Innovation Pipeline: A framework for value-based decision making
Bibliographic record
Abstract
The concept of value-based healthcare and focus on health outcomes is not new. While these ideas have been shared for decades, health systems still struggle to implement value-based decision making. This article describes the Innovation Pipeline, a framework that embeds value-based decision making in a healthcare organization. The Innovation Pipeline outlines the measurable evidence requirements needed to demonstrate organizational definitions of value. This evidence of value allows health leaders to make decisions supported by rigorous data, evidence, and evaluation, ensuring initiatives that bring organizational value progress from good ideas that require testing to evidence-based services embedded and sustained in operational workflows. The Innovation Pipeline is rigorous and customizable to all levels of the health system and designed to streamline evidence-generation activities, focusing on collecting evidence needed to demonstrate value and inform funding and resource allocation decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.138 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.032 | 0.030 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".