Patient-, care partner–, and clinician- proposed solutions to address the time toxicity of oncology care.
Bibliographic record
Abstract
370 Background: As the concept of time toxicity has gained traction, work has largely focused on mapping and measuring time burdens. This qualitative study sought to elicit perspectives from people with lived or professional experience with cancer on strategies to decrease the time burden of oncology care. Methods: We conducted semi-structured interviews with 47 participants (16 patients with advanced gastrointestinal cancer, 15 informal care partners, and 16 oncology clinicians with diverse roles) from one academic cancer center in Minneapolis, Minnesota. Interviews were conducted between 2/2023-10/2023 and transcribed and analyzed using a hybrid approach. Results: We identified 24 subthemes that we grouped into 5 themes (Table 1): (1) Personalized ambulatory care scheduling taking into account patient/ care partner needs and preferences; (2) Support for administrative and logistical tasks; (3) Home-based care when safe and feasible; (4) Clear and compassionate communication about time burdens; and (5) Innovations in care design and care delivery. Conclusions: Findings from this study will aid the oncology community in considering and designing interventions to decrease the time burdens of cancer care. Implementing individualized, person-centered solutions will require interventions at the patient, provider, health system, and policy levels. Themes, subthemes, and quotations. Theme Subthemes Illustrative quotes Personalized scheduling of ambulatory care Site of care close to home, personalized scheduling, cluster scheduling, cluster scheduling is not always preferred, designated schedulers, ensuring care is necessary "I was going over to [the university hospital] and then they got things set up at [community site] which is just five minutes from me, which made it much more convenient." (Patient) Support for administrative and logistical tasks Support with medical bills, insurance, and paperwork, adequate staffing for support "The bureaucracies of insurance and all of the time that it takes to get people what they need, is a huge stressor for not only patients and families, but also for the staff that have to do it." (Clinician) Home-based care Telemedicine, other home-based services, policy support “My goodness. Not having to come in [and be able to get the pump disconnected at home] was excellent.” (Care partner) Communication regarding time burdens Desire for information and guidance, setting honest expectations, using objective measures of time, using time burdens as a factor in decision-making “I really hate it when the doctor says "it’ll be quick" and then the receptionist books a 3-hour infusion, and then it takes 5 hours.” (Care Partner) Care innovation Shorter courses, oral, less frequent, and faster treatments, digital communication "I was scheduled to have chemo for six months and after this trial, they showed that you could have chemo for three months." (Patient)
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".