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Record W2978344085 · doi:10.2196/15245

Standardizing Postpartum Discharge Instructions With an Educational Video: A Quality Improvement Project

2019· article· en· W2978344085 on OpenAlexvenueno aff
Jennifer Hoffman, Lori M. Burke, Cheryl Kay, Rachel Hlavaty, Jennifer Thompson-Wood, Danyelle D. O'Dell

Bibliographic record

VenueIproceedings · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality managementFocus groupMedical emergencyPatient educationObstetricsEmergency medicineFamily medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.334
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
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