Identifying factors that affect the use of health information technology in the treatment and management of hypertension

BACKGROUND

We conducted this study with the aim of identifying factors that affect the use of health information technology in the treatment and management of hypertension.

METHODS

This paper is a descriptive-analytic study conducted in 2022. To obtain relevant articles, databases including Scopus, Web of Science, IEEE, and PubMed were searched and the time period was between 2013 and 2022. Based on the review of similar articles, a five-point Likert scale checklist was developed in the second phase. The statistical population of the present study was specialist physicians (N = 40) and patients (N = 384). In order to analyze the data, SPSS Statistics 24 was used. To analyze the data obtained from the checklist, we used summary statistics (mean and standard deviation).

RESULTS

As a result of the review literature process, 50 papers were screened, that based we can distinguish motivational and inhibitory factors affecting the use of health information technology in hypertension management. Indeed, Motivational factors and inhibitory factors can be classified into five groups: organizational, economic, technical, personal, and legal/moral factors. Based on the results of the checklist, the factors that were identified as most influential on motivation and inhibitory patients and specialist physicians' to use of health information technology to manage and treat hypertension.

CONCLUSION

Utilizing technologies for hypertension, its management can be improved by identifying motivating and inhibiting factors. Our approach can improve the acceptability of these technologies, save costs, reduce long-term complications of hypertension, and improve patient quality of life.

The effects of electronic nursing handover on patient safety in the general (non-COVID-19) and COVID-19 intensive care units: a quasi-experimental study

BACKGROUND

The unprecedented increase in the nurses' workload is one of the issues affecting the quality and safety of patient care in the Intensive Care Units (ICUs). The electronic nursing handover can share sufficient, relevant, and necessary data about patients with greater efficiency and accuracy and prevent their information from being deleted. Therefore, this study aimed to determine and compare the effect of the Electronic Nursing Handover System (ENHS) on patient safety in General ICU and COVID-19 ICU.

METHOD

This is a quasi-experimental study conducted during an 8-month period from 22 to 2021 to 26 June 2022 using a test-retest design. A total of 29 nurses working in the General and COVID-19 ICUs participated in this study. Data were collected using a five-part questionnaire consisting of demographic information, handover quality, handover efficiency, error reduction, and handover time. Data analysis was conducted in IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, N.Y., USA) using the chi-squared test, paired t-test, and Analysis of Covariance (ANCOVA).

RESULTS

The results showed that the mean scores of handover quality and efficiency, reduction of clinical error, and handover time in the electronic handover were significantly higher than those obtained in the paper-based method. The results showed that the mean score of patient safety in the COVID-19 ICU was 177.40 ± 30.416 for the paper-based handover and 251.40 ± 29.049 for the electronic handover (p = .0001). Moreover, the mean score of patient safety in the general ICU was 209.21 ± 23.072 for the paper-based handover and 251.93 ± 23.381 for the electronic one (p = .0001).

CONCLUSION

The use of ENHS significantly improved the quality and efficiency of shift handover, reduced the possibility of clinical error, saved handover time, and finally increased patient safety compared to the paper-based method. The results also showed the positive perspectives of ICU nurses toward the positive effect of ENHS on the patient safety improvement.

Prediction of Breast Cancer Survival by Machine Learning Methods: An Application of Multiple Imputation

Background:

The low breast cancer survival rates in less developed countries are critical. The machine learning techniques predict cancers survival with high accuracy. Missing data are the most important limitation for using the highest potential of these techniques to predict cancers survival. Multiple imputation (MI) was implemented and analyzed in detail to impute the missing data of a breast cancer dataset.

Methods:

The dataset was from The Omid Treatment and Research Center Urmia, Iran between Jan 2006 and Dec 2012 and had information from 856 women. The algorithms such as C5 and repeated incremental pruning to produce error reduction were applied on the imputed versions of the original dataset and the non-imputed dataset to predict and extract clinical rules, respectively.

Results:

The findings showed the performance of C5 in all the evaluation criteria including accuracy (84.42%), sensitivity (92.21%), specificity (64%), Kappa statistic (59.06%), and the area under the receiver operator characteristic (ROC) curve (0.84), was improved after imputation.

Conclusion:

The dataset of the present study met the requirements for using the multiple imputation method. The extracted rules after the application of MI were more comprehensive and contained knowledge that is more clinical. However, the clinical value of the extracted rules after filling in the missing data did not noticeably increase.

