Data That Heals: A Three Horizon Analysis
Bibliographic record
Abstract
In recent decades we have seen a major disruption to all major industries as a result of changing consumer demands, consumer empowerment, big data, and shifting demographics. Evidence of this can be seen in the disruption of major industries such as the automotive industry, retail, banking and manufacturing. Data is enabling a shift in power from large companies to smaller, emerging players. \n \nBig Pharma has long enjoyed a privileged position, having grown with the healthcare system since its inception in the United States. We are at a moment in time where there is emerging capability to bring about radical change in the way that healthcare is delivered as a result of new technologies and the advancement of science. Digital Therapeutics and alternatives to pharmacological response to disease are becoming not only viable, but necessary, to contain costs and improve health outcomes. These changes are creating a risk of disruption for Big Pharma, if there is not a radical reassessment of organizational strategies and investment. \n \nUsing the Three Horizon’s Foresight Methodology, this paper explores the needs, barriers and opportunities for each major stakeholder – pharma, emerging technology players, payers, patients and providers – given an overview to the present situation, the ideal future and presenting strategies to bridge these two horizons. The result is an understanding of the potential areas of disruption to health therapeutics industry and an understanding of how the pharmaceutical industry may adapt to build resiliency by transitioning and rethinking its core product, facilitating and partnering with emerging players, and moving towards a strategy of improving health outcomes (real value creation) rather than revenue increases and identifying new revenue opportunities in the process.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".