High-Cost Patients and Preventable Spending: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Although high-cost (HC) patients make up a small proportion of patients, they account for most health system costs. However, little is known about HC patients with cancer or whether some of their care could potentially be prevented. This analysis sought to characterize HC patients with cancer and quantify the costs of preventable acute care (emergency department visits and inpatient hospitalizations). METHODS: This analysis examined a population-based sample of all HC patients in Ontario in 2013. HC patients were defined as those above the 90th percentile of the cost distribution; all other patients were defined as non-high-cost (NHC). Patients with cancer were identified through the Ontario Cancer Registry. Sociodemographic and clinical characteristics were examined and the costs of preventable acute care for both groups by category of visit/condition were estimated using validated algorithms. RESULTS: Compared with NHC patients with cancer (n=369,422), HC patients with cancer (n=187,770) were older (mean age 70 vs 65 years), more likely to live in low-income neighborhoods (19% vs 16%), sicker, and more likely to live in long-term care homes (8% vs 0%). Although most patients from both cohorts tended to be diagnosed with breast, prostate, or colorectal cancer, those with multiple myeloma or pancreatic or liver cancers were overrepresented among the HC group. Moreover, HC patients were more likely to have advanced cancer at diagnosis and be in the initial or terminal phase of treatment compared with NHC patients. Among HC patients with cancer, 9% of spending stemmed from potentially preventable/avoidable acute care, whereas for NHC patients, this spending was approximately 30%. CONCLUSIONS: HC patients with cancer are a unique subpopulation. Given the type of care they receive, there seems to be limited scope to prevent acute care spending among this patient group. To reduce costs, other strategies, such as making hospital care more efficient and generating less costly encounters involving chemotherapy, should be explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".