The Healthcare Cost Burden of Acute Myocardial Infarction in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Little is known about the cost burden of acute myocardial infarction (AMI) on healthcare systems. Accordingly, we examined the long-term trends of healthcare costs for AMI in the province of Alberta, Canada. METHODS: We linked five Albertan health databases, including ambulatory care, hospitalization, practitioner claims, pharmaceutical information network, and population registry to identify patients with a primary diagnosis of AMI between 2004 and 2013. We used the Alberta Interactive Health Data Application to provide unit costs for ambulatory care and inpatient services, claim paid amounts for physician services, and the Alberta Drug Benefit List for drug prices. Healthcare costs for AMI were grouped into ambulatory care, hospitalization, physician costs, and drug costs. All costs were converted to 2016 Canadian dollar values ($Can). RESULTS: A total of 52,912 patients with AMI were included in the analysis. Patient age decreased over time, as did the proportion of females. AMI cost the Alberta healthcare system Can$1033 million during the study period; of which the largest proportion was hospitalization costs (Can$716.4 million, 63.1%), followed by drug costs (Can$147.2 million, 21.1%), ambulatory care costs (Can$94.5 million, 8.8%) and physician costs (Can$74.9 million, 7.0%). The cost per AMI hospitalization decreased from Can$14,116 in 2004 to Can$11,792 in 2013 (p < 0.001). CONCLUSIONS: Healthcare costs for AMI are significant; however, they decreased slightly during the study period. Hospital services accounted for the largest share of the costs. There are opportunities for further savings in AMI care.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".