Standardizing Postpartum Discharge Instructions With an Educational Video: A Quality Improvement Project
Bibliographic record
Abstract
Background The World Health Organization (WHO) estimates that, in the United States, 1200 women annually experience perinatal events that prove fatal and 60,000 suffer complications that are near-fatal. Postpartum morbidity and mortality may be decreased by explicit patient education. At our institution, the maternal discharge education process was varied among providers. Of the available literature, most focus on maternal knowledge of pediatric concerns with a limited amount of studies looking at maternal postpartum health. Objective The objective of this quality improvement project is to standardize postpartum education with the use of a postpartum education video available on a bedside tablet in order to improve maternal perception and knowledge of postpartum warning signs. Methods This prospective cohort study was designed using a patient survey which was administered to evaluate the effectiveness of the maternal discharge education procedures in our institution. The baseline results were reviewed by a team of physicians and nurses. A 10-question survey was provided to patients following the birth of their first baby about maternal warning signs and complications after discharge from the hospital. A standardized discharge education video was created using information from ACOG and AWHONN. The video was made accessible on bedside tablet devices. Patients were able to indicate understanding or request clarification on the devices after review of the materials, and this was communicated from the tablets to the electronic health record. All postpartum nurses were trained on the video content and how to operate the tablets. Survey responses were collected via bedside tablets following implementation of the video and were compared with the baseline results. Educational information was available to patients after discharge from the hospital via a patient portal. Results Twenty-nine women were surveyed prior to implementation of the standardized educational video available on bedside tablets. After implementation, 50 women were surveyed. Comparison of the survey responses showed there was an increase in patient-reported knowledge and understanding in all 10 questions on the survey. Of those, 4 areas were statistically significant with P values <.05: when to call 911 (82.8% before and 98% after), when to call your doctor (75.9% before and 98.0% after), heavy vaginal bleeding (62.1% before and 87.8% after), and symptoms of acute blood loss (51.7% before and 83.7% after). Conclusions The implementation of a postpartum education video available to patients on bedside tablets improved and standardized workflow for routine postpartum care and discharge processes. Survey questions regarding patient knowledge and perception of when to call 911, when to call your doctor, heavy bleeding, and signs of acute blood loss were all noted to be statistically significant. The improved perception of postpartum warning signs after the educational video appears to be beneficial in patient education at this high volume OB institution. The establishment of this platform for education became a model for education in other hospital units using bedside tablets. The use of a multidisciplinary team to review, revise, and standardize the postpartum education program at our institution was well received and supported by nurses, providers, and ultimately patients.
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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 | 0.065 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".