A Review of Best Practices for Monitoring and Improving Inpatient Pediatric Patient Experiences
Bibliographic record
Abstract
CONTEXT: Achieving high-quality patient-centered care requires assessing patient and family experiences to identify opportunities for improvement. With the Child Hospital Consumer Assessment of Healthcare Providers and Systems Survey, hospitals can assess performance and make national comparisons of inpatient pediatric experiences. However, using patient and family experience data to improve care remains a challenge. OBJECTIVE: We reviewed the literature on best practices for monitoring performance and undertaking activities aimed at improving pediatric patient and family experiences of inpatient care. DATA SOURCES: We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, and PsychINFO. STUDY SELECTION: We included (1) English-language peer-reviewed articles published from January 2000 to April 2019; (2) articles based in the United States, United Kingdom, or Canada; (3) articles focused on pediatric inpatient care; (4) articles describing pediatric patient and family experiences; and (5) articles including content on activities aimed at improving patient and family experiences. Our review included 25 articles. DATA EXTRACTION: Two researchers reviewed the full article and abstracted specific information: country, study aims, setting, design, methods, results, Quality Improvement (QI) initiatives performed, internal reporting description, best practices, lessons learned, barriers, facilitators and study implications for clinical practice, patient-experience data collection, and QI activities. We noted themes across samples and care settings. RESULTS: We identified 10 themes of best practice. The 4 most common were (1) use evidence-based approaches, (2) maintain an internal system that communicates information and performance on patient and family experiences to staff and hospital leadership, (3) use experience survey data to initiate and/or evaluate QI interventions, and (4) identify optimal times (eg, discharge) and modes (eg, print) for obtaining patient and family feedback. These correspond to adult inpatient best practices. CONCLUSIONS: Both pediatric and adult inpatient best practices rely on common principles of culture change (such as evidence-based clinical practice), collaborative learning, multidisciplinary teamwork, and building and/or supporting a QI infrastructure that requires time, money, collaboration, data tracking, and monitoring. QI best practices in both pediatric and adult inpatient settings commonly rely on identifying drivers of overall ratings of care, rewarding staff for successful implementation, and creating easy-to-use and easy-to-access planning and QI tools for staff.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".