The completeness and accuracy of data in the Nigerian health care system is a challenge. Studies have shown that the data quality, and by extension data integrity, has been suboptimal and thus poses a barrier to strengthening service delivery. This article showcases how the design process sparked the concept for an intervention to improve the integrity of public health data being collected in Nigeria.In collaboration with the Nigerian Institute of Medical Research (NIMR) and Lifebank, the Co-creation Hub team conducted formative research with the coronavirus disease (COVID-19) test center managers at NIMR. The insights informed the development of the features for an outbreak management system. These features were refined through an iterative process of development and continuous feedback from the end users.NIMR reported an improvement in its data collection process and data integrity. They reported that (1) almost all data collection by the test center was now automated, thereby minimizing the proportion of inaccurate and repeat entry in comparison to data collected in other parts of the same center; (2) the auto-validation feature of the system ensured that all required fields of a patient’s information were completed and verified, thereby ensuring 100% data completeness; and (3) the validation and verification feature ensured that patients’ contact information was validated.The integration of this intervention into the current health information system ensures an improvement in the accuracy and validity of health care data being collected and stored.
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Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development – In the first wave of the Covid-19 pandemic
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