A Retrospective Review of Physician-related Patient Complaints from a Tertiary Pediatric Hospital
Bibliographic record
Abstract
BACKGROUND: Trends in patient concerns can identify systematic problems in health care delivery that may not be detected when addressing individual concerns. It can be difficult identifying trends without using a standardized taxonomy. The study objectives were to describe patient complaints from a tertiary care pediatric hospital and categorize them using a standardized complaint taxonomy. METHODS: Physician-based patient complaints were compiled from April 2011 to May 2014 from a tertiary pediatric hospital. These complaints were coded independently by 2 reviewers using the Reader taxonomy, a published standardized taxonomy. Complaints were placed into 3 domains: clinical, management, and relationships then organized into categories. Inter-rater reliability for domain classification between the 2 reviewers was calculated using Cohen's unweighted κ. RESULTS: Eighty-seven patient complaints were identified, representing approximately 1 per 10,000 physician-patient encounters. Half (48/87) were related to care in the emergency department. When adjusted for volume, pediatric hospital medicine had the highest number of complaints, with 12.1 per 10,000 encounters. The majority of patient complaints, 66% (57/87), were of the clinical domain (κ = 0.61). Sixty percent (52/87) were in the relationship domain (κ = 0.68), and 16% (14/87) were in the management domain (κ = 0.65). CONCLUSIONS: We found a low overall complaint rate. Our results indicate that interventions to improve patient experience should initially be targeted at emergency and hospital medicine on the clinical and relationship domains. The inter-rater reliability of the Reader taxonomy was moderate with implications for processing patient complaints at a hospital level.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".