Using RFID technology to drive quality improvement.
Bibliographic record
Abstract
70 Background: The Odette Cancer Centre (OCC) is the sixth largest cancer institution in North America and manages over 24,000 chemotherapy patient visits/year. We initiated an automated system whereby patients can “actively” self-check-in using Radio Frequency Identification (RFID) cards to the chemotherapy unit as part of a quality improvement initiative. Methods: Four self check-in kiosks were implemented into the OCC and all patients received a unique identifiable RFID card. Patients “self-arrived” to the OCC upon entry as well as before the nursing assessment. The technology created an electronic data infrastructure to capture patient experience data for ambulatory chemotherapy. In addition, visual management boards have been implemented that display the patients’ unique identifiers and communicates their process status. This tool also acts as a call-in system wherein the screen flashes and prompts the patient to proceed to the service area. Data was extracted electronically from various information systems, consolidated in excel linking information by the patients hospital file number. Results were analyzed. Results: The mean number of patients treated per day is 85 with a range of 65 to 105 (n=853). Median wait time from arrival to chair placement was 1:52 minutes ranging 0:02 to 6:12. There were 43% of patients that had medication ready within 30 minutes of their appointment time. There was an observed reduction in interruptions to the assessment nurses related to patient status updates (data to be presented). Patient satisfaction was high despite the modest improvement in efficiency. Conclusions: A comprehensive business analysis is being performed on the operations of the OCC with the implementation of this technology. The visual management boards enhance communication to patients while increasing privacy and patient confidentiality. The boards allow for patient mobility in the waiting room and eases anxiety associated with being “lost in the queue”. Next steps for the OCC are to create a data cube linked with other systems to further enrich the data. This technology enabled data analysis, evaluated the impact of change, set baseline targets for performance and built continuous quality improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".