Development of machine learning-based model for quality measurement in maternal, neonatal and child health services : a country level model for Tanzania

Background: The high maternal and neonatal mortality in developing countries is frequently linked to inadequacies in the quality of maternal, neonatal and child health (MNCH) services provided. Quality measurement is among the recommended strategies for quality improvement in MNCH care. Consequently, developing countries require a novel quality measurement approach that can routinely facilitate the measurement and reporting of MNCH care quality. An effective quality measurement approach can enhance quality measurement and improve the quality of MNCH care. This study intends to explore the effectiveness of approaches available for MNCH quality measurement in developing countries. The study further proposes a machine learningbased approach for MNCH quality measurement. Method: A comprehensive literature search from Pub Med, HINARI, ARDI and Google Scholar electronic databases was conducted. Also, a search for organizations' websites, including World Health Organization (WHO), USAID's MEASURE Evaluation Project, Engender Health, and Family Planning 2020 (FP2020), was included. A search from databases yielded 324 articles, 32 of which met inclusion criteria. Extracted articles were synthesized and presented. Findings: The majority of quality measurement approaches are manual and paper-based. Therefore are laborious, timeconsuming and prone to human errors. Also, it was observed that most approaches are costly since they require trained data collectors and special data sets for quality measurement. It is further noticed that the complexity of the quality measurement process and extra funds needed to facilitate data collection for quality measurement puts an extra burden on developing countries which always face constraints in health budgets. The study further proposes a machine learning-based approach for measuring MNCH quality. In developing this model, financial and human resource constrain were considered. Conclusion: The study found a variety of quality assessment approaches available for quality assessment on MNCH in developing countries. However, the majority of the existing approaches are relatively ineffective. Measuring MNCH quality by a machine learning-based approach could be advantageous and establish a much larger evidence base for MNCH health policies for Tanzania.publishedVersionPeer reviewe

Integrated machine learning based quality measurement model for maternal, neonatal and child health services in Tanzania

A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe high maternal and neonatal mortality rate has remained a challenge for most developing countries. Scholars link the high death occurrences to the poor quality of health services provided to pregnant women and children. It is further revealed that most deaths could be prevented if women and children could access high-quality maternal, neonatal and child health services. Quality measurement, a process of using data to evaluate healthcare plans and performance, is essential in improving the quality of health services and reducing mortality rates. However, most developing countries and Tanzania lack effective approaches to measure and report the quality of Maternal, Neonatal and Child Health services provided. The Lack of an effective quality measurement approach limits the quality measurement processes and may jeopardize the quality measurement results. Additionally, failure to establish the quality of health services hampers healthcare plans and governance of healthcare supplies and other resources. The available quality measurement approaches require trained data collectors, dedicated datasets and the physical presence of quality measurement personnel at each health facility; therefore, labour intensive and resource inefficient. This study proposed and developed an integrated machine learning-based quality measurement model for maternal, neonatal and child health services in Tanzania. The study employed a machine learning technique, a K-means clustering algorithm, and a dataset selected from the national health information system and data warehouse: “District Health Information System (DHIS 2)”. The developed model clustered the Maternal, Neonatal and Child Health (MNCH) dataset into two groups (clusters), and cluster analysis was performed to discover the knowledge about the quality of health services in each cluster formed. The study also performed model validation to establish the usefulness of the developed integrated machine learning-based model for quality measurement in MNCH. This study brings to the body knowledge an integrated machine learning-based quality measurement model for maternal, neonatal and child health services and a list of important indicators for quality measurement, the essential inputs for an effective quality measurement process. The current quality measurement model requires only data to measure the quality of health services readily available in DHIS 2, making the quality measurement model resource-efficient and ideal for quality measurement in resource-constrained countries such as Tanzania