Development of a value assessment framework for health technology assessment and coverage decision making in China
Bibliographic record
Abstract
Value assessment framework (VAF) has become a promising tool for assessing the value of health technologies and informing coverage decision making. Most VAFs have been developed for high-income countries and are insufficient for various contexts given. There were limited patient and public engagement in the framework development process and the uncertainty in coverage decision making was not accommodated. This doctoral thesis aimed to develop a VAF that involved multiple stakeholders to support transparent and consistent coverage decision making in China. This thesis begins with an overview of coverage decision making and health technology assessment (HTA), and the emergence and application of VAF in this field in recent years. This thesis subsequently presents a systematic review of existing VAFs that investigated how value is defined and measured in healthcare and summarized the methods of framework development in existing VAFs. Then, this thesis presents a qualitative description study informed by the systematic review and the principles of qualitative description (QD). Through open-ended semi-structured interviews with 34 Chinese stakeholders, as well as a review and analysis of 16 publicly available government documents related to HTA and coverage policies in China, 12 value attributes were identified for the development of a VAF in China. Then, this thesis includes an online factorial survey among 365 Chinese stakeholders to generate value scoring algorithms. With the developed VAF, the value of a health technology under assessment and its probabilities of entering negotiation or being covered by the national medical insurance in China for diseases with different levels of severity, can be estimated. This thesis ends with a discussion of the key findings, limitations, and implications of this program of research and presents our perspectives on challenges and future directions in the field of VAF.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".