Increasing Parent Satisfaction With Discharge Planning
Bibliographic record
Abstract
BACKGROUND: Neonatal intensive care unit (NICU) families are often overwhelmed by the discharge process. Their anxiety can inhibit learning and contribute to poor infant outcomes and increased healthcare utilization after discharge. Quality of the discharge teaching is the strongest predictor of discharge readiness, so NICUs must develop excellent discharge preparation programs. PURPOSE: This improvement project enhances NICU discharge preparedness by providing consistent, early discharge teaching using technology as a supplemental resource and raises parental satisfaction with the process. METHODS: Neonatal intensive care unit staff and former NICU parents developed a task force to create technology-based discharge education content. The content was originally uploaded to an e-book and later transferred to the electronic health record inpatient portal. Families were able to view discharge teaching content at their own convenience and pace and review topics as needed with the NICU staff. Postdischarge follow-up phone calls provided insight into parental reaction to the new education format. RESULTS: Parent satisfaction top-box scores, reflecting the highest rating in the "Prepared for Discharge" category of the patient satisfaction survey, improved from a baseline of 47% in 2017 to 70% in 2019. Overwhelmingly, 92% of families highly rated the tablet-based discharge teaching during postdischarge phone calls. IMPLICATIONS FOR PRACTICE: A comprehensive, consistent, and early discharge program using technology can lead to more effective and efficient NICU discharge education and improved parent satisfaction. IMPLICATIONS FOR RESEARCH: Further studies are needed to generalize hospital-based inpatient portal teaching as an additional resource for parental education in the NICU.
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 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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".