Comparing the dangers of a stay in English and German hospitals for high‐need patients
Bibliographic record
Abstract
OBJECTIVE: To estimate the risk of an avoidable adverse event for high-need patients in England and Germany and the causal impact that has on outcomes. DATA SOURCES: We use administrative, secondary data for all hospital inpatients in 2018. Patient records for the English National Health Service are provided by the Hospital Episode Statistics database and for the German health care system accessed through the Research Data Center of the Federal Statistical Office. STUDY DESIGN: We calculated rates of three hospital-acquired adverse events and their causal impact on mortality and length of stay through propensity score matching and estimation of average treatment effects. DATA COLLECTION/EXTRACTION METHODS: Patients were identified based on diagnoses codes and translated Patient Safety Indicators developed by the Agency for Healthcare Research and Quality. PRINCIPAL FINDINGS: For the average hospital stay, the risk of an adverse event was 5.37% in the English National Health Service and 3.26% in the German health care system. High-need patients are more likely to experience an adverse event, driven by hospital-acquired infections (2.06%-4.45%), adverse drug reactions (2.37%-2.49%), and pressure ulcers (2.25%-0.45%). Adverse event risk is particularly high for patients with advancing illnesses (10.50%-27.11%) and the frail elderly (17.75%-28.19%). Compared to the counterfactual, high-need patients with an adverse event are more likely to die during their hospital stay and experience a longer length of stay. CONCLUSIONS: High-need patients are particularly vulnerable with an adverse event risking further deterioration of health status and adding resource use. Our results indicate the need to assess the costs and benefits of a hospital stay, particularly when care could be provided in settings considered less hazardous.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".