Providing Value to New Health Technology: The Early Contribution of Entrepreneurs, Investors, and Regulatory Agencies
Bibliographic record
Abstract
BACKGROUND: New technologies constitute an important cost-driver in healthcare, but the dynamics that lead to their emergence remains poorly understood from a health policy standpoint. The goal of this paper is to clarify how entrepreneurs, investors, and regulatory agencies influence the value of emerging health technologies. METHODS: Our 5-year qualitative research program examined the processes through which new health technologies were envisioned, financed, developed and commercialized by entrepreneurial clinical teams operating in Quebec's (Canada) publicly funded healthcare system. RESULTS: Entrepreneurs have a direct influence over a new technology's value proposition, but investors actively transform this value. Investors support a technology that can find a market, no matter its intrinsic value for clinical practice or healthcare systems. Regulatory agencies reinforce the "double" value of a new technology-as a health intervention and as an economic commodity-and provide economic worth to the venture that is bringing the technology to market. CONCLUSION: Policy-oriented initiatives such as early health technology assessment (HTA) and coverage with evidence may provide technology developers with useful input regarding the decisions they make at an early stage. But to foster technologies that bring more value to healthcare systems, policy-makers must actively support the consideration of health policy issues in innovation policy.
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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 | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".