In limited resources settings, Health Technology Management (HTM) presents specific challenges, which significantly differ from those faced in higher income settings. In Sub-Saharan Africa (SSA), HTM requires holistic approaches based on reliable information on medical devices operationalized in local medical locations, which may differ significantly from the USA or European ones. Computerized Maintenance Management System (CMMS) tools offer unprecedented opportunities to optimize medical device organization and management in SSA. Nonetheless, CMMS for SSA should be designed to meet real local needs and facing local economic and organizational constraints. This paper describes the results of a project aimed to design and deploy a context-driven CMMS tool, called BGMaint-KM Benin^, which was designed, developed, tested, validated and deployed in the
Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients' hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems' characteristics and the patient physiological parameters' monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter's functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients' care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance.
Background: Because of the health systems globalization, it is important to examine health systems organization in Africa, in terms of patient care, to highlight the failures and propose possible solutions. Objective: Modeling based on the Internet of Things (IoT) an Integrated Network for Monitoring Patient Data in West African Health Systems. Methodology: To achieve this, three steps have been followed. 1) Identification of the different characteristics of IoT-based health surveillance systems, WBAN systems and physiological parameters monitorable on a patient. 2) The modeling of the architecture of West African health systems in the form of a cloud of Technocentres. 3) Cross analysis between different IoT technologies, characteristics and functional requirements identified. All this is based on wireless medical sensor networks in Wireless Body Area Network (WBAN) systems. Result: This work has been used to model health systems in Africa as a remote monitoring network for patients. Conclusion: The implementation of this model of monitoring networks will be a tool for supporting large-scale decision-making for a health system in Africa. It will enable the West African health system to have an information database.
The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, eventually, death. In this regard, several artificial-intelligence-based methods have been proposed based on the detection of contractions using electrohysterogram (EHG) signals. However, the forecasting of pregnancy contractions based on real-time EHG signals is a challenging task. This study proposes a novel model based on neural basis expansion analysis for interpretable time series (N-BEATS) which predicts labour based on EHG forecasting and contraction classification over a given time horizon. The publicly available TPEHG database of Physiobank was exploited in order to train and test the model, where signals from full-term pregnant women and signals recorded after 26 weeks of gestation were collected. For these signals, the 30 most commonly used classification parameters in the literature were calculated, and principal component analysis (PCA) was utilized to select the 15 most representative parameters (all the domains combined). The results show that neural basis expansion analysis for interpretable time series (N-BEATS) forecasting can forecast EHG signals through training after few iterations. Similarly, the forecasting signal’s duration is determined by the length of the recordings. We then deployed XG-Boost, which achieved the classification accuracy of 99 percent, outperforming the state-of-the-art approaches using a number of classification features greater than or equal to 15.
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