Multicentre Usability Evaluation and Co-Development of a Digital Decision-Support Tool for Labour Triage: Mixed-Methods Study (Preprint)
Notice bibliographique
Résumé
Abstract Background Digital decision-support tools for labor care remain limited, with few technologies successfully addressing the complex, time-sensitive decisions required during labor triage. Fit4Labour is a clinician-facing, data-driven research tool, currently under development, that combines computerized cardiotocography interpretation with maternal and fetal risk factors to generate individualized risk scores at labor onset. Its primary aim is to support clinicians in identifying fetuses who may require closer monitoring or expedited delivery, while simultaneously providing reassurance in low-risk cases. By promoting consistent communication and timely escalation of care, the Fit4Labour tool seeks to strengthen clinical decision-making. Understanding and addressing usability and implementation barriers will be critical to its adoption in clinical practice. Objective This study aims to evaluate whether a digitally co-developed labor decision-support tool (Fit4Labour) maintains usability and implementation readiness across NHS hospitals with differing clinical contexts. Methods We conducted a convergent parallel mixed methods study in 3 United Kingdom hospitals (December 2022 to May 2025). Phase 1 involved iterative co-development with midwives and doctors at Oxford University Hospitals NHS Foundation Trust; Phase 2 validated the locked version at Birmingham Women’s and Children’s NHS Foundation Trust and Buckinghamshire Healthcare NHS Trust. Participants completed scenario-based usability sessions evaluated with the System Usability Scale (SUS) and Single Ease Question (SEQ), and task completion time, followed by focus groups and interviews analyzed thematically. Results Twenty-six health care professionals participated: 12 in co-development (7 midwives, 5 doctors) and 14 in validation (8 midwives, 6 doctors) phases. During co-development at Oxford, the tool met the “excellent” usability threshold (mean SUS 82.1, SD 12.3), indicating readiness for the validation phase. The locked version (v4.0) independently met the “excellent” threshold at both validation sites (combined mean SUS 85.8, SD 10.2; Birmingham 80.7, SD 10.8; Buckinghamshire 90.8, SD 7.2). Task completion times were comparable across validation sites (Birmingham 10.3, SD 1.6 min; Buckinghamshire 9.2, SD 1.9 min), while SEQ scores were consistently high across all scenarios (mean 6.1/7, SD 0.8). Thematic analysis identified 12 themes within 3 domains: clinical integration and workflow, technology adoption and implementation, and patient safety and decision-making. Participants described the Fit4Labour tool as a supportive tool, “like a co-pilot,” improving confidence in decisions with the potential to aid triage assessment. Perceived limitations included an incomplete risk factor profile and the need for minor technical adjustments or integration with existing hospital systems. Conclusions Through systematic co-development, the Fit4Labour tool met the established usability benchmark at 2 independent NHS hospitals with markedly different clinical contexts. Clinicians viewed the tool as a supportive aid providing a shared language for risk communication and enhanced decision-making while preserving clinical autonomy. Whether these usability findings translate to improved clinical outcomes in real-world practice requires prospective evaluation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,075 | 0,102 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».