Involving Patients and Clinicians in the Design of Wireframes for Cancer Medicines Electronic Patient Reported Outcome Measures in Clinical Care: Mixed Methods Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,178 | 0,188 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».