Development Of A Commercialisation Strategy For A Medical Device Company In Canada : Bridging the gap between innovation and market entry
Bibliographic record
Abstract
The rapid growth of the older population coupled with the rise in chronic diseases has intensified the demand for advanced medical treatments. The COVID-19 outbreak was a wake-up call for the healthcare system, revealing the limitations of the hospital-centric model of care. The crisis highlighted the significance of patient-centric models of care. But penetrating such regulated industry is a challenge. The case company (Company A) intends to penetrate the Canadian market and identify the barriers to commercialisation. In this research, the author utilized an online qualitative survey coupled with semi-structured interviews with medical device executives in Canada to identify the barriers to commercialisation. This research employed an exploratory approach by thoroughly reviewing existing literature on the commercialisation of medical devices, while paying special attention to the transition from ideation to market entry. The data obtained on the barriers to commercialisation was divided into five themes, namely: regulations, funding gaps, partnerships, branding, and geographical location. The results pointed out that a regulatory strategy was most relevant to the commercialization of medical devices. A lack of clarity in the Canadian regulatory process was also identified as the major impediment to medical device commercialisation in Canada. The outcome of this research is the business model canvas for Company A, which includes building blocks such as key partners, key activities, resources, value proposition, customer relationships, channels, customer segments, cost structure, and revenue stream. A roadmap to commercialisation of a medical device in Canada was also provided. Company A can use the proposed roadmap to guide its commercialisation and market entry strategies in Canada.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".