Institutional Voids in the Governance of Digital Health: An Umbrella Review
Bibliographic record
Abstract
Digital technologies rapidly evolve in public health, potentially transforming healthcare systems. However, there are yet significant challenges necessitating a governance framework to effectively guide policy, practice, and research in this emerging field. This article examines the emergence of the digital health field through the lens of institutional theory and identifies institutional voids in digital health governance based on the research conducted to date. Institutional voids are structural and behavioral gaps in the relationships among various actors that a single entity cannot solely address. Establishing a robust institutional field relies on recognizing and addressing these voids. We conducted an “umbrella review”— an overview of reviews—to qualitatively synthesize multiple review papers on the topic and comprehensively summarize the available evidence. We searched peer-reviewed sources indexed on Google Scholar, Scopus, PubMed, and MEDLINE using keywords relevant to the research question, covering the period from 2014 to 2024. We finally selected 45 review articles and inductively analyzed them to identify institutional voids in digital health governance. 26 primary concepts were extracted from the articles and categorized into nine themes. The proposed framework for institutional voids in digital health governance consists of nine dimensions: collective action, legitimacy and trust, logic and discourse, research and development, regulation, bridging and brokering, technical infrastructure, data governance, and funding and insurance. This research serves as a guide for policymakers and other stakeholders in digital health for the sustainable development of the field. Additionally, we identified critical research gaps that have yet to be explored through empirical studies.
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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".