Disinvestment in healthcare: an overview of HTA agencies and organizations activities at European level
Bibliographic record
Abstract
BACKGROUND: In an era of a growing economic pressure for all health systems, the interest for "disinvestment" in healthcare increased. In this context, evidence based approaches such as Health Technology Assessment (HTA) are needed both to invest and to disinvest in health technologies. In order to investigate the extent of application of HTA in this field, methodological projects/frameworks, case studies, dissemination initiatives on disinvestment released by HTA agencies and organizations located in Europe were searched. METHODS: In July 2015, the websites of HTA agencies and organizations belonging to the European network for HTA (EUnetHTA) and the International Network of Agencies for HTA (INAHTA) were accessed and searched through the use of the term "disinvestment". Retrieved deliverables were considered eligible if they reported methodological projects/frameworks, case studies and dissemination initiatives focused on disinvestment in healthcare. RESULTS: 62 HTA agencies/organizations were accessed and eight methodological projects/frameworks, one case study and one dissemination initiative were found starting from 2007. With respect to methodological projects/frameworks, two were delivered in Austria, one in Italy, two in Spain and three in U.K. As for the case study and the dissemination initiative, both came from U.K. The majority of deliverables were aimed at making an overview of existing disinvestment approaches and at identifying challenges in their introduction. CONCLUSIONS: Today, in a healthcare context characterized by resource scarcity and increasing service demand, "disinvestment" from low-value services and reinvestment in high-value ones is a key strategy that may be supported by HTA. The lack of evaluation of technologies in use, in particular at the end of their lifecycle, may be due to the scant availability of frameworks and guidelines for identification and assessment of obsolete technologies that was shown by our work. Although several projects were carried out in different countries, most remain constrained to the field of research. Disinvestment is a relatively new concept in HTA that could pose challenges also from a methodological point of view. To tackle these challenges, it is necessary to construct experiences at international level with the aim to develop new methodological approaches to produce and grow evidence on disinvestment policies and practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".