Convenient Access to Expert-Reviewed Health Information via an Alexa Voice Assistant Skill for Patients With Multiple Myeloma: Development Study
Notice bibliographique
Résumé
BACKGROUND: Patients with multiple myeloma (MM) have high information needs due to the complexity of the disease and variety of treatments. Digital voice assistants provide support in daily life and can be a convenient tool that even older patients can use to access health information. Voice assistants may therefore be useful in providing digital health services to meet the information needs of patients with MM. OBJECTIVE: We aim to describe and report on the development, content, and functionality of the first Amazon Alexa voice assistant skill for patients with MM in Germany with the goal of empowering and educating patients. Further, we share data on skill usage and first learnings. METHODS: In a cocreation workshop with MM patient organizations and MM medical experts in Germany, Takeda Oncology discussed the development and content of the Alexa skill Multiple Myeloma. Patient information on MM disease, diagnostics, and therapy was presented in a question-and-answer format, reviewed by experts, and programmed into the skill. Additionally, a search function for finding patient support groups within a perimeter of 200 km around the users and a myeloma quiz functionality with multiple-choice questions were integrated into the skill. Aggregated retrospective data on the total number of skill installations and skill usage were retrieved from an Amazon Alexa developer account, and a web-based patient survey was conducted on the Takeda Oncology website. RESULTS: The Alexa skill Multiple Myeloma was launched in September 2019. It was available free of charge on the German Amazon Alexa skill store between September 2019 and March 2022 and could be used with devices featuring the Amazon Alexa voice assistant. Since the launch in September 2019 and up to July 2021, a total of 141 users have installed the skill. Between July 2020 and July 2021, a total of 189 skill sessions with 797 utterances were analyzed. The most popular inquiries were searches for patient support groups near the users (58/797, 7.3%), followed by inquiries about information on MM disease (53/797, 6.6%) and the quiz (43/797, 5.4%). The web-based survey on voice assistant usage and the feedback on the Alexa skill Multiple Myeloma were collected from 24 participants and showed that 46% (11/24) of participants would recommend the Alexa skill. Nonusers of voice assistants (11/24, 46%) stated that data protection concerns (7/11, 64%) and a lack of need (6/11, 55%) were the most important factors of not using voice assistants. CONCLUSIONS: The Alexa skill Multiple Myeloma offers patient-friendly and expert-reviewed answers and explanations for medical terms related to MM disease, diagnostics, and therapy, as well as connections to patient support groups and a quiz functionality. In the future, the skill can be extended with new content and functionalities, such as medication adherence support.
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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 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».