Patient safety initiatives in obstetrics: a rapid review
Bibliographic record
Abstract
OBJECTIVES: This review was commissioned by WHO, South Africa-Country office because of an exponential increase in medical litigation claims related to patient safety in obstetrical care in the country. A rapid review was conducted to examine the effectiveness of quality improvement (QI) strategies on maternal and newborn patient safety outcomes, risk of litigation and burden of associated costs. DESIGN: A rapid review of the literature was conducted to provide decision-makers with timely evidence. Medical and legal databases (eg, MEDLINE, Embase, LexisNexis Academic, etc) and reference lists of relevant studies were searched. Two reviewers independently performed study selection, abstracted data and appraised risk of bias. Results were summarised narratively. INTERVENTIONS: We included randomised clinical trials (RCTs) of QI strategies targeting health systems (eg, team changes) and healthcare providers (eg, clinician education) to improve the safety of women and their newborns. Eligible studies were limited to trials published in English between 2004 and 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: RCTs reporting on patient safety outcomes (eg, stillbirths, mortality and caesarean sections), litigation claims and associated costs were included. RESULTS: The search yielded 4793 citations, of which 10 RCTs met our eligibility criteria and provided information on over 500 000 participants. The results are presented by QI strategy, which varied from one study to another. Studies including provider education alone (one RCT), provider education in combination with audit and feedback (two RCTs) or clinician reminders (one RCT), as well as provider education with patient education and audit and feedback (one RCT), reported some improvements to patient safety outcomes. None of the studies reported on litigation claims or the associated costs. CONCLUSIONS: Our results suggest that provider education and other QI strategy combinations targeting healthcare providers may improve the safety of women and their newborns during childbirth.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".