Health Technology Optimization Analysis: Conceptual Approach and Illustrative Application
Bibliographic record
Abstract
We present a conceptual approach to determine the optimal solution to delivering a health technology, consistent with the objective of maximizing patient outcomes subject to resources available to a publicly funded health system. The article addresses two key policy questions: 1) adding system values through appropriate planning of health services delivery and 2) considering the tradeoff between patient outcomes and costs to the health system through appropriate use of health technologies for conditions with time-dependent treatment outcomes. We develop a health technology optimization framework that considers geographical variation and searches for the best delivery method through a pairwise comparison of all possible strategies, factoring in controlled variables including disease epidemiology, time or distance to hospitals, available medical services, treatment eligibility, treatment efficacy, and costs. Taking variations of these factors into account would help support a more efficient allocation of health resources. Drawing identified strategies together then creates a map of optimal strategies. We apply the proposed method to a policy-relevant health technology assessment of endovascular therapy (EVT) for treating acute ischemic stroke. The best strategy for providing EVT relies on the geographical location of stroke onset and the decision maker's preference for either patient outcomes or economic efficiency. The proposed method produced an optimization map showing the optimal strategy for EVT delivery, which maximizes patient outcomes while minimizing health system costs. In the illustrative case study, there were no tradeoffs between health outcomes and costs, meaning that the delivery strategies that were clinically optimal for patients were also the most cost-effective. In conclusion, the health technology optimization approach is a useful tool for informing implementation decisions and coordinating the delivery of complex health services such as EVT.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".