Abstract P5-09-06: Patient education for targeted therapies with apps
Notice bibliographique
Résumé
Abstract Background: Explaining targeted therapies to oncology patients is a challenging task for most health care professionals (HCPs). This discussion may need to include the schedule, side effects, benefits, mechanism of action and the rationale of these medications. Despite the high frequency with which HCPs counsel these patients, there is no standardized set of information that serves to meet the challenge of providing this information in a simple, easy to understand format yet, is also amenable to individualization. Informal pictorial patient education by simply drawing on the exam table paper (a picture is better than thousand words) had been used to bridge this gap of information. This experience formed the basis for development of the hand held computer applications. Design/Methods: Three applications (Apps), one each for tamoxifen, aromatase inhibitors (AI's) and Herceptin™ were created, utilizing the IPhone platform with subsequent conversion to the I Pad. The content of these Apps was approved through the LRCP Breast Disease Site Team and Patient Education Committee. The study was approved by the Ethics Committee at the University of Western Ontario. All adult patients with breast cancer undergoing adjuvant treatments with tamoxifen, aromatase inhibitors and/or Herceptin™ were considered eligible. The study was described to patients and consent obtained. The Apps use animated cartoons with limited text to designate the receptors or the medications. The information was verbally reviewed with patients as the HCP showed the animated cartoons through the App. In order to gauge the impact of using these apps as an education tool with patients, a satisfaction survey was designed with 5 questions, using a visual analogue scale where 1 indicated poor agreement and 7 complete agreement. To evaluate the level of understanding achieved, the patients were asked two "exam" questions with a multiple-choice answer format after each App was used by a HCP Results: A total of 64 patients participated, 33 with the AI App, 19 with the tamoxifen App and 12 with the Herceptin™ App. For the "exam questions" segment, there were no wrong answers given by any of the patients using one of the three Apps. The survey questions evaluated the patient's understanding of the mechanism of action, benefits, side effects and the dosing schedule. The App was evaluated for overall preference for the pictorial App presentation format, likelihood of recommending the App to other patients, and feeling more knowledgeable post presentation. The vast majority of patients rated the Apps very highly on all questions with the mean for these questions ranging between 6.67 to 7 on the visual analogue scale. Conclusions: The use of Apps is a novel and effective approach to educate oncology patients regarding complex molecularly targeted treatments. These Apps are quick, easy to use, readily available and demonstrable with smart phones or an I-pad. It also reminds and helps HCPs to provide pertinent information in simple language. From our results, it is quite apparent that patients liked this approach of providing education and counseling and actually understood the contents explained to them. Citation Format: Younus J, Lyn K. Patient education for targeted therapies with apps. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-09-06.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».