Innovation Centers in Health Care Delivery Systems: Structures for Success
Bibliographic record
Abstract
The need to support innovation in health care delivery was prompted by payment reforms and access to digital tools and has been accelerated by the shift to virtual care as part of the COVID-19 pandemic response. Prior to the pandemic, a growing number of health systems set up innovation centers to focus on creating new services and exploring new business models relevant to value-based care. This is distinct from process improvement or implementation science, and often needs a different set of incentives to succeed within a large organization. We used a national survey to identify a diverse sample of innovation centers, and interviewed leaders to describe their aims, organizational structures, and activities. They all aim to improve patient outcomes and experience while reducing costs, but their strategic focus may differ. The centers also vary in their reporting structure, how they build internal capacity, and how they measure success. We highlight the range of strategies through examples of projects that improve quality, reduce costs, and generate new revenue. While the optimal forms and impact of innovation centers are still emerging, the fiscal pressures and the rapid uptake of digital technologies present opportunities for the redesign of health services in the postpandemic era. The experiences of these centers illustrate a set of approaches to increase any organization's capacity for innovation.
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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 | 0.051 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.047 | 0.033 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".