Newborn Health Data Transfer in the Cape Coast Metropolis, Ghana

  • Emmanuel Kusi Achampong Senior Lecturer, Department of Medical Education and Information Technology, School of Medical Sciences, University of Cape Coast, Ghana
  • Godwin Adzakpah Department of Health Information Management, School of Allied Health Sciences, University of Cape Coast, Ghana
  • Richard Okyere Boadu Department of Health Information Management, School of Allied Health Sciences, University of Cape Coast, Ghana
  • Obed Owumbornyi Lasim Department of Health Information Management, School of Allied Health Sciences, University of Cape Coast, Ghana
  • Richard Opoku Department of Biomedical Sciences, School of Allied Health Sciences, University of Cape Coast, Ghana
Keywords: newborn data, data quality, monitoring and evaluation, health information system


Newborn data is important for decision making and planning purposes. Data transfer from facilities to district, regional and national levels must have high level of quality to benefit from its use for planning and decision making. The objective of this research was to assess the quality and accuracy of newborn health data transfer from facilities to the DHIMS II application. The study was conducted within the Cape Coast Metropolis. Four facilities (two public and two private hospitals) were randomly selected for the study. Facilities registers were compared with summary sheets as well as the data in DHIMS II. The study revealed that there were data inaccuracies across all the indicators ranging from -46.5% to 89.3%. Percentage errors 1 and 3 were extremely high due to the inability of some facilities to produce aggregated forms. Percentage error 2 was generally low for all indicators as compared to percentage errors 1 and 3 except for institutional neonatal deaths with percentage error of 89.3%. The others range from -1.4% to 4.4% which means that there is very little error in transferring the facility register data to the web-based DHIMS-II. The overall percentage errors 1, 2 and 3 in transfer of the data were 7.5% (95% CI = 6.5% to 8.6%), 43.1% (95% CI = 41.8% to 44.3%) and 3.6% (95% CI = 3.2% to 4.0%) respectively. High-quality newborn health is essential for planning and decision making to enhance service quality.


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How to Cite
Achampong, E., Adzakpah, G., Boadu, R. O., Lasim, O., & Opoku, R. (2018). Newborn Health Data Transfer in the Cape Coast Metropolis, Ghana. Journal of Information Sciences and Computing Technologies, 8(1), 744-749. Retrieved from