First 28: Design of a Mobile App for Neonatal Health Risk Assessment and Support for New Mothers
Notice bibliographique
Résumé
Background: Factors like dehydration and respiratory infection pose risks to infant survival in the critical first 28 days of life. UNICEF reported the 2016 global rate of neonatal death was 19 per 1000 live births. Typically, women manage multiple household and family responsibilities in addition to care of a new baby and often feel overwhelmed by the demands of new motherhood. The American College of Obstetricians and Gynecologists recommends that support to new mothers be an ongoing process, rather than a single postnatal visit. However, in low-resource environments such as developing countries and remote communities, access to ongoing support for breastfeeding, health education, and infant check-ups from a professional health care provider or health worker may not be possible. Numerous examples exist of successful mobile health interventions in low-resource environments. However, existing mobile apps for newborn health often focus on single issues that are disconnected from health care providers. There is a need to comprehensively address multiple newborn health issues, with evidence-based and personalized interventions that support new mothers. Objective: This study aims to design and build a prototype of a mobile app to comprehensively identify early signs and symptoms of common newborn illnesses, access relevant evidence-based health information, and support decision-making with the overall goal of enhancing new mothers’ ability to improve newborn health outcomes. The prototype will be used in a future pragmatic trial. Methods: An interdisciplinary and international team including nursing, medicine, dietary, health informatics, and public health collaborated on this study. First, a literature review was conducted to supplement the team’s existing knowledge on common neonatal problems, generate the evidence base for appropriate in-home interventions, and identify best practices in breast feeding. Second, a review of current mobile apps available in neonatal risks was conducted to assess gaps with attention to comprehensiveness of health issues, interface/integration with clinical decision support systems, and application of user-centered design and state of the art design principles and standards. Results: Our app, First 28, works offline for easy accessibility and displays evidence-based best practices and guidelines, personalized for mothers based on risks. Using a tailored symptoms list and computerized data entry to gather information, the mobile app performs analysis using a decision table algorithm to identify the risks the baby might encounter and suggests best solutions based on the outcomes. Mothers can submit images or crucial information about their baby and track growth through the app’s data visualization tools. Data is stored on a FHIR server for integration with health care services and electronic health records. Future plans include automated data and image analytics of the uploaded information to alert health care providers of any abnormalities that may provide critical early evidence for potential neonatal risks and complications. Conclusions: First 28 empowers mothers with the knowledge and resources to maintain proper breastfeeding techniques, assess newborn health risks, and improve health outcomes within the crucial first 28 days of life. In the next phase, the prototype will be evaluated by users with a plan to utilize it in a pragmatic trial.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».