Smartphone-Based Maternal Education for the Complementary Feeding of Undernourished Children Under 3 Years of Age in Food-Secure Communities: Randomised Controlled Trial in Urmia, Iran

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.

Mobile phone applications to overcome malnutrition among preschoolers: a systematic review

Background

Malnutrition is one of the most important reasons for child mortality in developing countries, especially during the first 5 years of life. We set out to systematically review evaluations of interventions that use mobile phone applications to overcome malnutrition among preschoolers.

Methods

The review was conducted and reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses: the PRISMA statement. To be eligible, the study had to have evaluated mobile phone interventions to increase nutrition knowledge or enhance behavior related to nutrition in order to cope with malnutrition (under nutrition or overweight) in preschoolers. Articles addressing other research topics, older children or adults, review papers, theoretical and conceptual articles, editorials, and letters were excluded. The PubMed, Web of Science and Scopus databases covering both medical and technical literature were searched for studies addressing preschoolers’ malnutrition using mobile technology.

Results

Seven articles were identified that fulfilled the review criteria. The studies reported in the main positive signals concerning the acceptance of mobile phone based nutritional interventions addressing preschoolers. Important infrastructural and technical limitations to implement mHealth in low and middle income countries (LMICs) were also communicated, ranging from low network capacity and low access to mobile phones to specific technical barriers. Only one study was identified evaluating primary anthropometric outcomes.

Conclusions

The review findings indicated a need for more controlled evaluations using anthropometric primary endpoints and put relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for mHealth use against malnutrition in LMICs.

A Systematic Review of the Technology Acceptance Model in Health Informatics

BACKGROUND

One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM).

OBJECTIVE

This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction.

METHOD

An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM.

RESULTS

The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered.

CONCLUSION

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.

Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments

OBJECTIVE

Regardless of the acceptance of users, information and communication systems can be considered as a health intervention designed to improve the care delivered to patients. This study aimed to determine the adoption and use of the extended Technology Acceptance Model (TAM2) by the users of hospital information system (HIS) in paraclinical departments including laboratory, radiology, and nutrition and to investigate the key factors of adoption and use of these systems.

MATERIALS AND METHODS

A standard questionnaire was used to collect the data from nearly 253 users of these systems in paraclinical departments of eight university hospitals in two different cities of Iran. A total of 202 questionnaires including valid responses were used in this study (105 in Urmia and 97 in Khorramabad). The data were processed using LISREL and SPSS software and statistical analysis technique was based on the structural equation modeling (SEM).

RESULTS

It was found that the original TAM constructs had a significant impact on the staffs' behavioral intention to adopt HIS in paraclinical departments. The results of this study indicated that cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use), except for result demonstrability, were significant predictors of intention to use, whereas the result revealed no significant relationship between social influence processes (subjective norm, voluntariness, and image) and the users' behavioral intention to use the system.

CONCLUSION

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.

Application of Canonical Correlation Analysis for Detecting Risk Factors Leading to Recurrence of Breast Cancer

BACKGROUND

Advances in treatment options of breast cancer and development of cancer research centers have necessitated the collection of many variables about breast cancer patients. Detection of important variables as predictors and outcomes among them, without applying an appropriate statistical method is a very challenging task. Because of recurrent nature of breast cancer occurring in different time intervals, there are usually more than one variable in the outcome set. For the prevention of this problem that causes multicollinearity, a statistical method named canonical correlation analysis (CCA) is a good solution.

OBJECTIVES

The purpose of this study was to analyze the data related to breast cancer recurrence of Iranian females using the CCA method to determine important risk factors.

PATIENTS AND METHODS

In this cross-sectional study, data of 584 female patients (mean age of 45.9 years) referred to Breast Cancer Research Center (Tehran, Iran) were analyzed anonymously. SPSS and NORM softwares (2.03) were used for data transformation, running and interpretation of CCA and replacing missing values, respectively. Data were obtained from Breast Cancer Research Center, Tehran, Iran.

RESULTS

Analysis showed seven important predictors resulting in breast cancer recurrence in different time periods. Family history and loco-regional recurrence more than 5 years after diagnosis were the most important variables among predictors and outcomes sets, respectively.

CONCLUSIONS

Canonical correlation analysis can be used as a useful tool for management and preparing of medical data for discovering of knowledge hidden in them.

Prediction of breast cancer survival through knowledge discovery in databases

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.