Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.
The results confirmed that several factors in the TAM2 that were important in previous studies were not significant in paraclinical departments and in government-owned hospitals. The users' behavior factors are essential for successful usage of the system and should be considered. It provides valuable information for hospital system providers and policy makers in understanding the adoption challenges as well as practical guidance for the successful implementation of information systems in paraclinical departments.
The collection of large volumes of medical data has offered an opportunity to develop prediction models for survival by the medical research community. Medical researchers who seek to discover and extract hidden patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to predict the outcome of a disease. The study was conducted to develop predictive models and discover relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics 16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to prediction of breast cancer survival. Among important variables, behavior of tumor as the most important variable and stage of malignancy as the least important variable were identified. In current study, applying of the knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer patients with high confidence and identified the most important variables participating in breast cancer survival.
The mothers’ nutritional literacy is an important determinant of child malnourishment. We assessed the effect of a smartphone-based maternal nutritional education programme for the complementary feeding of undernourished children under 3 years of age in a food-secure middle-income community. The study used a randomised controlled trial design with one intervention arm and one control arm (n = 110; 1:1 ratio) and was performed at one well-child clinic in Urmia, Iran. An educational smartphone application was delivered to the intervention group for a 6-month period while the control group received treatment-as-usual (TAU) with regular check-ups of the child’s development at the well-child centre and the provision of standard nutritional information. The primary outcome measure was change in the indicator of acute undernourishment (i.e., wasting) which is the weight-for-height z-score (WHZ). Children in the smartphone group showed greater wasting status improvement (WHZ +0.65 (95% Confidence Interval (CI) ± 0.16)) than children in the TAU group (WHZ +0.31 (95% CI ± 0.21); p = 0.011) and greater reduction (89.6% vs. 51.5%; p = 0.016) of wasting caseness (i.e., WHZ < −2; yes/no). We conclude that smartphone-based maternal nutritional education in complementary feeding is more effective than TAU for reducing undernourishment among children under 3 years of age in food-secure communities.
Background: Assessment of hospital information system (HIS) service quality helps to meet the needs of users and a strategy to expand the interaction between HIS developers and the users. SERVQUAL is an extensively used technique to measure the service quality of information systems.
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