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Background Access to quality medicines is a global initiative to ensure universal health coverage. However, the limited capacity of National Medicines Regulatory Authorities (NMRAs) to prevent and detect the supply of poor-quality medicines led to the predominance of sub-standard and falsified (SF) medicines in the supply chains of many countries. Therefore, this study was designed to assess the capacity of a young NMRA to ensure the quality of medicines with Rwanda as a case study. Objective This study aimed to assess the capacity of the Rwanda FDA, a young NMRA, to identify gaps and existing opportunities for improving regulatory capacity and ensuring the quality of medicines. Methods This study used a descriptive cross-sectional design with both quantitative and qualitative approaches. The quantitative approach used a self-administered questionnaire to collect data from employees of Rwanda FDA who are involved in medicine regulatory practices based on their positions while the qualitative research approach covered a desk review of key regulatory documents. The data collection tool was developed from the World Health Organization (WHO) Global Benchmarking Tool (GBT) for “Evaluation of National Regulatory System of Medical Products Revision VI”. Results Of the 251 WHO sub-indicators assessed, 179 sub-indicators (71%) were fully implemented, 17 sub-indicators (7%) were partially implemented, 9 sub-indicators (4%) were ongoing and 46 sub-indicators (18%) were not implemented by the time of the study. The results of the study showed that the estimated maturity level at which Rwanda FDA operates is maturity level 2. The study reported the challenges hindering the implementation of key regulatory functions that need to be addressed. Challenges reported include but are not limited to understaffing, lack of automation system, poor implementation of the quality management system, lack of screening technologies for SF medicines, low capacity of the quality control laboratory to test all sampled medicines and lack of regulatory inspection tools/equipment. Conclusion Findings indicated that all key regulatory functions were operating and supported by the legal framework. However, the implementation of key regulatory functions faced challenges that need to be addressed for better organizational effectiveness and compliance with the requirements of a higher maturity level.

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Background Family planning involves the use of traditional or modern methods to prevent maternal and infant mortality associated with unintended pregnancies and negative economic outcomes. In sub-Saharan Africa, the unmet need for modern family planning is approximately 66%. However, information on factors affecting utilization of female family planning commodities is limited. Therefore, this research was conducted to bridge this gap. Methods Health facility-based descriptive cross-sectional research design was conducted and involved the public health facilities offering family planning, targeting respondents who handle the commodities and service providers themselves. A semi-structured questionnaire was used to collect data about availability of the commodities, knowledge of service providers and barriers affecting provision of the service. Data were coded and analyzed via Microsoft Excel 2019 and SPSS version 20. Results The study showed that shorter term methods were more readily available, 60–75% than the long-term methods, 20–60%. Approximately 60% of the service providers did not comprehensively utilize the recommended World Health Organization Medicine Eligibility Criteria (WHO MEC) during service provision. Stock outs, myths and misconceptions, male interference and culture were the major barriers identified. Conclusion Provision of family planning commodities in public health facilities in Kajiado county is affected by stock levels at the national program, and provider knowledge on WHO MEC. The key factors affecting provision of family planning were stock outs, myths and misconceptions on the contraceptives, inadequate male involvement and inadequate community engagement on potential benefits of the service. These challenges need to be part of the solutions to bridging the gap identified.

Today’s global business trends are causing a significant and complex data revolution in the healthcare industry, culminating in the use of artificial intelligence and predictive modeling to improve health outcomes and performance. The dataset, which was referred to is based on consumption data from 2015 to 2019, included approximately 500 goods. Based on a series of data pre-processing activities, the top ten (10) essential medicines most used were chosen, namely cotrimoxazole 480 mg, amoxicillin 250 mg, paracetamol 500 mg, oral rehydration salts (O.R.S) sachet 20.5 g, chlorpheniramine 4 mg, nevirapine 200 mg, aminophylline 100 mg, artemether 20 mg + lumefantrine (AL) 120 mg, Cromoglycate ophthalmic. Our study concentrated on the application of machine learning (ML) to forecast future trends in the demand for essential drugs in Rwanda. The following models were created and applied: linear regression, artificial neural network, and random forest. The random forest was able to predict 10 selected medicines with an accuracy of 88 percent with the train set and 76 percent with the test set, and it can thus be used to forecast future demand based on past consumption data by inputting a month, year, district, and medicine name. According to our findings, the random Forest model performed well as a forecasting model for the demand for essential medicines. Finally, data-driven predictive modeling with machine learning (ML) could become the cornerstone of health supply chain planning and operational management.