Abstract:
The 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