Consuming Patients’ Days: Time Spent on Ambulatory Appointments by People With Cancer
Bibliographic record
Abstract
BACKGROUND: In qualitative work, patients report that seemingly short trips to clinic (eg, a supposed 10-minute blood draw) often turn into "all-day affairs." We sought to quantify the time patients with cancer spend attending ambulatory appointments. METHODS: We conducted a retrospective study of patients scheduled for oncology-related ambulatory care (eg, labs, imaging, procedures, infusions, and clinician visits) at an academic cancer center over 1 week. The primary exposure was the ambulatory service type(s) (eg, clinician visit only, labs and infusion, etc.). We used Real-Time Location System badge data to calculate clinic times and estimated round-trip travel times and parking times. We calculated and summarized clinic and total (clinic + travel + parking) times for ambulatory service types. RESULTS: We included 435 patients. Across all service day type(s), the median (IQR) clinic time was 119 (78-202) minutes. The estimated median (IQR) round-trip driving distance and travel time was 34 (17-49) miles and 50 (36-68) minutes. The median (IQR) parking time was 14 (12-15) minutes. Overall, the median (IQR) total time was 197 (143-287) minutes. The median total times for specific service type(s) included: 99 minutes for lab-only, 144 minutes for clinician visit only, and 278 minutes for labs, clinician visit, and infusion. CONCLUSION: Patients often spent several hours pursuing ambulatory cancer care on a given day. Accounting for opportunity time costs and the coordination of activities around ambulatory care, these results highlight the substantial time burdens of cancer care, and support the notion that many days with ambulatory health care contact may represent "lost days."
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".