Incremental costs of hospital-acquired complications in Alberta, Canada
Bibliographic record
Abstract
Hospital-acquired diagnoses (HAD) not only lengthen inpatients’ recovery times but also incur significant additional costs of care. The focus of previous research has been on ‘highly preventable’ indicator conditions and the cost of individual episodes, rather than on the entire spectrum of unintentional patient harm. As well, little attention has been paid to the frequency of HADs and resulting total costs. The objective of this study was to estimate the incremental cost, aggregated system costs, and length-of-stay effects of hospital-acquired diagnoses in eight Alberta (Canada) hospitals. Routinely coded diagnosis data, combined with a Present-on-Admission (POA) flag, were used to group 206,011 inpatient records into the 144 classes of the Classification of Hospital Acquired Diagnoses (CHADx). In Alberta’s larger hospitals, costs are measured using sophisticated bottom-up, patient-level costing systems. We employed a generalized linear model (GLM) with a gamma distribution using a log link relationship between the total cost of hospitalization and all 144 CHADx groups, after controlling for in-hospital death, one-day hospitalization, and the mean of uncomplicated cases in each CaseMix Group (CMG). Nearly a quarter of the sample (23.9%) had at least one recorded hospital-acquired diagnosis. Across all cases, any HAD was associated with increased costs of C$10,866, more than double the mean cost of an uncomplicated admission, with a mean of 4.7 additional days of stay. CHADx representing the highest per-episode median incremental cost included multi-drug resistant Staph aureus (CHADx 4.3, C$11,357), falls with fractured neck of femur (CHADx 3.1, C$6,679), and pressure ulcers (CHADx 8.1, C$6,512). Twenty-two CHADx added > 2 days to the median for a similar but uncomplicated stay. Taking the volume of cases into account, and using an approximation of the mean incremental cost to capture all system costs, urinary tract infection (CHADx 9.2) was the most costly, adding C$19.3 million to system costs. CHADx responsible for the greatest extension of LOS (length of stay) across the system were similar to those adding the greatest costs, with the notable additions of Clostridium difficile infection (CHADx 7.3, +8,813 days) and septicaemia (CHADx 4.1, +6,284). High-level grouping of CHADx showed hospital-acquired infections to be the mostly costly type of complication, adding C$49.6 million, although this finding is sensitive to the way in which HAD conditions are grouped.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".