Populations and Health Domains Served by Direct-to-Consumer Digital Health Companies in the United States, 2011-2023: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Direct-to-consumer (DTC) digital health companies, offering services such as on-demand prescriptions, mental health apps, fertility tracking, and at-home diagnostics, have become more common in the United States. These companies represent a shift in health care delivery by engaging consumers directly and operating largely outside of traditional health care systems. Despite their increasing presence, little is known about the populations that these companies serve, the health domains they address, and the technologies they use. Understanding these characteristics is critical for evaluating the quality of services provided, implications for health care costs, and impact on health equity. Objective: This study aimed to describe the growth and focus of DTC digital health companies in the United States from 2011 to 2023, examining their target populations, health domains, and differentiating technologies. Methods: We conducted a cross-sectional descriptive analysis using the Rock Health Digital Health Venture Funding Database, which systematically tracks US digital health companies that have received at least US $2 million in publicly disclosed funding. This database was selected because of its scope, consistency, and detailed coding of company characteristics. Of the 2652 digital health companies identified between 2011 and 2023, 478 (18.0%) were classified as exclusively pursuing a DTC model. We extracted and validated data on company characteristics, including founding year, operational status, funding levels, target populations, health domains, and technologies used. Descriptive analyses of frequencies, medians, and IQRs were conducted. Results: Between 2011 and 2023, the number of DTC digital health companies grew steadily, with the highest number founded in 2020 (59/478, 12.3%). As of 2023, 445 (93.1%) of the 478 companies remained active, and 6.9% (n=33) had ceased operations. Across all 478 companies, total venture funding ranged from US $2 million to US $570 million (median US $9.6 million, IQR US $4.0-$25.0 million). Companies focusing on rural or Medicaid populations (n=10, 2.1%) were rare and had lower median funding (median US $5.0 million, IQR US $3.5-$13.4 million). Women were the most targeted population (n=70, 14.6%), followed by children and adolescents (n=36, 7.5%), and older adults (n=25, 5.2%). Mental health was the most common health domain (n=80, 16.7%), followed by reproductive and maternal health (n=71, 14.9%). Telemedicine (n=108, 22.6%), wearables and biosensors (n=93, 19.5%), and artificial intelligence or machine learning (n=63, 13.2%) were the most frequently adopted technologies, with their use varying by population and health domain. Conclusions: As DTC digital health companies increasingly influence where and how care is delivered, systematic monitoring of their scope and characteristics is essential to evaluate whether they contribute to equitable access to care. Our findings provide a foundation for assessing whether these models are effectively addressing health needs, reaching diverse populations, and lowering health care costs.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».