Improving the paediatric surgery patient experience: an 8-year analysis of narrative quality data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 it