The Role of Academic Health Systems in Leading the “Third Wave” of Digital Health Innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investors, entrepreneurs, health care pundits, and venture capital firms all agree that the health care sector is awaiting a digital revolution. Steven Case, in 2016, predicted a "third wave" of innovation that would leverage big data, artificial intelligence, and machine learning to transform medicine and finally achieve reduced costs, improved efficiency, and better patient outcomes. Academic medical centers (AMCs) have the infrastructure and resources needed by digital health intrapreneurs and entrepreneurs to innovate, iterate, and optimize technology solutions for the major pain points of modern medicine. With large unique patient data sets, strong research programs, and subject matter experts, AMCs have the ability to assess, optimize, and integrate new digital health tools with feedback at the point of care and research-based clinical validation. As AMCs begin to explore digital health solutions, they must decide between forming internal teams to develop these innovations or collaborating with external companies. Although each has its drawbacks and benefits, AMCs can both benefit from and drive forward the digital health innovations that will result from this journey. This viewpoint will provide an explanation as to why AMCs are ideal incubators for digital health solutions and describe what these organizations will need to be successful in leading this "third wave" of innovation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it