An overview of patient acceptance of Health Information Technology in developing countries: a review and conceptual model

The potential to improve the quality, efficiency, outcomes, patient safety and reduce cost of healthcare by Health Information Technology (HIT) has been established by researchers. But unfortunately HIT systems are not properly utilized or are not widely available. This problem is even more glaring in developing countries. This article presents a review of some available HIT systems in order to assess the level of their presence and the technology used in developing them. Works related to acceptance of HIT systems were also reviewed so as to study the gaps in this area and propose a solution in order to fill the gaps identified. The problems discovered from this review include lack of availability of these systems especially in developing countries, low rate of HIT systems acceptance and insufficient works on patient acceptance of HIT systems. Studying the factors that affect the acceptance of HIT systems by patients and considering the factors while developing the systems will play a significant role in getting over the aforementioned limitations. As Technology Acceptance Model (TAM) is one of the most popular models for studying users' perception and acceptance of Information System (IS)/Information Technology (IT), we proposed a conceptual model of HIT acceptance in developing countries based on TAM

An overview of patient acceptance of Health Information Technology in developing countries: a review and conceptual model

The potential to improve the quality, efficiency, outcomes, patient safety and reduce cost of healthcare by Health Information Technology (HIT) has been established by researchers. But unfortunately HIT systems are not properly utilized or are not widely available. This problem is even more glaring in developing countries. This article presents a review of some available HIT systems in order to assess the level of their presence and the technology used in developing them. Works related to acceptance of HIT systems were also reviewed so as to study the gaps in this area and propose a solution in order to fill the gaps identified. The problems discovered from this review include lack of availability of these systems especially in developing countries, low rate of HIT systems acceptance and insufficient works on patient acceptance of HIT systems. Studying the factors that affect the acceptance of HIT systems by patients and considering the factors while developing the systems will play a significant role in getting over the aforementioned limitations. As Technology Acceptance Model (TAM) is one of the most popular models for studying users\u27 perception and acceptance of Information System (IS)/Information Technology (IT), we proposed a conceptual model of HIT acceptance in developing countries based on TAM

Willingess To Use Telehealth For Diabetes Management In The Rural Healthcare Setting

Diabetes is a disease that has far reaching physically and financially consequences. This disease has shown to increase morbidity and mortality, along with increasing overall healthcare costs. Optimal management of diabetes may require a multidisciplinary approach across the span of multiple encounters with the diabetic patient. Considering the nature of diabetes, integration of telehealth has the opportunity to improve diabetes management by improving healthcare outcomes, along with potential cost savings. However, the use of novel technology like telehealth is only as useful if patients are willing to use it. Thus, this survey aimed to determining whether patients within a rural family medicine clinic in Central California would be willing to use telehealth if it were made available. Findings of this study indicated that age, smartphone ownership, and having Internet access were factors that determined whether participants were likely or not to use telehealth. Identifying determining factors provided an initial data set of the target population regarding willingness to use telehealth if made available. This data set has the potential to be used to design a potential telehealth program that would be tailor-made to cater to the preferences of patients who attend the rural family medicine clinic in Central California for care. This study provided the foundation for a possible telehealth program in the future at the site of this study

Wearable Medical Devices in Use: A Study of Insulin Pump Adoption by Young Diabetic Patients In Saudi Arabia

