Effective implementation strategies and drivers of culture change for improving time to treatment for severe maternal hypertension
Bibliographic record
Abstract
Abstract Objective To assess effective implementation strategies to reduce time to treatment for severe maternal hypertension and drivers of culture change among high‐performing sites in a statewide quality improvement (QI) initiative. Methods Using a mixed‐methods sequential explanatory design, mixed effect linear regression models with a logit link were used to assess the association between achievement of system changes at each hospital and the proportion of cases in which time to treatment was achieved, including fixed effects for system changes and time (quarter of implementation year) and random effects for hospital and quarter within hospital (random slope). All models were adjusted for birth volume, location (urban/rural), and patient population demographics, and a sensitivity analysis was performed for multiple comparisons. Then, key informant interviews of 11 high‐performing hospital teams explored implementation strategies driving system and clinical culture change. Results Among 108 participating hospitals, 79 submitted quarterly survey data on progress toward implementing system changes. Our quantitative analysis demonstrated that several individual system changes were initially associated with a reduction in time to treatment for maternal hypertension, but these associations were not significant after adjusting for multiple comparisons. Through qualitative interviews, we learned that high‐performing sites enacted the system changes which showed initial promise in our quantitative analysis by reducing burden for their QI teams by utilizing existing QI support, educating clinical teams and patients to empower them as agents of behavior change, promoting clinician engagement using multi‐level strategies, optimizing workflow and infrastructure, and fostering innovation based on other teams’ experiences. Key drivers of clinical culture change included hospital environments that emphasized communication at the patients’ bedside around QI priorities, valued interprofessional relationships and communication, promoted shared values around providing high‐quality maternal care and protecting maternal safety, and harnessed external support from professional societies and leadership. Conclusion This mixed‐methods analysis identifies key implementation strategies that perinatal quality collaboratives and individual hospitals can utilize to sustain behavior change to reduce time to treatment for severe maternal hypertension. While we were unable to definitively identify singular system changes that reduced time to treatment, our qualitative data suggest that a combination of these changes may change clinical culture and lead to improved outcomes. Future work to assess the impact of selected system changes as well as clinical culture change on obstetric QI efforts is needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".