Health technology assessment for digital health technologies
Bibliographic record
Abstract
Health technology assessment (HTA) frameworks used for making public funding decisions on digital health technologies (DHTs) have not been informed by large stakeholder preference studies and rarely cover all nine domains of the widely used EUnetHTA “Core Model”. Our aim was to develop a literature-informed and stakeholder-prioritised checklist of DHT-specific considerations for DHTs that manage chronic disease that extends an internationally established HTA framework. We conducted two systematic reviews to identify: (i) DHT evaluation frameworks and (ii) primary research on DHTs published until 20 March 2020. Stakeholder prioritisation of issues was performed using a best-worst preference study among a broad cross-section of patients, carers, health professionals, and the general population in Australia, Canada, New Zealand, and the UK. Systematic review issues were prioritised and adapted for use as a practical checklist. DHT evaluation content was recommended by 44 identified frameworks for 28 of the 145 issues in the Core Model and for 22 new DHT-specific issues. A coverage assessment of 112 clinical studies of remote treatment and self-management DHTs for patients with cardiovascular disease or diabetes revealed that less than half covered DHT-specific content in all but one domain, or traditional HTA content in clinical effectiveness and ethical analysis. The preference survey of 1,251 stakeholders identified broad agreement on the 12 most important DHT attributes, six of which were related to safety. The most important attribute was “helps health professionals respond quickly when changes in patient care are needed”, which is not a focus of existing DHT HTA frameworks. Using the thesis-developed checklist in conjunction with the Core Model can enable users to perform a DHT-specific and comprehensive HTA on DHTs that manage chronic disease and can assist primary researchers to collect appropriate data to inform this HTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.115 | 0.351 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".