Construction of a Business Model to Assure Financial Sustainability of Biobanks
Bibliographic record
Abstract
Biobank-suisse (BBS) is a collaborative network of biobanks in Switzerland. Since 2005, the network has worked with biobank managers towards a Swiss biobanking platform that harmonizes structures and procedures. The work with biobank managers has shown that long-term, sustainable financing is difficult to obtain. In this report, three typical biobank business models are identified and their characteristics analyzed. Five forces analysis was used to understand the competitive environment of biobanks. Data provided by OECD was used for financial estimations. The model was constructed using the business model canvas tool. The business models identified feature financing influenced by the economic situation and the research budgets in a given country. Overall, the competitive environment for biobanks is positive. The bargaining power with the buyer is negative since price setting and demand prediction is difficult. In Switzerland, the healthcare industry collects approximately 5600 U.S. dollars per person and year. If each Swiss citizen paid 0.1% (or 5 U.S. dollars) of this amount to Swiss biobanks, 45 million U.S. dollars could be collected. This compares to the approximately 10 million U.S. dollars made available for cohort studies, longitudinal studies, and pathology biobanks through science funding. With the same approach, Germany, the United States, Canada, France, and the United Kingdom could collect 361, 2634, 154, 264, and 221 million U.S. dollars, respectively. In Switzerland and in other countries, an annual fee less than 5 U.S. dollars per person is sufficient to provide biobanks with sustainable financing. This inspired us to construct a business model that not only includes the academic and industrial research sectors as customer segment, but also includes the population. The revenues would be collected as fees by the healthcare system. In Italy and Germany, a small share of healthcare spending is already used to finance selected clinical trials. The legal frameworks could serve as templates for the business model proposed here.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".