Using information technology (IT) to improve communication between the emergency room and cancer clinic.
Bibliographic record
Abstract
158 Background: A communication gap was identified at the Cancer Centre of Southeastern Ontario which resulted in oncologists at the cancer centre (CC) not consistently being informed when oncology patients (pt) were seen in the emergency room (ER). An automated IT notification system was developed to inform oncologists when their pt were seen in the ER. Methods: Pt who had been seen within 60 days in the CC were considered active. An IT notification system linked active CC visits and ER visits. A list was generated daily of pt that were both CC and in the ER and this was emailed through a secure system to treating oncologists. Following six months of usage an evaluation was performed which identified which pt were being seen in ER (by disease site, treatment regimen and other factors). A survey evaluated oncologist feedback regarding usefulness of this system. Results: From December 2014 to May 2015 notifications were analyzed. A total of 773 pt (12% of total active cancer patients) had an ER visit resulting in 1267 visits; 56% of pt had more than one visit. Thirty-three percent of visits result in admission to the hospital. The highest usage of the ER was palliative care (26% of pt) and hematology (29% of visits). The risk of dying in the three months following an ER visit was 20% as compared to < 2% for all pt seen in the ER during that time period. Sixty-three percent of the 41 oncologists responded to a survey. The majority used this notification system (85%) and found that the report improved patient care (87%). Oncologists typically used this information to check up on pt information on the electronic medical record and if appropriate visited pt that had been admitted to hospital. Conclusions: Cancer pt who have an ER visit are now automatically being reported to the treating oncologists. This process has been able to identify at risk pt for ER visits and this information will be used to develop targeted measures for these high risk populations. The report is very popular with oncologists and is now considered standard practice at our CC.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".