Defining user needs for an electronic tool to improve chemotherapy-related toxicity management.
Bibliographic record
Abstract
157 Background: Cancer drugs are associated with toxicities which can negatively impact patients’ (pts) quality of life, outcomes and increase acute care use (ACU). There is increasing interest in leveraging technology to solve clinical problems in healthcare. We hypothesized that an electronic tool (toxicity module) targeting management of chemotherapy toxicities could decrease ACU by facilitating more effective symptom management. Methods: Participatory design methodology consisting of end user needs assessment through ethnographic field study (shadowing and in-depth interviews) and focus groups was used to inform design of an interactive prototype toxicity module. Oncology pts and their caregivers, and health care providers (HCPs) including oncologists, oncology nurses and primary care providers were included in all stages of development. Contemporaneous notes were taken during ethnography while focus groups were also audio recorded. Thematic analysis through ideation sessions and time-of-day exercises allowed identification of overarching issues. Results: Eight pts and 8 HCPs participated in the ethnographic field study. Two focus groups, one with 7 pts, one with 4 HCPs were held. Most themes were common to both pts and HCPs; gaps and barriers in the current system, need for decision aids, improved HCP communication and options in care delivery, and access to credible information delivered in a timely, secure manner and integrated into existing systems. Additionally, pts further identified missed opportunities, care not meeting their needs, feeling overwhelmed and anxious and wanting to be more empowered; HCPs identified accountability as an issue. These themes informed development of a prototype for a web-based toxicity management tool, which has served the purpose of defining user needs for symptom tracking, self-management advice, and timely communication with an oncology provider. Iterative evaluation over 2 rounds of usability testing is currently underway. Conclusions: An electronic tool that integrates just-in-time self-management advice and oncology provider support into routine care may address some of the gaps identified in the current system for managing chemotherapy toxicity.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".