Digital health infrastructure for strengthening routine health data use through modular automation in Zimbabwe
1
Strategic Information and Research, Organization for Public Health Interventions and Development, Harare, Zimbabwe
Popul. Med. 2026;8(Supplement Supplement 1):A920
ABSTRACT
INTRODUCTION:
Routine health information systems in low- and middle-income countries (LMIC) are frequently constrained by fragmented data flows, manual reporting processes, and limited analytical capacity. These challenges undermine data quality, delay reporting, and restrict the use of routine data for programme learning and decision-making. In Zimbabwe, increasing reporting demands within the HIV programme have intensified the need for integrated, automated, and system-aligned digital solutions.
METHODS:
We conducted an implementation-focused descriptive case study documenting the design, development, and rollout of an AI enabled modular data intelligence platform (MDIP) across 15 districts, in Zimbabwe between January 2023 and June 2024. data sources included routine programme reports, automated dashboards, supervision findings, system usage logs, and structured user feedback. performance indicators assessed reporting timeliness, completeness, data quality, and operational efficiency before and after implementation. reporting was guided by selected domains of the WHO m-ERA checklist (1).
RESULTS:
The platform integrated five interoperable modules supporting automated data diagnostics, analytics, report generation, narrative synthesis, and stakeholder-specific data exports. Across 335 health facilities serving approximately 345000 clients, on-time report submission improved from 27% to 100%. Average data cleaning time declined from 10.2 to 2.9 days, while report preparation time decreased from 5–7 days to under 2 days. Critical data errors related to missing values, disaggregation mismatches, and indicator inconsistencies were eliminated. Districts increasingly used dashboards and automated insights to initiate local data reviews and adjust outreach strategies.
CONCLUSIONS:
Modular, automation-enabled digital infrastructure can substantially improve the efficiency, quality, and use of routine health data when embedded within existing national systems. The MDIP platform demonstrates a scalable, interoperable approach to strengthening data-driven decision-making and learning in resource-constrained health systems (2).
Routine health information systems in low- and middle-income countries (LMIC) are frequently constrained by fragmented data flows, manual reporting processes, and limited analytical capacity. These challenges undermine data quality, delay reporting, and restrict the use of routine data for programme learning and decision-making. In Zimbabwe, increasing reporting demands within the HIV programme have intensified the need for integrated, automated, and system-aligned digital solutions.
METHODS:
We conducted an implementation-focused descriptive case study documenting the design, development, and rollout of an AI enabled modular data intelligence platform (MDIP) across 15 districts, in Zimbabwe between January 2023 and June 2024. data sources included routine programme reports, automated dashboards, supervision findings, system usage logs, and structured user feedback. performance indicators assessed reporting timeliness, completeness, data quality, and operational efficiency before and after implementation. reporting was guided by selected domains of the WHO m-ERA checklist (1).
RESULTS:
The platform integrated five interoperable modules supporting automated data diagnostics, analytics, report generation, narrative synthesis, and stakeholder-specific data exports. Across 335 health facilities serving approximately 345000 clients, on-time report submission improved from 27% to 100%. Average data cleaning time declined from 10.2 to 2.9 days, while report preparation time decreased from 5–7 days to under 2 days. Critical data errors related to missing values, disaggregation mismatches, and indicator inconsistencies were eliminated. Districts increasingly used dashboards and automated insights to initiate local data reviews and adjust outreach strategies.
CONCLUSIONS:
Modular, automation-enabled digital infrastructure can substantially improve the efficiency, quality, and use of routine health data when embedded within existing national systems. The MDIP platform demonstrates a scalable, interoperable approach to strengthening data-driven decision-making and learning in resource-constrained health systems (2).
| eISSN: | 2654-1459 |
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