Patient identification and tracking for chemotherapy delivery: Use of RFID or barcode technologies for automated self check-in.
Bibliographic record
Abstract
316 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 kiosk system whereby patients can “actively” self-check-in to the chemotherapy unit as part of a quality initiative to improve one of the most complex processes in patient care. Methods: From January-May 2012, consecutive patients receiving >2 cycles of chemotherapy were randomly assigned to either radio-frequency identification (RFID) or barcode technologies to facilitate self check-in and time-in-motion studies. In parallel, the former manual check-in system (with OCC staff) continued. The primary outcome was the proportion of patients with more 3 or more scheduled appointments who used the self-check system at least 2 times. Patient satisfaction was attained with baseline and post-study surveys. Results: The study accrued 81 patients (43 patients using RFID and 38 patients using barcodes). Mean age was 59 (20-81 years). Of 48 patients who completed baseline surveys, most had regular access to a computer (87.5%) and used the internet at least >1 hour/day (50%). However, 21% at baseline felt a person-to-person check-in was preferable to an automated option. With implementation of the study, 24 of 81 patients (29%) have used the kiosk only once. Of individuals with multiple scheduled appointments (at least 3), 50% assigned to the RFID group and 52.6% assigned to the barcode group used the kiosk at least 2 times (p=0.827; Fisher’s exact). In follow-up, 96.7% of patients agreed or strongly agreed that the kiosks were easy to use although only one-third (33.3%) of patients felt the new system improved the efficiency of care. Conclusions: An automated check-in process is feasible for a diverse population of patients receiving chemotherapy. Multiple uses of the kiosk technology suggest appropriate uptake and retention of the technology. Continued use of the system was not different between RFID and barcode technologies. Patient satisfaction was high despite the lack of improvement in efficiency. The next phase will incorporate patient tracking and real-time status updates to address these concerns.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".