Using Health Research for Evidence-Informed Decisions in Health Systems in L&MICs

This chapter describes principles of information management for health systems and the need to focus on key data items required to improve individual and population health. It discusses the collection and analysis of relevant, high-quality data and the importance of agreeing on health program aims before defining the minimum dataset. We review the derivation of health indicators, focusing on WHO indicators. Many indicators rely on linking data from different sources, which requires accurate personal identifiers. Data is useless unless reports based on it can be shared and understood, so data analysts should use different visualization techniques to facilitate and support user decisions such as self-service dashboards. We also review the many high-quality, open source, free-to-use data capture, analysis, and sharing tools that can support health systems, concluding that it is rarely necessary to develop an information system from scratch. Finally, while big data analytics, artificial intelligence, and machine learning capture many headlines, health systems can achieve much using simple tools to capture relevant, high-quality data and turn it into actionable knowledge to support their decision-makers.

a:1:{i:0;s:215:"https://www.cambridge.org/core/books/abs/making-health-systems-work-in-low-and-middle-income-countries/using-health-research-for-evidenceinformed-decisions-in-health-systems-in-lmics/2A1856C7C95BE83C58A0F80487A0C707";}

Sheikh, K., A. Kwamie and A. Ghaffar (2022) Using Health Research for Evidence-Informed Decisions in Health Systems in L&MICs. In Siddiqi, S., A. Mataria, K. D. Rouleau, M. Iqbal (Eds) Making Health Systems Work in Low and Middle Income Countries. Cambridge, United Kingdom of Great Britain and Northern Irelankd: Cambridge University Press.