Development of Machine Learning-Based Model for Quality Measurement in Maternal, Neonatal and Child Health Services: A Country  Level Model for Tanzania

Sarah Nyanjara; Dina Machuve,; Pirkko Nykänen

doi:10.31695/IJASRE.2022.8.8.3

Authors

Sarah Nyanjara Nelson Mandela African Institution of Science and Technology - Arusha, Tanzania
Dina Machuve, Nelson Mandela African Institution of Science and Technology - Arusha, Tanzania
Pirkko Nykänen Faculty for Information Technology and Communication Sciences, Tampere University Tanzania

DOI:

https://doi.org/10.31695/IJASRE.2022.8.8.3

Keywords:

Quality Measurement, Maternal and Neonatal Health, Child Health Quality, Quality Health Care, Machine Learning

Abstract

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 learning-based 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, time-consuming 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 that 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.

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Development of Machine Learning-Based Model for Quality Measurement in Maternal, Neonatal and Child Health Services: A Country Level Model for Tanzania

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