Quality improvement initiative: improving obstetric triaging practices in a rural maternal hospital in central India
Bibliographic record
Abstract
Triaging of obstetric patients by emergency care providers is paramount. It helps provide appropriate and timely management to prevent further injury and complications. Standardised trauma acuity scales have limited applicability in obstetric triage. Specific obstetric triage index tools improve maternal and neonatal outcomes but remain underused. The aim was to introduce a validity-tested obstetric triage tool to improve the percentage of correctly triaged patients (correctly colour-coded in accordance with triage index tool and attended to within the stipulated time interval mandated by the tool) from the baseline of 49% to more than 90% through a quality improvement (QI) process.A team of nurses, obstetricians and postgraduates did a root cause analysis to identify the possible reasons for incorrect triaging of obstetric patients using process flow mapping and fish bone analysis. Various change ideas were tested through sequential Plan-Do-Study-Act (PDSA) cycles to address issues identified.The interventions included introduction and application of an obstetric triage index tool, training of triage nurses and residents. We implemented these interventions in eight PDSA cycles and observed outcomes by using run charts. A set of process, output and outcome indicators were used to track if changes made were leading to improvement.Proportion of correctly triaged women increased from the baseline of 49% to more than 95% over a period of 8 months from February to September 2020, and the results have been sustained in the last PDSA cycle, and the triage system is still sustained with similar results. The median triage waiting time reduced from the baseline of 40 min to less than 10 min. There was reduction in complications attributable to improper triaging such as preterm delivery, prolonged intensive care unit stay and overall morbidity. It can be thus concluded that a QI approach improved obstetric triaging in a rural maternity hospital in India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".