Quality Indicators as a Tool in Improving the Introduction of New Medicines
Bibliographic record
Abstract
Quality indicators are increasingly used as a tool to achieve safe and quality clinical care, cost-effective therapy, for professional learning, remuneration, accreditation and financial incentives. A substantial number focus on drug therapy but few address the introduction of new medicines even though this is a burning issue. The objective was to describe the issues and challenges in designing and implementing a transparent indicator framework and evaluation protocol for the introduction of new medicines and to provide guidance on how to apply quality indicators in the managed entry of new medicines. Quality indicators need to be developed early to assess whether new medicines are introduced appropriately. A number of key factors need to be addressed when developing, applying and evaluating indicators including dimensions of quality, suggested testing protocols, potential data sources, key implementation factors such as intended and unintended consequences, budget impact and cost-effectiveness, assuring the involvement of the medical professions, patients and the public, and reliable and easy-to-use computerized tools for data collection and management. Transparent approaches include the need for any quality indicators developed to handle conflict of interests to enhance their validity and acceptance. The suggested framework and indicator testing protocol may be useful in assessing the applicability of indicators for new medicines and may be adapted to healthcare settings worldwide. The suggestions build on existing literature to create a field testing methodology that can be used to produce country-specific quality indicators for new medicines as well as a cross international approach to facilitate access to new medicines.
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 | 0.560 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".