Performance optimization of an R Shiny-based digital health dashboard for monitoring small and sick newborn care in low-resource hospital settings

Performance Optimization of an R Shiny-based Digital Health Dashboard for Monitoring Small and Sick Newborn Care in Low-resource Hospital Settings | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Performance Optimization of an R Shiny-based Digital Health Dashboard for Monitoring Small and Sick Newborn Care in Low-resource Hospital Settings View ORCID Profile Julius Thomas , Georgia Jenkins , Junwei Chen , Morris Ogero , Lucas Malla , View ORCID Profile Lisa R Hirschhorn , Rebecca Richards-Kortum , Z. Maria Oden , Christine Bohne , John Wainaina doi: https://doi.org/10.64898/2026.03.08.26347893 Julius Thomas 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julius Thomas For correspondence: jt77{at}rice.edu Georgia Jenkins 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Junwei Chen 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Morris Ogero 2 London School of Hygiene and Tropical Medicine , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lucas Malla 2 London School of Hygiene and Tropical Medicine , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa R Hirschhorn 3 Department of Medical Social Science, Feinberg School of Medicine, Northwestern University , Chicago, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa R Hirschhorn Rebecca Richards-Kortum 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Z. Maria Oden 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Bohne 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Wainaina 1 Rice360 Institute for Global Health Technologies, Rice University , Texas, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Digital health dashboards can enhance health system performance by transforming routinely collected data into actionable insights for decision-making. In low-resource settings, however, their effectiveness depends not only on the relevance of indicators but also on system reliability within constrained digital infrastructure. Neonatal mortality remains a major global health challenge, with the highest burden in low- and middle-income countries, where many deaths are preventable through timely, evidence-based interventions. Continuous monitoring of care processes and outcomes is therefore essential. To support this need, we developed the NEST360 Implementation Tracker (NEST-IT) using R Shiny to support quality improvement across more than 100 hospitals in sub-Saharan Africa. As the platform scaled to over half a million records and increasing concurrent users, performance constraints emerged, particularly in hospitals with limited computing resources, threatening timely access to critical information. Objective This study aimed to describe optimization strategies applied to the NEST-IT dashboard and evaluate their impact before and after implementation. Methods A structured optimization process was implemented following established R Shiny performance principles. Dashboard profiling was first conducted to identify key bottlenecks, after which targeted improvements were applied to improve efficiency and responsiveness. A quasi-experimental pre–post evaluation (December 2023–August 2024) assessed performance using three indicators: server processing time, visualization rendering time (VRT), and Time to First Byte (TTFB). Metrics were measured repeatedly during one-month baseline and post-optimization periods and summarized using mean values. Results Four primary bottlenecks were identified: delayed server responses, slow visualization rendering, inefficient data handling, and inconsistent device performance. Following optimization, interactive plot load time decreased from 10.1 to 2.7 ± 0.6 seconds (73.3% improvement). Visualization rendering improved from 3.61 to 1.62 seconds, while server processing time fell from 2.3 ± 0.7 to 0.8 ± 0.3 seconds. TTFB improved from 1.9 ± 0.4 to 0.6 ± 0.2 seconds, and system uptime increased from 92.5% to 99.2%. Conclusion Performance optimization substantially improved dashboard responsiveness, enabling timely access to critical neonatal information in resource-constrained hospital settings. The findings provide practical, evidence-based framework for improving the performance of R Shiny dashboards and demonstrate scalable strategies for delivering reliable digital decision-support tools in low-resource health systems. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work is funded through the NEST360 Alliance with thanks to John D. and Catherine T. MacArthur Foundation, the Bill & Melinda Gates Foundation, ELMA Philanthropies, The Children's Investment Fund Foundation UK, The Lemelson Foundation, The Sall Family Foundation, and the Ting Tsung and Wei Fong Chao Foundation under agreements to William Marsh Rice University. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present work are contained in the manuscript Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted March 19, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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Maria Oden , Christine Bohne , John Wainaina medRxiv 2026.03.08.26347893; doi: https://doi.org/10.64898/2026.03.08.26347893 Share This Article: Copy Citation Tools Performance Optimization of an R Shiny-based Digital Health Dashboard for Monitoring Small and Sick Newborn Care in Low-resource Hospital Settings Julius Thomas , Georgia Jenkins , Junwei Chen , Morris Ogero , Lucas Malla , Lisa R Hirschhorn , Rebecca Richards-Kortum , Z. 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