TY - JOUR T1 - Improving Data Integrity in Public Health: A Case Study of an Outbreak Management System in Nigeria JF - Global Health: Science and Practice JO - GLOB HEALTH SCI PRACT SP - S226 LP - S233 DO - 10.9745/GHSP-D-21-00240 VL - 9 IS - Supplement 2 AU - Bosun Tijani AU - Tomi Jaiyeola AU - Busayo Oladejo AU - Zahra Kassam Y1 - 2021/11/29 UR - http://www.ghspjournal.org/content/9/Supplement_2/S226.abstract N2 - Key MessagesThe design of an outbreak management system to automate and streamline data collection and validation at drive-through COVID-19 test centers ensured the fast and efficient data and sample collection and ensured that all data that were collected were accurate and complete.Key ImplicationIntegrating the automated data collection system within the current health information system in Nigeria will help address the challenge of inaccurate, incomplete, and invalid health care data being collected and stored and improve the health system’s capacity to respond to public health emergencies, such as COVID-19.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. ER -