Methodological Frameworks and Dimensions to Be Taken Into Consideration in Digital Health Technology Assessment: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) is one of the main tools that health systems have to appraise evidence and determine the value of a given health technology. Although the existing HTA frameworks are useful tools for the evaluation of a wide range of health technologies, more and more experts, organizations across the world, and HTA agencies are highlighting the need to update or develop specific methodological frameworks for the evaluation of digital health technologies in order to take into account additional domains that cover these technologies' intrinsic characteristics. OBJECTIVE: The purpose of our scoping review is to identify the methodological frameworks that are used worldwide for the assessment of digital health technologies; determine what dimensions and aspects are being considered; and generate, through a thematic analysis, a proposal for a methodological framework that is based on the most frequently described dimensions in the literature. METHODS: The scoping review will be performed in accordance with the guidelines established in the updated statement of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We will search for peer-reviewed and grey literature published between 2011 and the date of the search execution. The retrieved references will be reviewed in a single-blind manner by 2 independent authors, and their quality will be assessed by using the Critical Appraisal Skills Program tool. The ATLAS.ti software (Scientific Software Development GmbH) will be used for data extraction and to perform the thematic analysis. RESULTS: The scoping review is currently (May 2022) in progress. It is expected to be completed in October 2022, and the final results of the research will be presented and published by November 2022. CONCLUSIONS: To our knowledge, no studies have been published to date that identify the existing methodological frameworks for digital HTA, determine which dimensions must be evaluated for correct decision-making, and serve as a basis for the development of a methodological framework of reference that health care systems can use to carry out this kind of assessment. This work is intended to address this knowledge gap of key relevance for the field of HTA. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39905.
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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 | 0.224 | 0.214 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.020 |
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".