Involving Patients and Clinicians in the Design of Wireframes for Cancer Medicines Electronic Patient Reported Outcome Measures in Clinical Care: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Cancer treatment is a key component of health care systems, and the increasing number of cancer medicines is expanding the treatment landscape. However, evidence of the impact on patients has been focused more on chemotherapy toxicity and symptom control and less on the effect of cancer medicines more broadly on patients' lives. Evolving electronic patient-reported outcome measures (ePROMs) presents the opportunity to secure early engagement of patients and clinicians in shaping the collection of quality-of-life metrics and presenting these data to better support the patient-clinician decision-making process. OBJECTIVE: The aim of this study was to obtain initial feedback from patients and clinicians on the wireframes of a digital solution (patient app and clinician dashboard) for the collection and use of cancer medicines ePROMs. METHODS: We adopted a 2-stage, mixed methods approach. Stage 1 (March to June 2019) consisted of interviews and focus groups with cancer clinicians and patients with cancer to explore the face validity of the wireframes, informed by the technology acceptance model constructs (perceived ease of use, perceived usefulness, and behavioral intention to use). In stage 2 (October 2019 to February 2020), the revised wireframes were assessed through web-based, adapted technology acceptance model questionnaires. Qualitative data (stage 1) underwent a framework analysis, and descriptive statistics were performed on quantitative data (stage 2). Clinicians and patients with cancer were recruited from NHS Greater Glasgow & Clyde, the largest health board in Scotland. RESULTS: A total of 14 clinicians and 19 patients participated in a combination of stage 1 interviews and focus groups. Clinicians and patients indicated that the wireframes of a patient app and clinician dashboard for the collection of cancer medicines ePROMs would be easy to use and could focus discussions, and they would be receptive to using such tools in the future. In stage 1, clinicians raised the potential impact on workload, and both groups identified the need for adequate IT skills to use each technology. Changes to the wireframes were made, and in stage 2, clinicians (n=8) and patients (n=16) indicated it was "quite likely" that the technologies would be easy to use and they would be "quite likely" to use them in the future. Notably, clinicians indicated that they would use the dashboard to enable treatment decisions "with around half" of their patients. CONCLUSIONS: This study emphasizes the importance of consulting both patients and clinicians in the design of digital solutions. The wireframes were perceived positively by patients and clinicians who were willing to use such technologies if available in the future as part of routine care. However, challenges were raised, and some differences were identified between participant groups, which warrant further research.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".