Populations and Health Domains Served by Direct-to-Consumer Digital Health Companies in the United States, 2011-2023: Cross-Sectional Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".