SENIOR HIGH COST HEALTHCARE USERS: HOW DO THEY DIFFER?
Bibliographic record
Abstract
High cost users (HCUs) are a small proportion of the population that use disproportionate healthcare resources. In 2011, 5% of the population in Ontario, Canada accounted for 65% ($19.8 billion) of provincial health expenditures. Understanding HCU multi-morbidity and drug use is required to target interventions to improve clinical outcomes and contain healthcare costs. This study aimed to compare senior HCUs to non-HCUs based on demographics, co-morbidities, medication use, health service utilization, clinical outcomes and costs. We conducted a retrospective population-based matched cohort analysis of incident senior HCUs defined as Ontarians age ≥ 66 years in the top 5% of total healthcare costs in FY2013. Healthcare and prescription drug utilization data were obtained from health administrative databases. The primary outcomes were annual total healthcare expenditures per patient, total drug costs per patient and drug-to-total healthcare expenditure ratio. Secondary outcomes were one-year mortality and hospitalization rate. Senior HCUs (n=176,604) accounted for $4.9 billion in total healthcare expenditures and $433 million in medication costs in FY2013. Compared to non-HCUs (n=529,812) on a per patient basis, HCUs incurred higher total healthcare costs ($27,697 vs. $2233) and higher medication costs ($2453 vs. $842). HCUs were characterized by greater polypharmacy (>5 medications, 87.7% vs. 47.6%) and multi-morbidity (median John Hopkins Expanded Diagnosis Clusters [EDCs], 14 vs. 10). HCUs had higher annual mortality (10.39% vs. 0.72%) and hospitalization rates (3.20 vs. 0.06 hospitalizations per 1000 person-years). Compared to non-HCUs, senior HCUs are frail, multi-morbid and vulnerable. The contribution of prescribing and medication utilization quality deserves further study.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".