Electronic application to improve management of infections in low-income neonatal units: pilot implementation of the NeoTree beta app in a public sector hospital in Zimbabwe

Hannah Gannon, Simbarashe Chimhuya, Gwendoline Chimhini, Samuel R Neal, Liam P Shaw, Caroline Crehan, Tim Hull-Bailey, Rashida A Ferrand, Nigel Klein, Michael Sharland, Mario Cortina Borja, Valerie Robertson, Michelle Heys, Felicity C Fitzgerald, Hannah Gannon, Simbarashe Chimhuya, Gwendoline Chimhini, Samuel R Neal, Liam P Shaw, Caroline Crehan, Tim Hull-Bailey, Rashida A Ferrand, Nigel Klein, Michael Sharland, Mario Cortina Borja, Valerie Robertson, Michelle Heys, Felicity C Fitzgerald

Abstract

There are 2. 4 million annual neonatal deaths worldwide. Simple, evidence-based interventions such as temperature control could prevent approximately two-thirds of these deaths. However, key problems in implementing these interventions are a lack of newborn-trained healthcare workers and a lack of data collection systems. NeoTree is a digital platform aiming to improve newborn care in low-resource settings through real-time data capture and feedback alongside education and data linkage. This project demonstrates proof of concept of the NeoTree as a real-time data capture tool replacing handwritten clinical paper notes over a 9-month period in a tertiary neonatal unit at Harare Central Hospital, Zimbabwe. We aimed to deliver robust data for monthly mortality and morbidity meetings and to improve turnaround time for blood culture results among other quality improvement indicators. There were 3222 admissions and discharges entered using the NeoTree software with 41 junior doctors and 9 laboratory staff trained over the 9-month period. The NeoTree app was fully integrated into the department for all admission and discharge documentation and the monthly presentations became routine, informing local practice. An essential factor for this success was local buy-in and ownership at each stage of the project development, as was monthly data analysis and presentations allowing us to rapidly troubleshoot emerging issues. However, the laboratory arm of the project was negatively affected by nationwide economic upheaval. Our successes and challenges piloting this digital tool have provided key insights for effective future roll-out in Zimbabwe and other low-income healthcare settings.

Keywords: electronic health records; global health; healthcare quality improvement; paediatrics.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Trend in NeoTree ID matches per month throughout the project.
Figure 2
Figure 2
Percentage of babies with their temperature measured at admission throughout the project.
Figure 3
Figure 3
Frequencies of admissions, discharges and deaths per month throughout the project.

References

    1. Chimhini G, Chimhuya S, Madzudzo L, et al. . Auditing use of antibiotics in Zimbabwean neonates. Infection Prevention in Practice 2020;2:100046 10.1016/j.infpip.2020.100046
    1. Crehan C, Kesler E, Nambiar B, et al. . The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi. BMJ Glob Health 2019;4:e000860. 10.1136/bmjgh-2018-000860
    1. Estimation UNI-agfCM Levels and trends in child mortality: report 2020. New York: Fund UNCs, 2020.
    1. Knippenberg R, Lawn JE, Darmstadt GL, et al. . Systematic scaling up of neonatal care in countries. Lancet 2005;365:1087–98. 10.1016/S0140-6736(05)71145-4
    1. Lawn JE, Blencowe H, Oza S, et al. . Every newborn: progress, priorities, and potential beyond survival. Lancet 2014;384:189–205. 10.1016/S0140-6736(14)60496-7
    1. Fitchett EJA, Seale AC, Vergnano S, et al. . Strengthening the reporting of observational studies in epidemiology for newborn infection (STROBE-NI): an extension of the STROBE statement for neonatal infection research. Lancet Infect Dis 2016;16:e202–13. 10.1016/S1473-3099(16)30082-2
    1. Zaidi AKM, Huskins WC, Thaver D, et al. . Hospital-Acquired neonatal infections in developing countries. Lancet 2005;365:1175–88. 10.1016/S0140-6736(05)71881-X
    1. R Core Team R: a language and environment for statistical computing 2018.
    1. Herzog TNS, Winkler WE. Data quality and record linkage techniques. 1st edn New York: Springer-Verlag, 2007.
    1. BSAC Antimicrobial stewardship: from principles to practice, 2018. Available:

Source: PubMed

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