Older high-cost patients in Norwegian somatic hospitals: a register-based study of patient characteristics
Bibliographic record
Abstract
OBJECTIVE: Two-thirds of the economic resources in Norwegian hospitals are used on 10% of the patients. Most of these high-cost patients are older adults, which experience more unplanned hospital admissions, longer hospital stays and higher readmission rates than other patients. This study aims to examine the individual and clinical characteristics of older patients with unplanned admissions to Norwegian somatic hospitals and how these characteristics differ between high-cost and low-cost older patients. DESIGN: Observational cross-sectional study. SETTING: Norwegian somatic hospitals. PARTICIPANTS: National registry data of older Norwegian patients (≥65 years) with ≥1 unplanned contact with somatic hospitals in 2019 (n=2 11 738). PRIMARY OUTCOME MEASURE: High-cost older patients were defined as those within the 10% of the highest diagnosis-related group weights in 2019 (n=21 179). We compared high-cost to low-cost older patients using bivariate analyses and logistic regression analysis. RESULTS: Men were more likely to be high-cost older patients than women (OR=1.25, 95% CI 1.21 to 1.29) and the oldest (90+ years) compared with the youngest older adults (65-69 years) were less likely to cause high costs (OR=0.47, 95% CI 0.43 to 0.51). Those with the highest level of education were less likely to cause high costs than those with primary school degrees (OR=0.74, 95% CI 0.69 to 0.80). Main diagnosis group (OR=3.50, 95% CI 3.37 to 3.63) and dying (OR=4.13, 95% CI 3.96 to 4.30) were the clinical characteristics most strongly associated with the likelihood of being a high-cost older patient. CONCLUSION: Several of the observed patient characteristics in this study may warrant further investigation as they might contribute to high healthcare costs. For example, MDGs, reflecting comprehensive healthcare needs and lower education, which is associated with poorer health status, increase the likelihood of being high-cost older patients. Our results indicate that Norwegian hospitals function according to the intentions of those having the highest needs receiving most services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".