RESUMO
BACKGROUND: The COVID-19 pandemic has led to serious challenges and emphasized the importance of using technology for health care operational transformation. Consequently, the need for technological innovations has increased, thus empowering patients with chronic conditions to tighten their adherence to medical prescriptions. OBJECTIVE: This study aimed to develop a model for a mobile health (mHealth) self-monitoring system for patients with diabetes in rural communities within resource-limited countries. The developed model could be based on the implementation of a system for the self-monitoring of patients with diabetes to increase medical adherence. METHODS: This study followed a quantitative approach, in which data were collected from health care providers using a questionnaire with close-ended questions. Data were collected from district hospitals in 3 South African provinces that were selected based on the prevalence rates of diabetes and the number of patients with diabetes treated. The collected data were analyzed using smart partial least squares to validate the model and test the suggested hypotheses. RESULTS: Using variance-based structural equation modeling that leverages smart partial least squares, the analysis indicated that environmental factors significantly influence all the independent constructs that inform patients' change of behavior toward the use of mHealth for self-monitoring of medication adherence. Technology characteristics such as effort expectancy, self-efficacy, and performance expectancy were equally significant; hence, their hypotheses were accepted. In contrast, the contributions of culture and social aspects were found to be insignificant, and their hypotheses were rejected. In addition, an analysis was conducted to determine the interaction effects of the moderating variables on the independent constructs. The results indicated that with the exception of cultural and social influences, there were significant interacting effects on other independent constructs influencing mHealth use for self-monitoring. CONCLUSIONS: On the basis of the findings of this study, we conclude that behavioral changes are essential for the self-monitoring of chronic diseases. Therefore, it is important to enhance those effects that stimulate the behavior to change toward the use of mHealth for self-monitoring. Motivational aspects were also found to be highly significant as they triggered changes in behavior. The developed model can be used to extend the research on the self-monitoring of patients with chronic conditions. Moreover, the model will be used as a basic architecture for the implementation of fully fledged systems for self-monitoring of patients with diabetes.
RESUMO
Background: Evidence-based health policies are widely acknowledged as being essential for attaining ongoing improvements in health outcomes. This study represents a theoretical model to test and identify to identify the key drivers for adopting evidence-based healthcare practice (EBHP). Objectives: The goal of this study was to investigate the critical success factors for the adoption of evidence-based healthcare at a South African public hospital. Methods: Medical healthcare professionals were surveyed using a questionnaire developed by the researcher. The results were analysed with SPSS 23 and AMOS. A hypothetical model based on updated (D&M) IS Success Model, Technology, Organization and Environment framework (TOE) as well as Technology Acceptance Model (TAM) was developed and analysed using structural equation modelling (SEM). Results: The findings of this study indicate that EHR is crucial and the main construct influencing the adoption of EBHP. The findings of this study indicated a positive relationship between the construct electronic health records (EHR) with medical error reduction (MER), information quality (IQ) and knowledge quality (KQ), all showed a strong positive correlation towards the adoption of EBHP. Conclusions: Better coordination of patient care (BCP) typically results in lower health care costs. The results also show that using EBHP improves the accuracy of disease diagnosis and treatment, which lowers the burden of disease. Contribution: The developed framework will help healthcare systems that are strapped for money, particularly in developing countries.
