Hospital's observed specific standard practice: A novel measure of variation in care for common inpatient pediatric conditions
Bibliographic record
Abstract
BACKGROUND: Previously few means existed to broadly examine variability across conditions/practices within or between hospitals for common pediatric conditions. OBJECTIVE: Our objective was to develop a novel empiric measure of variation in care and test its association with patient-centered outcomes. DESIGNS: We conducted a retrospective cohort study of children hospitalized from January 2016 to December 2018 using the Pediatric Hospital Information Systems database. SETTINGS AND PARTICIPANTS: We included children ages 0-18 years hospitalized with asthma, bronchiolitis, or gastroenteritis. INTERVENTION: We developed a hospital-specific measure of variation in care, the hospital's observed specific standard practice (HOSSP), the most common combination of laboratory studies, imaging, and medications used at each hospital. MAIN OUTCOME AND MEASURES: The outcomes were standardized costs, length of stay (LOS), and 7-day all-cause readmissions. RESULTS: Among 133,392 hospitalizations from 41 hospitals (asthma = 50,382, bronchiolitis = 54,745, and gastroenteritis = 28,265), there was significant variation in overall HOSSP adherence across hospitals for these conditions (asthma: 3.5%-47.4% [p < .001], bronchiolitis: 2.5%-19.8% [p < .001], gastroenteritis: 1.6%-11.6% [p < .001]). The majority of HOSSP variation was driven by differences in medication prescribing for asthma and bronchiolitis and laboratory ordering for gastroenteritis. For all three conditions, greater HOSSP adherence was associated with significantly lower hospital costs (asthma: p = .04, bronchiolitis: p < .001, acute gastroenteritis: p = .01), without increases in LOS or 7-day all cause readmissions. CONCLUSION: We found substantial variation in the components and adherence to HOSSP. Hospitals with greater HOSSP adherence had lower costs for these conditions. This suggests hospitals can use data around laboratory, imaging, and medication prescribing practices to drive standardization of care, reduce unnecessary testing and treatment, determine best practices, and reduce costs.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".