Objectives Using data to improve care and outcomes is particularly important for developing countries with high burden of disease and inferior health outcomes. However, there is limited use of data in resource-limited settings based on evidence. The aim of this work is to highlight the significant role of data to improve care and outcomes in resource-limited settings and outline the barriers and potential solutions to use data for evidence-based decision-making in developing countries. The study also aims to reflect on a practical example of using real-world data at the Children’s Cancer Hospital 57357 – Egypt (CCHE) and proposes the use of predictive analytics/modelling to improve care delivery and outcomes for children with cancer in Egypt through building learning health systems.
Method We reviewed the literature on the use of health data and analytics to improve care and health outcomes in resource-limited settings, to determine available applications, barriers and potential solutions for implementation in developing countries. We searched on PubMed, Google, and Google Scholar using search terms ‘health data’, ‘data-driven improvements’, ‘big data’, ‘advanced analytic’, ‘resource-limited settings’, ‘developing countries’, ‘improving outcomes’, ‘barriers’, ‘potential solutions’, and ‘EMR’. This was followed by reflection on a practical example of using real world data to improve care delivery and outcomes at CCHE and a proposed approach to use predictive analytics and modelling for evidence-based improvements in care delivery and patients’ outcomes.
Results Initial search showed 53 articles of which 23 were considered relevant and were included. Studies were reviewed for the setting, medical condition, data source, outcomes, barriers to implementation and potential solutions. Limited studies used data to make evidence-based decisions. Some barriers included unavailable data collection modalities, limited information technology investments, lack of national data registries, and cultural resistance. Potential solutions were adopting EMRs for data collection, building hospital-based registries, and cultural change. CCHE is an example for using data to improve care and outcomes for children with cancer in Egypt. CCHE adopts EMR for routine data collection, monitoring and analysis to optimise translation of data into improved clinical practice and better decision-making. CCHE will adopt predictive modelling through forecasting future events and allowing providers to tailor treatments and services accordingly. Applying predictive analytics at CCHE will optimise the use of data for evidence-informed decision-making and building a learning health system.
Conclusions The use of real world data to drive improvements in care delivery and health outcomes is very important in resource-limited settings. Despite current barriers for the optimal use of data to inform evidence-based decisions in developing countries, there are potential solutions that are believed to drive change and help overcome the challenge. A successful example was implemented at the Children’s Cancer Hospital in Egypt with efficient and effective data utilisation for data-driven improvements. Applying predictive modelling at CCHE would be a great step towards translating knowledge into wisdom to make evidence-based decisions based on future predictions of outcomes.
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