Identifying potentially preventable emergency department (PPED) visits among patients with cancer in Ontario.
Bibliographic record
Abstract
25 Background: Cancer patients (pts) often visit the emergency department (ED) when symptoms and side effects occur. Growing evidence suggests that some treatment-related toxicities can be managed proactively in outpatient clinics, improving patient experience and optimizing acute care utilization. Understanding PPED visits is crucial to developing and evaluating such improvement efforts. Our objectives were to quantify the extent of PPED visits in Ontario among cancer pts and identify the best measure of PPED for province-wide quality improvement. Methods: By linking Activity Level Reporting to the Discharge Abstract Database and the National Ambulatory Care Reporting System, we identified cancer pts who had ED visits or hospitalizations up to 30 days after receiving chemotherapy and/or radiotherapy from April 1, 2014 to March 31, 2015 in Ontario. Episodes were stratified as ED Only or Indirect Admission (ED visit leading to hospitalization). We mapped the presenting Canadian Emergency Department Information System (CEDIS) complaints against the PPED metric proposed by Panattoni et al (JOP, 2018) that combined the CMS preventable visits typology with the STAR PRO tool (Basch et al, JCO, 2016) which found 49.8% were considered potentially preventable (PP). Results: We identified 43,593 ED visits (67% ED Only& 33% Indirect Admissions) among 64,407 pts. The most common presenting CEDIS complaints were pain (20%), fever (13%) and shortness of breath (SoB, 7%) among chemotherapy pts, and pain (19%), SoB (11%) and general weakness (9%) among radiotherapy pts. By applying the CEDIS-based PPED definition, which includes 17 presenting complaints, 50% of ED Only and 68% of Indirect Admission visits were considered PPED. Conclusions: We were able to adapt the PPED algorithm for the Canadian context, which can aid cross-jurisdiction comparisons. We found a substantial proportion of PPED visits. While common presenting complaints had face validity for being PP and a similar proportion of visits were PP compared to the US-based definition, further validation of this approach against healthcare records and in other jurisdictions would be helpful as these metrics become increasingly used for quality improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".