Using a Twitter Chat to Rapidly Identify Barriers and Policy Solutions for Metastatic Breast Cancer Care: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Real-time, rapid assessment of barriers to care experienced by patients can be used to inform relevant health care legislation. In recent years, online communities have become a source of support for patients as well as a vehicle for discussion and collaboration among patients, clinicians, advocates, and researchers. The Breast Cancer Social Media (#BCSM) community has hosted weekly Twitter chats since 2011. Topics vary each week, and chats draw a diverse group of participants. Partnering with the #BCSM community, we used Twitter to gather data on barriers to care for patients with metastatic breast cancer and potential policy solutions. Metastatic breast cancer survival rates are low and in large part conditioned by time-sensitive access to care factors that might be improved through policy changes. OBJECTIVE: This study was part of an assessment of the barriers to care for metastatic breast cancer with the goal of offering policy solutions for the legislative session in California. METHODS: We provided 5 questions for a chat specific to metastatic breast cancer care barriers and potential policy solutions. These were discussed during the course of a #BCSM chat on November 18, 2019. We used Symplur (Symplur LLC) analytics to generate a transcript of tweets and a profile of participants. Responses to the questions are presented in this paper. RESULTS: There were 288 tweets from 42 users, generating 2.1 million impressions during the 1-hour chat. Participants included 23 patient advocates (most of whom were patients themselves), 7 doctors, 6 researchers or academics, 3 health care providers (2 nurses, 1 clinical psychologist), and 2 advocacy organizations. Participants noted communication gaps between patient and provider especially as related to the need for individualized medication dosing to minimize side effects and maximize quality of life. Timeliness of insurance company response, for example, to authorize treatments, was also a concern. Chat participants noted that palliative care is not well integrated into metastatic breast cancer care and that insurance company denials of coverage for these services were common. Regarding financial challenges, chat participants mentioned unexpected copays, changes in insurance drug formularies that made it difficult to anticipate drug costs, and limits on the number of physical therapy visits covered by insurance. Last, on the topic of disability benefits, participants expressed frustration about how to access disability benefits. When prompted for input regarding what health system and policy changes are necessary, participants suggested a number of ideas, including expanding the availability of nurse navigation for metastatic breast cancer, developing and offering a guide for the range of treatment and support resources patients with metastatic breast cancer, and improving access to clinical trials. CONCLUSIONS: Rapid assessments drawing from online community insights may be a critical source of data that can be used to ensure more responsive policy action to improve patient care.
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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 | 0,011 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».