Harnessing routinely collected data for timely healthcare decisions in LMICs amidst pandemics (AUTOMATE)

Research question: Can digital epidemiology enable harnessing routinely collected data for timely healthcare decisions during pandemics in LMICs? Background: The continued rise and re-emergent of global pandemics present significant challenges, especially in low and middle-income countries (LMICs) where health systems have limited capacity for timely identification and monitoring of the potential impact of pandemics. Digital epidemiology, including routinely collected healthcare data, offer great potential in providing evidence required to inform healthcare decisions to address/mitigate potential adverse impact of pandemics. Aims/objectives: The overall aim is to strengthen health systems in LMICs via digitalisation to improve preparedness/responsiveness to current and future pandemics. Specific objectives are to: (i) establish current capacity to use data/evidence for healthcare decisions, using Covid-19 and maternal, new-born and child health (MNCH) outcomes for illustration; (ii) develop digital health and intelligent data analytics/visualization tools to enhance data access/interpretation; and (iii) pilot and assess feasibility of digitalization of healthcare data for timely MNCH decisions. Methods: The study will adopt a comparative case-study design, focusing on two Counties in Western Kenya: Kisumu and Siaya. It will have three interrelated workstreams (WS), aligned with the objectives. WS1: An assessment of current capacity/data quality and protocol development will be followed with illustrative analysis to showcase use of data to inform policy/practice. The illustrative analysis will involve examining the impact of Covid-19 on MNCH care/outcomes and will feature multilevel and causal mediation analysis to enable examination of direct and indirect individual and contextual predictors of MNCH WS2: We will co-develop and implement digital health and data analytics/visualization tools and processes to facilitate data access and use for decision making. Specific activities will include: strengthening routine systems to improve data access; training data/information systems managers on effective processing/analysis and interpretation/use of routine data; and developing/implementing digitalization and data visualisation/dashboards to effectively monitor MNCH care/outcomes WS3: Piloting and evaluating feasibility/process of digitalization of healthcare data. We will adopt a comparative case study approach, involving quantitative and qualitative research, to assess the readiness of Kenyan healthcare system to implement digitalization of healthcare data to facilitate timely decision making. Timelines for delivery: 36 months, starting November 2024 Anticipated impact and dissemination: The proposed participatory approach will help facilitate timely translation of research findings into policy and practice. Findings will inform the development of appropriate data digitalization and visualization/analytics tools to enhance evidence-based decision making for improved performance of the health systems with feedback loops at all levels. The potential impact of the proposed project extends beyond the MNCH outcomes covered in this study and the study setting in Kenya. There is already in place a framework for scaling up to the other regional information systems if proven effective, and findings are likely to inform the development of similar data tools/techniques across the wider healthcare system in Kenya and similar LMIC settings.