2021
DOI: 10.1136/bmjgh-2020-004223
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Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review

Abstract: BackgroundRoutine health information system(s) (RHIS) facilitate the collection of health data at all levels of the health system allowing estimates of disease prevalence, treatment and preventive intervention coverage, and risk factors to guide disease control strategies. This core health system pillar remains underdeveloped in many low-income and middle-income countries. Efforts to improve RHIS data coverage, quality and timeliness were launched over 10 years ago.MethodsA systematic review was performed acro… Show more

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Cited by 22 publications
(9 citation statements)
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“…Health information systems in countries across all income levels fall short of their potential to contribute to improving health system performance because of three interrelated factors: technical limitations, including poorly structured data collection tools, limited interoperability, and inadequate investment in maintenance, support, and data privacy567; behavioural factors, such as poor motivation to generate high quality data89; and organisational determinants often arising from weak governance and resource constraints resulting in understaffing, commodity shortages, and limited workforce skills in data management and data use for quality improvement 910. Further, in many settings, health information technologies do not capture important measures of quality of care regularly.…”
Section: Unlocking Potential Of Routine Health Information Systemsmentioning
confidence: 99%
“…Health information systems in countries across all income levels fall short of their potential to contribute to improving health system performance because of three interrelated factors: technical limitations, including poorly structured data collection tools, limited interoperability, and inadequate investment in maintenance, support, and data privacy567; behavioural factors, such as poor motivation to generate high quality data89; and organisational determinants often arising from weak governance and resource constraints resulting in understaffing, commodity shortages, and limited workforce skills in data management and data use for quality improvement 910. Further, in many settings, health information technologies do not capture important measures of quality of care regularly.…”
Section: Unlocking Potential Of Routine Health Information Systemsmentioning
confidence: 99%
“…Furthermore, given the shortages of skilled health workers in areas such as sub-Saharan Africa, where medical education capacities are limited 12 , AIpowered clinical tools could represent one way to increase quantity and quality of medical care 13 . However, current AI applications and machine learning still require large amounts of complete and regularly updated datasets, which still remain scarce for most LMICs 14 . While reports on the application of different AI technologies in LMICs continue to grow, the actual evidence base has so far not been reviewed.…”
Section: Introductionmentioning
confidence: 99%
“…24,25 The use of EHRs has improved the efficiency of service delivery and data quality, reducing gaps along the HIV care continuum and evaluating programs to guide continuous improvement and decision-making. [26][27][28][29] BOX. Zambia Preexposure Prophylaxis Risk-Based Eligibility Criteria (2022) Persons at substantial risk for HIV infection, defined as engaging in one or more of the following activities within the last 6 months, are recommended for preexposure prophylaxis:…”
Section: Implement An Integrated Longitudinal Client-level Monitoring...mentioning
confidence: 99%