Improving the quality of in-patient neonatal routine data as a pre-requisite for monitoring and improving quality of care at scale: A multi-site retrospective cohort study in Kenyan hospitals

Objectives The objectives of this study were to (1) determine if membership of a clinical information network (CIN) was associated with an improvement in the quality of documentation of in-patient neonatal care provided over time, and (2) characterise accuracy of prescribing for basic treatments provided to neonatal in-patients if data are adequate. Design and Settings This was a retrospective cohort study involving all children aged ≤28 days admitted to New-Born Units (NBUs) between January 2018 and December 2021 in 20 government hospitals with an interquartile range of annual NBU 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. Outcomes The study’s outcomes were the number of patients as a proportion of all eligible patients with (1) complete domain-specific documentation scores, and (2) accurate domain-specific treatment prescription scores at admission. Findings 80060 NBU admissions were eligible for inclusion. Upon joining the CIN, documentation scores in the monitoring (vital signs) , 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. Conclusions 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 trends in performance to be explored and could support better impact evaluation, performance benchmarking, exploration of links between health system inputs and outcomes and scrutiny of variation in quality and outcomes of hospital care.