Disinvestment Activities and Candidates in the Health Technology Assessment Community: An Online Survey
Bibliographic record
Abstract
OBJECTIVES: As healthcare decision makers continue to face challenges in health services delivery to their patients, disinvestment programs are being established for a sustainable healthcare system. This study aimed to collect data and information by means of a survey of disinvestment candidates and ongoing disinvestment projects in the health technology assessment (HTA) community. METHODS: An online survey was conducted to collect information on disinvestment candidates and activities from members of the Health Technology Assessment International Disinvestment & Early Awareness Interest Group, the EuroScan International Network and International Network of Agencies for Health Technology Assessment. RESULTS: Among the 362 invitees, twenty-four unique responses were received, and almost 70 percent were involved in disinvestment initiatives. The disinvestment candidates identified represented a range of health technologies. Evidence or signaling of clinical ineffectiveness or inappropriate use typically led to the nomination of disinvestment candidates. Health technology assessments and reassessments were usually conducted to evaluate the technology in question, and decisions usually led to the limited use of the technology. Barriers to disinvestment decisions included the strength of interest and advocacy groups, insufficient data for assessments, a systematic decision process and political challenges, while obstacles to their implementation were clinicians' reluctance and insufficient funding and incentives. CONCLUSIONS: The survey results suggested that disinvestment activities are occurring in the HTA community, especially in the public sector. Future research can further investigate the processes and methods used to reach and implement disinvestment decisions from our survey respondents and explore to form closer ties between the HTA and clinical communities.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".