Promoting Business and Entrepreneurial Awareness in Health Care Professionals: Lessons From Venture Capital Panels at Medicine 2.0 Conferences
Bibliographic record
Abstract
There are few mechanisms that bring the academic and business worlds together in a way that would maximize the success of health technology (health tech) start-ups by increasing researchers' knowledge about how to operate in the business world. Existing solutions (eg, technology transfer offices and dual degree MD/MBA programs) are often unavailable to researchers from outside the institution or to those who have already completed their primary education, such as practicing physicians. This paper explores current solutions and offers a partial solution: include venture capital (VC) panels in medical conferences. These VC panels educate academics on 2 important and interconnected issues: how to "pitch" their ideas in the business world and what to consider when creating a company. In these sessions, academia-based start-up companies present their ideas before a VC panel composed of professional investors and receive feedback on their idea, business plan, and presentation techniques. Recent panel recommendations from Medicine 2.0 conferences fell into 7 categories: (1) the product, service, or idea you are developing into a company, (2) determine market forces and identify the target audience, (3) describe your competitive advantage, (4) the business plan, (5) current and future resources and capabilities, (6) legal aspects, and (7) general advice on the art of pitching. The academic and business literature validates many of these recommendations suggesting that VC panels may be a viable and cost-effective introduction to business and entrepreneurial education for physicians and other health care professionals. Panels benefit not only the presenting companies, but also the physicians, psychologists, and other health care professionals attending the session. Incorporating VC panels into academic conferences might also illuminate the need for incorporating relevant business training within academia.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".