Tracking health system performance in times of crisis using routine health data: lessons learned from a multicountry consortium
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Health Research Policy and Systems
Abstract
COVID-19 has prompted the use of readily available administrative data to track health system performance in times
of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience
working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health Sys‑
tem Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to
assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic,
Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common
health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of
the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the
indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health
services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume
of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets
were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or
population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional
care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of
administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health condi‑
tions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and
health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and
expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health
information technologies; reduce delays and loosen restrictions for researchers to access the data; complement
routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of
service disruptions.
Description
Research Article