<i>The Impact of the after-Hours Carechart Program in Assessing and Triaging after-Hours Patients with Malignancies Inquiries and an Analysis of the Changes to Service Access over the Course of the Covid-19 Pandemic</i>
Bibliographic record
Abstract
Background Oncology patients with hematological or solid organ malignancies have limited access to their clinical team after hours, leading to increased use of emergency services and avoidable hospitalizations. A pilot project, CAREchart, employing a nurse-led after-hours telephone service, reduced emergency department (ED) visits and was subsequently implemented across Ontario, including at the Juravinski Cancer Centre (JCC) in Hamilton, Canada. Methods In this retrospective cohort study, data were abstracted from electronic records, including patient and disease characteristics, reason for calling, and outcomes of the call between Oct 2019 and April 2021. Patients included were aged 18 or older receiving systematic/radiation therapy at JCC. The primary outcome was the proportion of patients accessing CAREChart who were referred to the ED and admitted to the hospital. Secondary outcomes covered the impact of the COVID-19 pandemic wave 1 and 2 on the primary outcome. Results A total of 636 calls were made to the CAREChart program from October 2019 to April 2021. Among these, 199 (31.3 %) of calls were directed to ED, 154 (24.4%) visited ED, out of which 93 (14.6%) required admission. This finding was consistent across different time periods: pre-COVID-19, COVID-19 Wave 1, and COVID-19 Wave 2, with 30.8%, 30.8%, and 31.7% of patients directed to the ED, respectively. Similarly, 10.8%, 15.4%, and 15.6% of patients required admission during the pre-COVID-19 period, COVID-19 Wave 1, and COVID-19 Wave 2, respectively. Forty patients (6.3%) visited the ED, and 27 required admission (4.2%) when they were not initially advised to go to the ED through CAREChart. Conclusions The CAREChart program provides a unique avenue for JCC cancer patients to access care after hours. The program was successful in avoiding >65% of potential ED visits thus reducing hospitalizations in these patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".