Clinical characteristics and preventable acute care spending among a high cost inpatient population
Bibliographic record
Abstract
BACKGROUND: A small proportion of patients account for the majority of health care spending. The objectives of this study were to explore the clinical characteristics, patterns of health care use, and the proportion of acute care spending deemed potentially preventable among high cost inpatients within a Canadian acute-care hospital. METHODS: We identified all individuals within the Ottawa Hospital with one or more inpatient hospitalization between April 1, 2010 and March 31, 2011. Clinical characteristics and frequency of hospital encounters were captured in the information systems of the Ottawa Hospital Data Warehouse. Direct inpatient costs for each encounter were summed using case costing information and those in the upper first and fifth percentiles of the cumulative direct cost distribution were defined as extremely high cost and high cost respectively. We quantified preventable acute care spending as hospitalizations for ambulatory care sensitive conditions (ACSC) and spending attributable to difficulty discharging patients as measured by alternate level of care (ALC) status. RESULTS: During the study period, 36,892 patients had 44,066 hospitalizations. High cost patients (n = 1,844) accounted for 38 % of total inpatient spending ($122 million) and were older, more likely to be male, and had higher levels of co-morbidity compared to non-high cost patients. In over half of the high cost cohort (54 %), costs were accumulated from a single hospitalization. The majority of costs were related to nursing care and intensive care unit spending. High cost patients were more likely to have an encounter deemed to be ambulatory care sensitive compared to non-high cost inpatients (6.0 versus 2.8 %, p < 0.001). A greater proportion of inpatient spending was attributable to ALC days for high cost versus non-high cost patients (9.1 versus 4.9 %, p < 0.001). CONCLUSIONS: Within a population of high cost inpatients, the majority of costs are attributed to a single, non-preventable, acute care episode. However, there are likely opportunities to improve hospital efficiency by focusing on different approaches to community based care directed towards specific populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".