Helping Patients With Chronic Conditions Overcome Challenges of High-Deductible Health Plans: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: A growing number of Americans are enrolled in high-deductible health plans (HDHPs). Enrollees in HDHPs, particularly those with chronic conditions, face high out-of-pocket costs and often delay or forgo needed care owing to cost. These challenges could be mitigated by the use of cost-conscious strategies when seeking health care, such as discussing costs with providers, saving for medical expenses, and using web-based tools to compare prices, but few HDHP enrollees engage in such cost-conscious strategies. A novel behavioral intervention could enable HDHP enrollees with chronic conditions to adopt these strategies, but it is unknown which intervention features would be most valued and used by this patient population. OBJECTIVE: This study aimed to assess preferences among HDHP enrollees with chronic conditions for a novel behavioral intervention that supports the use of cost-conscious strategies when planning for and seeking health care. METHODS: In an exploratory sequential mixed methods study among HDHP enrollees with chronic conditions, we conducted 20 semistructured telephone interviews and then surveyed 432 participants using a national internet survey panel. Participants were adult HDHP enrollees with diabetes, hypertension, coronary artery disease, chronic obstructive pulmonary disease, or asthma. The interviews and survey assessed participants' health care experiences when using HDHPs and their preferences for the content, modality, and frequency of use of a novel intervention that would support their use of cost-conscious strategies when seeking health care. RESULTS: Approximately half (11/20, 55%) of the interview participants reported barriers to using cost-conscious strategies. These included not knowing where to find information and worrying that the use of cost-conscious strategies would be very time consuming. Most (18/20, 90%) interviewees who had discussed costs with providers, saved for medical expenses, or used web-based price comparison tools found these strategies to be helpful for managing their health care costs. Most (17/20, 85%) interviewees expressed interest in an intervention delivered through a website or phone app that would help them compare prices for services at different locations. Survey participants were most interested in learning to compare prices and quality, followed by discussing costs with their providers and putting aside money for care, through a website-based or email-based intervention that they would use a few times a year. CONCLUSIONS: Regular use of cost-conscious strategies could mitigate financial barriers faced by HDHP enrollees with chronic conditions. Interventions to encourage the use of cost-conscious strategies should be delivered through a web-based modality and focus on helping these patients in navigating their HDHPs to better manage their out-of-pocket spending.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,024 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».