Examination of High-Cost Patients in Ontario
Bibliographic record
Abstract
IntroductionIn Ontario, the top 5% of high-cost users account for 66% of health care costs. The heavy use of resources combined with perceived inefficiencies offer an imperative to target strategies to redesign care to better meet patient needs and increase value. Objectives and ApproachAs part of a request submitted to the Applied Health Research Question (AHRQ) review team, the main objective of this study was to identify drivers of high health care use in Ontario in order to find better ways to improve the efficiency in healthcare delivery. Using data in fiscal year 2012/13, characteristics of the top 5% of high costs users were described, and further stratified by mental health status. Total spending by sector of care were also described. Data were linked including physician, hospital, medication and long term care databases for each patient. ResultsIn the top 5% of high-cost users, there were 729,870 patients who accounted for $20,179,208,348 of total healthcare spending in 2012/13, with the highest percentage of spending observed among older adults aged 61-80 years old. Mental health high-cost patients accounted for 6.1% of these patients, of which 51.5% were female, had a low socio-economic status and an average age of 44 years. These patients had an average of 4.9 (SD=2.3) ICD chapters and used an average of 8.7 (SD=3.8) drugs. Using the health accounts methodology (ICHA), as described by the OECD and WHO, over 90% of healthcare costs among the top 5% of high-cost patients were from inpatient care, day surgery and clinic care, physician care, outpatients drugs and inpatient rehabilitation and complex/continuing care. Conclusion/ImplicationsThis study provides a systematic description of the needs in a high cost patient group, and serves as a platform for international comparisons across healthcare systems to better understand gaps and identify targets for intervention. These cross-comparisons offer a tool to evaluate performance of healthcare systems and to prioritize policies.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 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".