Innovation Procurement in Health Systems: Exploring Practice and Lessons Learned
Bibliographic record
Abstract
As rising healthcare costs continue to challenge the sustainability of global health systems, there has been a strategic shift toward a focus on value, which considers the outcomes and value of healthcare delivery relative to the costs of care delivery. A unique feature of this focus on value has influenced a shift in procurement whereby health organizations are advancing the procurement of innovative solutions to achieve defined outcomes that overcome challenges such as the quality, safety and cost of care delivery. In this paper, we report on the implementation of three innovation procurement models in four Ontario healthcare organizations. These case studies provide evidence of the value and impact of innovation procurement approaches emerging from the four healthcare organizations. Three models of innovation procurement are described in the four cases, along with qualitative analysis of experiences and outcomes for both the organizations and the participating vendors. Evidence of the value and impact of procuring innovative solutions to address health organization challenges offers insights and new approaches to leveraging public procurement methodologies to achieve value and impact for health systems.
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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 | 0.052 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".