PD156 Scanning The Right Horizons: Does Singapore’s Horizon Scanning Identify And Assess The Relevant Technologies?
Bibliographic record
Abstract
Introduction In Singapore, a horizon scanning (HS) system was established in 2020 by the Agency for Care Effectiveness (ACE) to provide early alerts of new and emerging medical technologies (medtechs) for service planning and to warn against the diffusion of low value technologies. This study compared the medtechs identified and assessed by ACE with health technology trends identified by our reference agency. Methods Medtechs identified and assessed by ACE between 2020 and 2023 were analyzed to examine their distribution. Most of the identified medtechs were classified as digital health technologies, precision medicine, robotics, or implants. The prioritized technologies with a completed in-depth assessment were compared with the top 10 health technology trends identified in 2022 by our reference health technology assessment (HTA) agency, the Canadian Agency for Drugs and Technologies in Health (CADTH). Additionally, feedback from key stakeholders such as clinicians and policymakers on the HS reports was summarized to understand the relevance and value of the HS reports. Results From 2020 to 2023, there were 1,703 medtechs identified from various databases and manufacturer submissions. Digital health technologies were the largest proportion of technologies identified during this period, increasing from 26 percent in 2020 to 31 percent in 2023. Of the 20 evaluated technologies, 70 percent belonged to the top trending medtech fields identified by CADTH. These included artificial intelligence for diagnostics (n=5), point-of-care testing (n=4), companion diagnostics (n=2), wearables (n=2), and remote monitoring (n=1). Initial stakeholder feedback was positive, citing HS reports as being relevant for clinical practice. Some HS reports were also referenced by other policymakers to support regulatory decisions. Conclusions In summary, similarities were observed between medtechs identified and assessed by the ACE HS system and the top trending medtech fields identified by CADTH. Additionally, digital health technologies were the largest proportion of technologies identified by the ACE HS system in 2023. This was substantiated by feedback from our key stakeholders, indicating the relevance and value of the ACE HS work.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".