OP30 Health Technology Assessment And The Decision-Making Process Of New Drug Listing In Hong Kong
Bibliographic record
Abstract
INTRODUCTION: In Hong Kong, the Drug Advisory Committee (DAC) has had the role of evaluating and advising new drugs to be included in the listing of the Hospital Authority Drug Formulary since July 2005. The drug review process was subject to challenge due to a lack of transparency to members of the public and documentation of the scientific basis for decision making. The purpose of this review was to describe the process, evaluation criteria and possible outcomes of decision making for new drugs listed in the Hong Kong Hospital Authority Drug Formulary in comparison to Health Technology Assessment (HTA) policies in overseas countries. METHODS: Details of the decision-making processes including new drug listing submissions, the DAC meeting, procedures before and after the meeting, were extracted from the official Hong Kong Hospital Authority drug formulary management website and manual. Publicly available information related to new drug decision making processes for four HTA agencies (National Institute for Health and Clinical Excellence (NICE), Scottish Medicines Consortium (SMC), Australian Pharmaceutical Benefits Advisory Committee (PBAC), and Canadian Agency for Drugs and Technologies in Health (CADTH)) were reviewed and retrieved from official documents on their public domains. RESULTS: The DAC is in charge of the systematical and critical appraisal of new drugs for listing on the formulary, reviewing submitted applications, and making decisions of drug listing based on scientific evidence in which safety, efficacy and cost-effectiveness are primary considerations. When compared to other HTA agencies, transparency of decision-making processes of the DAC, relevance of clinical and health economic evidence, and lack of health economic and methodological input to submissions were major challenges of the new drug listing policy in Hong Kong. CONCLUSIONS: Despite the challenges identifed, this review provided suggestions for establishing a more transparent, credible, evidence-based decision-making process for the Hong Kong Hospital Authority Drug Formulary. Proposals for improvement in the listing of new drugs in the formulary should be a priority in healthcare reform.
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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 | 0.053 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".