Multicentre Usability Evaluation and Co-Development of a Digital Decision-Support Tool for Labour Triage: Mixed-Methods Study (Preprint)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".