Building Reliable Information from Data Generated in Everyday practice - the Data BRIDGE project

Electronic Health Record systems (EHRs) have the potential to improve health data collection and decision-making in sub-Saharan Africa (SSA) for monitoring health system performance, routine maternal, perinatal, and neonatal death surveillance and response (MPNDSR) and for integrated disease surveillance response (IDSR). However, existing EHRs audits reveal that they are challenging and expensive to sustainably implement in SSA. Therefore, in the short-to-medium term, paper-to-digital systems will continue to dominate. This study proposes to improve the quality, volume, and range of data collected through paper-to-digital systems in SSA while reducing the workload of information officers and improving data use culture. This will be achieved by (1) exploring the use of Artificial Intelligence and Machine Learning to develop an efficient, user-friendly clinical informatics pipeline that leverages paper-to-digital systems and (2) identifying how actionable patient-level data from the pipeline can be used for multiple surveillance needs (MPNDSR/IDSR) through optimising Audit and Feedback cycle and mechanisms targeting feedback, recipient and context variables. The initial focus will be on poorly performing neonatal conditions/outcomes exacerbated by global warming. This study aims to bridge the gap between data and information by developing a practical and sustainable solution for improving health data collection and use for decision-making in SSA.