Health equity through CMS collaboration with startups and digital health innovations
Bibliographic record
Abstract
As investment in digital health companies has slowed following unparalleled financing during the Covid-19 pandemic, the pressure on existing health technology companies to acquire customers and prove value has grown 1 . Given the predominance of employer-sponsored health insurance in the U.S., many health technology companies have focused their efforts on business-to-business (B2B) models in order to expand their services quickly rather than sticking to direct-to-consumer (D2C) models. However, many Americans are not insured by their employers—18.8% are insured by Medicaid, and 18.7% are covered by Medicare 2 . With a population of 92 million Medicaid and 65 million Medicare patients, the Medicaid and Medicare market has an annual market spend of $728B and $829B, respectively 3 . These patients typically have various complex healthcare barriers, such as lack of access to medications, challenges with health literacy, and difficulty with transportation. The myriad of inequities that these patients experience are areas that are often overlooked and, as such, are desperate for innovation.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.032 | 0.029 |
| Insufficient payload (model declined to judge) | 0.043 | 0.020 |
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".