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

Research output: Contribution to journalArticleScientificpeer-review

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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
Original languageEnglish
Pages (from-to)23-32
Number of pages10
JournalInternational Journal of Advances in Scientific Research and Engineering
Issue number8
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed


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

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  • Publication forum level 0


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