Identifying Potentially Avoidable Hospital Admissions From Canadian Long-Term Care Facilities
Bibliographic record
Abstract
BACKGROUND: The provision of preventive services and continuity of care are important aspects of long-term care (LTC). A proposed quality indicator of such care is the rate of hospitalizations due to ambulatory care sensitive conditions (ACSCs). As the ACSC approach to identifying potentially avoidable hospitalizations (PAH) was developed for younger community-dwelling adults in the United States, we sought to examine its applicability as a quality indicator for older institutionalized residents in Canada. METHODS: ACSCs were identified in a linked hospital-based LTC and acute care administrative database at the Institute for Clinical Evaluative Sciences in Ontario, Canada. An expert panel was then convened to assess the applicability of existing ACSCs to an older institutionalized population in Canada and to develop consensus-based revisions appropriate to this setting. The revised definition of PAH was then applied to the same linked database. RESULTS: The proportion of hospitalizations categorized as a PAH using the original ACSCs was 47% (4177 of 8885). The panel suggested the inclusion of 2 new conditions (septicemia and falls/fractures) coupled with the deletion of 4 of the original ACSCs (immunization-preventable conditions; nutritional deficiency; severe ear, nose and throat infections; tuberculosis) that were rare hospital diagnoses in this population. Using the revised definition, 55% of hospitalizations (4874) were identified as potentially avoidable. CONCLUSIONS: Changes to the original list of ACSCs led to more hospitalizations being categorized as potentially avoidable. Significant variation between LTC facilities and over time in our PAH indicator may identify areas for improvement in preventive services and continuity of care for LTC residents.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".