ABSTRACT
BACKGROUND: Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. OBJECTIVE: The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. METHODS: Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. RESULTS: Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. CONCLUSIONS: mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.
Subject(s)
Physicians , Telemedicine , Humans , Ethiopia , Qualitative Research , Biomedical TechnologyABSTRACT
Objectives: Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis. Methods: The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose. Results: The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use. Conclusions: With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.
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Introduction: The advent of digital systems and global mobile phone availability presents an opportunity for better healthcare access and equity. However, the disparity in the usage and availability of mHealth systems between Europe and Sub-Saharan Africa (SSA) has not been explored in relation to current health, healthcare status, and demographics. Objective: This study aimed to compare mHealth system availability and use in SSA and Europe in the above-mentioned context. Methods: The study analyzed health, healthcare status, and demographics in both regions. It assessed mortality, disease burden, and universal health coverage. A systematic narrative review was conducted to thoroughly assess available data on mHealth availability and use, guiding future research in the field. Results: SSA is on the verge of stages 2 and 3 in the demographic transition with a youthful population and high birth rate. Communicable, maternal, neonatal, and nutritional diseases contribute to high mortality and disease burden, including child mortality. Europe is on the verge of stages 4 and 5 in the demographic transition with low birth and death rates. Europe's population is old, and non-communicable diseases (NCDs) pose major health challenges. The mHealth literature adequately covers cardiovascular disease/heart failure, and cancer. However, it lacks approaches for respiratory/enteric infections, malaria, and NCDs. Conclusions: mHealth systems in SSA are underutilized than in Europe, despite alignment with the region's demographics and major health issues. Most initiatives in SSA lack implementation depth, with only pilot tests or small-scale implementations. Europe's reported cases highlight actual implementation and acceptability, indicating a strong implementation depth of mHealth systems.
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Background: mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between sub-Saharan Africa and Europe, despite the ongoing digitalization of the global healthcare system. Objective: This work aims to compare and investigate the use and availability of mHealth systems in sub-Saharan Africa and Europe, and identify gaps in current mHealth development and implementation in both regions. Methods: The study adhered to the PRISMA 2020 guidelines for article search and selection to ensure an unbiased comparison between sub-Saharan Africa and Europe. Four databases (Scopus, Web of Science, IEEE Xplore, and PubMed) were used, and articles were evaluated based on predetermined criteria. Details on the mHealth system type, goal, patient type, health concern, and development stage were collected and recorded in a Microsoft Excel worksheet. Results: The search query produced 1020 articles for sub-Saharan Africa and 2477 articles for Europe. After screening for eligibility, 86 articles for sub-Saharan Africa and 297 articles for Europe were included. To minimize bias, two reviewers conducted the article screening and data retrieval. Sub-Saharan Africa used SMS and call-based mHealth methods for consultation and diagnosis, mainly for young patients such as children and mothers, and for issues such as HIV, pregnancy, childbirth, and child care. Europe relied more on apps, sensors, and wearables for monitoring, with the elderly as the most common patient group, and the most common health issues being cardiovascular disease and heart failure. Conclusion: Wearable technology and external sensors are heavily used in Europe, whereas they are seldom used in sub-Saharan Africa. More efforts should be made to use the mHealth system to improve health outcomes in both regions, incorporating more cutting-edge technologies like wearables internal and external sensors. Undertaking context-based studies, identifying determinants of mHealth systems use, and considering these determinants during mHealth system design could enhance mHealth availability and utilization.
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BACKGROUND: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result from the visual interpretation has a significant contribution to unnecessary surgical delivery or delayed intervention. OBJECTIVE: In the current study an innovative computer aided fetal distress diagnosing model is developed by using time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning of pre-trained ResNet 50 deep neural network model. METHOD: From the CTG data that is obtained from the only open access CTU-UHB data base only FHR signal is extracted and preprocessed to remove noises and spikes. After preprocessing the time frequency information of FHR signal is extracted by using generalized Morse wavelet and fed to a pre-trained ResNet 50 model which is fine tuned and configured according to the dataset. MAIN OUTCOME MEASURES: Sensitivity (Se), specificity (Sp) and accuracy (Acc) of the model adopted from binary confusion matrix is used as outcome measures. RESULT: After successfully training the model, a comprehensive experimentation of testing is conducted for FHR data for which a recording is made during early stage of labor and last stage of labor. Thus, a promising classification result which is accuracy of 98.7%, sensitivity of 97.0% and specificity 100% are achieved for FHR signal of 1st stage of labor. For FHR recorded in last stage of labor, accuracy of 96.1%, sensitivity of 94.1% and specificity 97.7% are achieved. CONCLUSION: The developed model can be used as a decision-making aid system for obstetrician and gynecologist.
