Improving the paediatric surgery patient experience: an 8-year analysis of narrative quality data
Bibliographic record
Abstract
BACKGROUND: Narrative data about the patient experience of surgery can help healthcare professionals and administrators better understand the needs of patients and their families as well as provide a foundation for improvement of procedures, processes and services. However, units often lack a methodological framework to analyse these data empirically and derive key areas for improvement. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) is aimed at improving the quality of surgical care by collecting patient data and reporting risk-adjusted surgical outcomes for each participant hospital in the programme. Though qualitative data about patient experience are captured as part of the NSQIP database, to date no framework or methodology has been proposed, or reported on, to analyse these data for the purposes of quality improvement. The goal of this study was to demonstrate the feasibility of using content analysis to empirically derive key areas for quality improvement from a sample of 3601 narrative comments about paediatric surgery from patients and families at British Columbia Children's Hospital. STUDY DESIGN: Thematic content analysis conducted on a total of 3601 patient and family narratives received between 2011 and 2018. RESULTS: Overall satisfaction with care was high and experiences with healthcare providers at the hospital were positive. Areas for improvement were identified in the themes of health outcomes, communication and surgery timelines. Results informed follow-up interprofessional quality improvement initiatives. CONCLUSIONS: Recording and analysing patient experience data as part of validated quality improvement programmes such as ACS NSQIP can provide valuable and actionable information to improve quality of care.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".