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      Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree

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          Abstract

          Introduction

          Improving peri‐ and postnatal facility‐based care in low‐resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost‐effective, simple, evidence‐based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high‐resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS.

          Methods

          Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co‐develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low‐cost hardware and state‐of‐the‐art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree.

          Results

          Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID‐19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects.

          Conclusion

          Human‐centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

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          Most cited references51

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Evidence-based, cost-effective interventions: how many newborn babies can we save?

            In this second article of the neonatal survival series, we identify 16 interventions with proven efficacy (implementation under ideal conditions) for neonatal survival and combine them into packages for scaling up in health systems, according to three service delivery modes (outreach, family-community, and facility-based clinical care). All the packages of care are cost effective compared with single interventions. Universal (99%) coverage of these interventions could avert an estimated 41-72% of neonatal deaths worldwide. At 90% coverage, intrapartum and postnatal packages have similar effects on neonatal mortality--two-fold to three-fold greater than that of antenatal care. However, running costs are two-fold higher for intrapartum than for postnatal care. A combination of universal--ie, for all settings--outreach and family-community care at 90% coverage averts 18-37% of neonatal deaths. Most of this benefit is derived from family-community care, and greater effect is seen in settings with very high neonatal mortality. Reductions in neonatal mortality that exceed 50% can be achieved with an integrated, high-coverage programme of universal outreach and family-community care, consisting of 12% and 26%, respectively, of total running costs, plus universal facility-based clinical services, which make up 62% of the total cost. Early success in averting neonatal deaths is possible in settings with high mortality and weak health systems through outreach and family-community care, including health education to improve home-care practices, to create demand for skilled care, and to improve care seeking. Simultaneous expansion of clinical care for babies and mothers is essential to achieve the reduction in neonatal deaths needed to meet the Millennium Development Goal for child survival.
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              Stillbirths: rates, risk factors, and acceleration towards 2030

              An estimated 2.6 million third trimester stillbirths occurred in 2015 (uncertainty range 2.4-3.0 million). The number of stillbirths has reduced more slowly than has maternal mortality or mortality in children younger than 5 years, which were explicitly targeted in the Millennium Development Goals. The Every Newborn Action Plan has the target of 12 or fewer stillbirths per 1000 births in every country by 2030. 94 mainly high-income countries and upper middle-income countries have already met this target, although with noticeable disparities. At least 56 countries, particularly in Africa and in areas affected by conflict, will have to more than double present progress to reach this target. Most (98%) stillbirths are in low-income and middle-income countries. Improved care at birth is essential to prevent 1.3 million (uncertainty range 1.2-1.6 million) intrapartum stillbirths, end preventable maternal and neonatal deaths, and improve child development. Estimates for stillbirth causation are impeded by various classification systems, but for 18 countries with reliable data, congenital abnormalities account for a median of only 7.4% of stillbirths. Many disorders associated with stillbirths are potentially modifiable and often coexist, such as maternal infections (population attributable fraction: malaria 8.0% and syphilis 7.7%), non-communicable diseases, nutrition and lifestyle factors (each about 10%), and maternal age older than 35 years (6.7%). Prolonged pregnancies contribute to 14.0% of stillbirths. Causal pathways for stillbirth frequently involve impaired placental function, either with fetal growth restriction or preterm labour, or both. Two-thirds of newborns have their births registered. However, less than 5% of neonatal deaths and even fewer stillbirths have death registration. Records and registrations of all births, stillbirths, neonatal, and maternal deaths in a health facility would substantially increase data availability. Improved data alone will not save lives but provide a way to target interventions to reach more than 7000 women every day worldwide who experience the reality of stillbirth.
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                Author and article information

                Contributors
                m.heys@ucl.ac.uk
                Journal
                Learn Health Syst
                Learn Health Syst
                10.1002/(ISSN)2379-6146
                LRH2
                Learning Health Systems
                John Wiley and Sons Inc. (Hoboken )
                2379-6146
                06 April 2022
                January 2023
                : 7
                : 1 ( doiID: 10.1002/lrh2.v7.1 )
                : e10310
                Affiliations
                [ 1 ] Population, Policy and Practice Research and Teaching Department University College London Great Ormond Street Institute of Child Health London UK
                [ 2 ] Children's Hospital of Philadelphia General, Thoracic, and Fetal Surgery Newborn Intensive Care Unit Philadelphia USA
                [ 3 ] Snowplow Analytics London UK
                [ 4 ] Infection, Immunity and Inflammation Research and Teaching Department University College London Great Ormond Street Institute of Child Health London UK
                [ 5 ] Department of Primary Healthcare Sciences University of Zimbabwe Harare Zimbabwe
                [ 6 ] Parent and Child Health Initiative Lilongwe Malawi
                [ 7 ] Biomedical Research and Training Institute Harare Zimbabwe
                [ 8 ] Mbuya Nehanda Maternity Hospital Harare Zimbabwe
                [ 9 ] Paediatric Department Kamuzu Central Hospital Lilongwe Malawi
                [ 10 ] Maternity Division Sally Mugabe Central Hospital Harare Zimbabwe
                Author notes
                [*] [* ] Correspondence

                Michelle Heys, Population, Policy and Practice Research and Teaching Department, University College London Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK.

                Email: m.heys@ 123456ucl.ac.uk

                Article
                LRH210310
                10.1002/lrh2.10310
                9835040
                36654803
                d5c1917e-96f1-49f5-988d-9119e393a089
                © 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 February 2022
                : 06 August 2021
                : 20 March 2022
                Page count
                Figures: 4, Tables: 1, Pages: 12, Words: 7916
                Funding
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Award ID: 215742_Z_19_Z
                Categories
                Experience Report
                Experience Reports
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:12.01.2023

                behavioural sciences,global health,health services,neonatal

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