his research belongs to the multi-disciplinary research community concerned with wearable medical technology and branches of sociology and psychology that study its impact. It addresses a real-life problem of Insulin Pump (IP) adoption by Children. This is important for Saudi Arabia, since it is among the top five countries in the world with the highest rate of diabetes. Theories of reasoned action (TRA), technology acceptance model (TAM) and health belief models (HBM) for some of the cases predict that the perception of benefits is the main motivator for the proper use of the technology. This is often not realised in practice, because the main theoretical focus is on the benefits of IP, specifically in the pre-adoption phase. In contrast, this research project is focused on the reasons why some diabetic children patients misuse the IP in spite of the initial perception of its benefits. To find answers to this research question, an empirical study of adoption of IP by children and young adults in Saudi Arabia was carried out. A novel analytical framework was developed in this study in order to unify different perspectives and expectations of the benefits of the IP for a diabetic child and young adult. The analytic framework is applied using empirical study of diabetic children struggling with the IP in the course of the adoption process, with main emphasis on the post-adoption phase. Research methods were predominantly qualitative, involving in-depth interviews and case studies. In the discovery phase, data was collected through interviews of medical personnel and case studies with children and their parents. The analysis was focused on different interactions between medical personnel, patients and their caregivers, the discourses among them in order to explicate the contradictions between them. The main findings are that contradictions show different expectations between the different actors. The medical personnel used medical reasons, whereas the caregiver focus on emotional aspects. However, the diabetic child was concerned with the life-style changes that the use of the IP caused. The different motivations create misunderstandings and result in resistance towards the IP. Age-related and culture-specific factors were also considered, but further research is needed to ensure that the findings can be generalised to other devices, age-groups, cultures and different social contexts. Such studies would also refine the analytical framework and enrich research methodology to make generalisations possible

Diagnosing Patients and Recommending mHealth Technology? Exploring Physicians' Intention to Influence Patients' Use of Self-Health Management Technology

This paper introduces a new type of IT role, IT influencers. We define IT influencers as persons whose decision-making is critical but who do not directly use the focal technology. Then we contextualize the social role of IT influencers within the unified theory of acceptance and use of technology (UTAUT) framework to explore the conditions under which such individuals demonstrate IT-directed social behavior, termed intention to influence and become a social influence upon the targeted user’s technology use. We look at physicians, as IT influencers, and chronic diabetic patients, as IT users, who work together to promote patients' self-management of chronic diabetes using mobile health (mHealth) technology. The results demonstrated that physicians' evaluation of both IT and patients' technical ability led to intention to influence patients' use of mHealth technology. Furthermore, intent to influence is promoted in a social context in which supporting resources are available for both IT users

The relationship between illness perception and medication adherence in patients with diabetes mellitus type II: illness perception and medication adherence

Introduction: One of the most well-known chronic diseases in the world is diabetes. Disease perception is the patient's organized cognitive representation of his or her illness and can affect treatment adherence. The aim of this study was to investigate the relationship between illness perception and adherence to the medical regimen in patients with type II diabetes. Methods: This descriptive-analytical cross-sectional study was performed on 260 patients with type II diabetes referred to Gonabad Diabetes Clinic by systematic random sampling in 2019. Data collection tools were demographic questionnaire, Morisky medication Adherence Scale (MMAS-8), and Brief illness Perception Questionnaire (BIPQ). Data were analyzed by SPSS 20 software. And using descriptive statistics, Pearson correlation coefficient.  P < 0.05 was considered significant. Results: The results showed that the mean score of illness perception of type II diabetes was 46.39 ± 9.45 (range 0-70) and the mean score of medication Adherence was 2.93 ± 1.9 (range 0-8). The results of Pearson correlation test showed a significant relationship between illness perception and medication Adherence (P <0.001, r = 0.199). Also, the regression model showed that the dimensions of disease comprehension and personal control from illness perception were significantly related to medication Adherence of type II diabetic patients (P <0.001). Conclusion: By measuring the level of illness perception, the degree of medication Adherence can be predicted. Therefore, strengthening the illness perception in order to medication Adherence seems to be an important therapeutic strategy in educational interventions.     &nbsp

The experiences of people with diabetes during covid-19 pandemic lockdown

Little is known about the theoretical foundation underling the response of people with diabetes managing their everyday routines during COVID-19 pandemic lockdown. Aim: To explore the experience of people with diabetes during COVID-19 pandemic lockdown in light of the risk perception, response and behavioral change theories. Method: A qualitative descriptive design was employed, and Braun and Clark’s six step analysis were used for thematic analysis. Semi-structured interviews were conducted online using Zoom Videos Communication. Result: Five themes were defined as follows: (1) perceived the threat and faced their fears, (2) appraised the damage, (3) identified the challenges, (4) modified their routine, and (5) identified the strengths that facilitate the efficacy of their response. There were eight sub-themes within the themes. Conclusion: The results of this study may provide an opportunity for nurses to reflect on issues highlighted by the patients regarding more effective communication, knowledge and skill development for people to support self-care during national emergencies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics