ArticleBMJ open2021
Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05127070 (Evaluating the NeoTree), which is not on this map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluating the NeoTree: An eHealth Solution to Reduce Neonatal Mortality in Two Low Income Countries: Malawi and Zimbabwe
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- MANTRA game analytics: effectiveness of educational mobile game on knowledge gain and retention of Female Community Health Volunteers (FCHVs) and women in rural Nepal assessed through game analytics.BMC public health · 2026Article
- Impact and cost-effectiveness of Neotree, a digital data capture and decision support tool designed to improve neonatal survival in Zimbabwe: an interrupted time series analysis and economic evaluation.BMJ global health · 2026Article
- Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.BMJ paediatrics open · 2025Article
- Assessing the Use of Neonatal Sepsis Guidelines and Antibiotic Prescription With Large-Scale Prospective Data From Zimbabwe and Malawi.Journal of the Pediatric Infectious Diseases Society · 2025Article
- Therapeutic hypothermia for neonatal hypoxic ischaemic encephalopathy in Sub-Saharan Africa: A scoping review.PloS one · 2025Article
- Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study.JMIR formative research · 2024Article
- Development and Pilot Implementation of Neotree, a Digital Quality Improvement Tool Designed to Improve Newborn Care and Survival in 3 Hospitals in Malawi and Zimbabwe: Cost Analysis Study.JMIR mHealth and uHealth · 2023Article
- Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model.Archives of disease in childhood · 2023Article
- Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.Learning health systems · 2023Article
- Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings.Wellcome open research · 2022Article
- Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App and Dashboard: A 1-Year Digital Perinatal Outcome Audit.Frontiers in digital health · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
objectivesTo determine whether a panel of neonatal experts could address evidence gaps in local and international neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).
designTwo-round, modified Delphi technique. SETTING AND
participantsParticipants were neonatal experts from high-income and low-income countries (LICs).
methodsThis was a consensus-generating study. In round 1, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus for inclusion (≥80% agreement) were incorporated into the algorithms. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round 2, experts rated items from round 1 that did not reach consensus.
resultsFourteen experts participated in round 1, 10 in round 2. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Per experts' recommendations, items in line with local and WHO guidelines yet not meeting consensus were still included to encourage consistency for front-line healthcare workers. As a result, the final algorithms included 53 items (62%).
conclusionFour algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to NeoTree will be made in response to these findings. Next steps include clinical validation of the algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.