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dc.contributor.authorTuti, Timothy
dc.contributor.authorJalemba, Aluvaala
dc.contributor.authorChelangat, Daisy
dc.contributor.authorMbevi, George
dc.contributor.authorWainaina, John
dc.contributor.authorMumelo, Livingstone
dc.contributor.authorWairoto, Kefa
dc.contributor.authorMochache, Dolphine
dc.contributor.authorIrimu, Grace
dc.contributor.authorMaina, Michuki
dc.contributor.authorClinical Information Network Group
dc.contributor.authorEnglish, Mike
dc.date.accessioned2023-06-09T11:54:48Z
dc.date.available2023-06-09T11:54:48Z
dc.date.issued2022
dc.identifier.citationTuti T, Aluvaala J, Chelangat D, Mbevi G, Wainaina J, Mumelo L, Wairoto K, Mochache D, Irimu G, Maina M; Clinical Information Network Group; English M. Improving in-patient neonatal data quality as a pre-requisite for monitoring and improving quality of care at scale: A multisite retrospective cohort study in Kenya. PLOS Glob Public Health. 2022 Oct 20;2(10):e0000673. doi: 10.1371/journal.pgph.0000673. PMID: 36962543; PMCID: PMC10021237.en_US
dc.identifier.urihttps://pubmed.ncbi.nlm.nih.gov/36962543/
dc.identifier.urihttp://erepository.uonbi.ac.ke/handle/11295/163700
dc.description.abstractThe objectives of this study were to (1)explore the quality of clinical data generated from hospitals providing in-patient neonatal care participating in a clinical information network (CIN) and whether data improved over time, and if data are adequate, (2)characterise accuracy of prescribing for basic treatments provided to neonatal in-patients over time. This was a retrospective cohort study involving neonates ≤28 days admitted between January 2018 and December 2021 in 20 government hospitals with an interquartile range of annual neonatal inpatient admissions between 550 and 1640 in Kenya. These hospitals participated in routine audit and feedback processes on quality of documentation and care over the study period. The study's outcomes were the number of patients as a proportion of all eligible patients over time with (1)complete domain-specific documentation scores, and (2)accurate domain-specific treatment prescription scores at admission, reported as incidence rate ratios. 80,060 neonatal admissions were eligible for inclusion. Upon joining CIN, documentation scores in the monitoring, other physical examination and bedside testing, discharge information, and maternal history domains demonstrated a statistically significant month-to-month relative improvement in number of patients with complete documentation of 7.6%, 2.9%, 2.4%, and 2.0% respectively. There was also statistically significant month-to-month improvement in prescribing accuracy after joining the CIN of 2.8% and 1.4% for feeds and fluids but not for Antibiotic prescriptions. Findings suggest that much of the variation observed is due to hospital-level factors. It is possible to introduce tools that capture important clinical data at least 80% of the time in routine African hospital settings but analyses of such data will need to account for missingness using appropriate statistical techniques. These data allow exploration of trends in performance and could support better impact evaluation, exploration of links between health system inputs and outcomes and scrutiny of variation in quality and outcomes of hospital care.en_US
dc.language.isoenen_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleThe African Development Corridors Database: a New Tool to Assess the Impacts of Infrastructure Investmentsen_US
dc.typeArticleen_US


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