User-Centered Design of a Web-Based Tool to Support Management of Chemotherapy-Related Toxicities in Cancer Patients (Preprint)
Bibliographic record
Abstract
BACKGROUND Cancer patients receiving chemotherapy have high symptom needs that can negatively impact quality of life and result in high rates of unplanned acute care visits. Remote monitoring tools may improve symptom management in this patient population. OBJECTIVE This study aimed to design a prototype tool to facilitate remote management of chemotherapy-related toxicities. METHODS User needs were assessed using a participatory, user-centered design methodology that included field observation, interviews, and focus groups, and then analyzed using affinity diagramming. Participants included oncology patients, caregivers, and health care providers (HCPs) including medical oncologists, oncology nurses, primary care physicians, and pharmacists in Ontario, Canada. Overarching themes informed development of a Web-based prototype, which was further refined over 2 rounds of usability testing with end users. RESULTS Overarching themes were derived from needs assessments, which included 14 patients, 1 caregiver, and 12 HCPs. Themes common to both patients and HCPs included gaps and barriers in current systems, need for decision aids, improved communication and options in care delivery, secure access to credible and timely information, and integration into existing systems. In addition, patients identified missed opportunities, care not meeting their needs, feeling overwhelmed and anxious, and wanting to be more empowered. HCPs identified accountability for patient management as an issue. These themes informed development of a Web-based prototype (bridges), which included toxicity tracking, self-management advice, and HCP communication functionalities. Usability testing with 11 patients and 11 HCPs was generally positive; however, identified challenges included tool integration into existing workflows, need for standardized toxicity self-management advice, issues of privacy and consent, and patient-tailored information. CONCLUSIONS Web-based tools integrating just-in-time self-management advice and HCP support into routine care may address gaps in systems for managing chemotherapy-related toxicities. Attention to the integration of new electronic tools into self-care by patients and practice was a strong theme for both patients and HCP participants and is a key issue that needs to be addressed for wide-scale adoption.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".