no specific tool has been designed to assess or measure e-health readiness. OBJECTIVE: The aim of this research was to design an e-health readiness assessment tool and to apply this tool to two Iranian hospitals to assess their readiness for the implementation of e-health. METHOD: The study, which was undertaken in 2012, consisted of three phases: (i) review of existing models of e-health; (ii) design of an e-health readiness assessment tool; and (iii) trial of the assessment tool in two Iranian hospitals. Phase 1 consisted of a literature review that informed the development of the tool. In phase 2, we developed an e-health readiness assessment tool with feedback using two questionnaires from 40 employees from two teaching hospitals in Iran. In phase 3, we applied the tool to the same two Iranian teaching hospitals. Participants who completed the questionnaires were from management, health information technology, medical and nursing backgrounds and were familiar with e-health. A purposive sampling method was used to invite them to take part in the study. Data from the questionnaires were analysed using factor analysis and descriptive statistics. RESULTS: Five dimensions and twenty-one indices were selected to be included in the e-health readiness tool. The 5 dimensions and their relative importance were e-health readiness (16%), information and communication technology (ICT) functions (15%), environmental readiness (20%), human resources readiness (29%) and ICT readiness (20%). The total e-health readiness scores for hospital A and hospital B were 0.22 and 0.4, respectively (a score of 1 is the ideal). CONCLUSION: It is important to assess the e-health readiness of hospitals to save time and money and be able to better prepare for ICT implementations. The e-health readiness assessment tool provides a relatively simple method for assessing hospitals and provides essential information to assist healthcare facilities focus preparations and planning for e-health implementations.