Preferences and Willingness to Pay for Health App Assessments Among Health Care Stakeholders: Discrete Choice Experiment
Notice bibliographique
Résumé
BACKGROUND: The adoption of high-quality health apps has been slow, despite the myriad benefits associated with their use. This is partly due to concerns regarding the effectiveness, safety, and data privacy of such apps. Quality assessments with robust and transparent criteria can address these concerns and, thereby, encourage the use of high-quality apps. However, a major challenge for such assessments is reaching a scale at which a substantial proportion of the more than 350,000 available health apps can be evaluated. OBJECTIVE: To support the scaling of health app quality assessments, this study aimed to examine the preferences and willingness to pay for assessments with different value propositions among potential customers. METHODS: We conducted 2 discrete choice experiments: one with 41 health app developers and another with 46 health system representatives (from health care institutions, authorities, and insurers) from across Europe. Mixed logit models were applied to examine the impact of assessment attributes on participants' choices as well as to calculate marginal willingness to pay and predicted assessment uptake. RESULTS: Among health app developers, the attributes with the largest impact on assessment choices were the associated clinical care uptake (integration into clinical guidelines and reimbursement or procurement) and cost (purchase price). Increased willingness to use assessed apps and app store integration of assessment results had a moderate impact on choices, while required developer time investment and time until assessment results become available made the smallest contribution. Among health system representatives, increased willingness of clinicians and patients to use evaluated apps had the greatest impact on assessment choices, followed by cost. Time until assessment result availability and the percentage of peers recommending the assessment made a moderate contribution, while reassessment frequency had the smallest impact on choices. On average, health app developers were willing to pay an additional €9020 (95% CI €4968-€13,072) if an assessment facilitates guideline integration and procurement or reimbursement (at the time of data collection, €1=US $1.11), while health system representatives were, on average, willing to pay €7037 (95% CI €4267-€9806) more if an assessment results in a large, rather than a small, increase in willingness to use the evaluated app. The predicted uptake of assessments that offer the preferred values for all attributes was 88.6% among app developers and 91.1% among health system representatives. CONCLUSIONS: These findings indicate that, to maximize uptake and willingness to pay among health app developers, it is advisable for assessments to facilitate or enable clinical guideline integration and reimbursement or procurement for high-scoring apps. Assessment scaling thus requires close collaboration with health authorities, health care institutions, and insurers. Furthermore, if health system organizations are targeted as customers, it is essential to provide evidence for the assessment's impact on patients' and clinicians' willingness to use health apps.
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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 | 0,018 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».