Development and implementation of a quality improvement toolkit, iron deficiency in pregnancy with maternal iron optimization (IRON MOM): A before-and-after study
Bibliographic record
Abstract
BACKGROUND: Iron deficiency (ID) in pregnancy is a common problem that can compromise both maternal and fetal health. Although daily iron supplementation is a simple and effective means of treating ID in pregnancy, ID and ID anemia (IDA) often go unrecognized and untreated due to lack of knowledge of their implications and competing clinical priorities. METHODS AND FINDINGS: In order to enhance screening and management of ID and IDA in pregnancy, we developed a novel quality-improvement toolkit: ID in pregnancy with maternal iron optimization (IRON MOM), implemented at St. Michael's Hospital in Toronto, Canada. It included clinical pathways for diagnosis and management, educational resources for clinicians and patients, templated laboratory requisitions, and standardized oral iron prescriptions. To assess the impact of IRON MOM, we retrospectively extracted laboratory data of all women seen in both the obstetrics clinic and the inpatient delivery ward settings from the electronic patient record (EPR) to compare measures pre- and post-implementation of the toolkit: a process measure of the rates of ferritin testing, and outcome measures of the proportion of women with an antenatal (predelivery) hemoglobin value below 100 g/L (anemia), the proportion of women who received a red blood cell (RBC) transfusion during pregnancy, and the proportion of women who received an RBC transfusion immediately following delivery or in the 8-week postpartum period. The pre-intervention period was from January 2012 to December 2016, and the post-intervention period was from January 2017 to December 2017. From the EPR, 1,292 and 2,400 ferritin tests and 16,603 and 3,282 antenatal hemoglobin results were extracted pre- and post-intervention, respectively. One year after implementation of IRON MOM, we found a 10-fold increase in the rate of ferritin testing in the obstetric clinics at our hospital and a lower risk of antenatal hemoglobin values below 100 g/L (pre-intervention 13.5% [95% confidence interval (CI) 13.0%-14.11%]; post-intervention 10.6% [95% CI 9.6%-11.7%], p < 0.0001). In addition, a significantly lower proportion of women received an RBC transfusion during their pregnancy (1.2% pre-intervention versus 0.8% post-intervention, p = 0.0499) or immediately following delivery and in the 8 weeks following (2.3% pre-intervention versus 1.6% post-intervention, p = 0.0214). Limitations of this study include the use of aggregate data extracted from the EPR, and lack of a control group. CONCLUSIONS: The introduction of a standardized toolkit including diagnostic and management pathways as well as other aids increased ferritin testing and decreased the incidence of anemia among women presenting for delivery at our site. This strategy also resulted in reduced proportions of women receiving RBC transfusion during pregnancy and in the first 8 weeks postpartum. The IRON MOM toolkit is a low-tech strategy that could be easily scaled to other settings.
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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".