Informatics Interventions for Maternal Morbidity: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Women have been entering pregnancy less healthy than previous generations, placing them at increased risk for pregnancy complications. One approach to ensuring effective monitoring and treatment of at-risk women is designing technology-based interventions that prevent maternal morbidities and treat perinatal conditions. OBJECTIVE: This scoping review evaluates what informatics interventions have been designed and tested to prevent and treat maternal morbidity. METHODS: MEDLINE, Embase, and Cochrane Library were searched to identify relevant studies. The inclusion criteria were studies that tested a medical or clinical informatics intervention; enrolled adult women; and addressed preeclampsia, gestational diabetes mellitus (GDM), preterm birth, Centers for Disease Control and Prevention-defined severe maternal morbidity, or perinatal mental health conditions. Demographic, population, and intervention data were extracted to characterize the technologies, conditions, and populations addressed. RESULTS: A total of 80 studies were identified that met the inclusion criteria. Many of the studies tested for multiple conditions. Of these, 73% (60/82) of the technologies were tested for either GDM or perinatal mental health conditions, and 15% (12/82) were tested for preeclampsia. For technologies, 32% (28/87) of the technologies tested were smartphone or tablet applications, 26% (23/87) were telehealth interventions, and 14% (12/87) were remote monitoring technologies. Of the many outcomes measured by the studies, almost half (69/140, 49%) were patient physical or mental health outcomes. CONCLUSIONS: Per this scoping review, most informatics interventions address three conditions: GDM, preeclampsia, and mental health. There may be opportunities to treat other potentially lethal conditions like postpartum hemorrhage using proven technologies such as mobile apps. Ample gaps in the literature exist concerning the use of informatics technologies aimed at maternal morbidity. There may be opportunities to use informatics for lesser-targeted conditions and populations.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".