High-needs hematology/oncology patients: A quality improvement project.
Bibliographic record
Abstract
197 Background: St. Michael’s Hospital (SMH) is an academic, inner-city hospital in Toronto, Canada. In the hematology/oncology (hem/onc) program, a small number of patients appeared to contribute disproportionately to hospital admissions and emergency department (ED) visits. We hypothesized that high needs hem/onc patients could be recognized early in their care and that ED visit and admission rates among these patients could be decreased through targeted interventions. Methods: Members of the hem/onc team were interviewed regarding characteristics, which they felt predicted higher needs and greater liklihood for hospital admission/ED visit. A list of high risk features was generated. ED visit and admission rates for a prospectively identified high needs cohort were compared to rates for the entire hem/onc clinic. An intervention targeting high needs hem/onc patients is on-going. Pre and post-intervention ED visit and admission rates will be compared. Results: Interviews with 3 nurses, 1 social worker, 1 discharge planner, and 4 physicians identified 10 factors that the hem/onc team believed were predictive of higher needs and subsequent higher ED visit and admission rates. Between December 1, 2012, and February 28, 2013, 42 high needs hem/onc out-patients were prospectively identified. The ED visit and admission rates for this cohort were retrospectively compared to those of the entire hem/onc clinic and found to be dramatically higher (Table). Begininng in June 2013, hem/onc patients identified as “high needs” were offered enrollment in a NP-based program offering telephone assessments following ED visits, hospital admissions or discharges. Assessment of the impact of this intervention is ongoing. Conclusions: It is possible to prospectively identify hem/onc patients who are at risk of higher than usual ED visit and admission rates. Identifying this population may provide an opportunity to decrease their ED visit and admission rates. An evaluation of an intervention targeting high needs hem/onc patients is ongoing. Preliminary data will be presented. [Table: see text]
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".