How digitally advanced is your digital public health system? A narrative review of indicators published as grey literature (Preprint)
Bibliographic record
Abstract
BACKGROUND Revealing the full potential of digital public health (DiPH) systems requires a wide-ranging tool to assess their maturity and readiness for emerging technologies. Although a variety of indices exist to address digital health systems, questions arise regarding the integration of indicators on information-communication-technology maturity and readiness, digital (health) literacy, and interest in DiPH tools by the society and workforce, as well as the legal maturity and readiness of digitalized health systems. Existing tools frequently target one of these domains while overlooking the others. Additionally, no review has been conducted to holistically investigate available national DiPH system maturity and readiness indicators using a multidisciplinary lens. OBJECTIVE Applying a narrative review, we aimed to map the landscape of DiPH system maturity and readiness indicators published in the grey literature. METHODS As original indicators were not published in scientific databases, we applied pre-defined search strings to DuckDuckGo.com and Google.com for 11 countries from all continents classified as having reached level 4 of 5 in the latest Global Digital Health Monitor evaluation. Additionally, 19 international organizations (such as the World Health Organization, World Bank, or International Telecommunication Union) were searched for maturity and readiness indicators concerning DiPH. RESULTS Of the 1484 identified references, 137 were included and named 15806 indicators (2129 after assessment for eligibility and duplication screening). We deemed 286 indicators from 90 references relevant for DiPH system maturity and readiness assessments. Most of these (133) had a legal background, and the fewest (37) were related to social domains. Although most indicators focused on clinical and healthcare-related topics, we identified indicators for various DiPH settings and issues, including data protection, literacy, infrastructure, empowering vulnerable groups, health promotion, public health surveillance, and workforce preparedness. CONCLUSIONS Our work is the first to comprehensively analyze the gray literature on maturity and readiness assessments from multidisciplinary perspectives. By this, we contributed towards a more holistic understanding of DiPH and justify why such a perspective is essential when conducting evaluations of digital healthcare systems to effectively leverage digital technologies to optimize public health goals and functions. Although new methods for systematically researching grey literature are needed, our study holds the potential to develop more comprehensive tools for DiPH system maturity and readiness assessments. Further examination is required to analyze the suitability and applicability of all identified indicators for diverse healthcare settings. By working towards a uniform evaluation of DiPH system maturity and readiness, we foster informed decision-making among healthcare planners and practitioners to improve resource distribution and continue to drive innovation in healthcare delivery.
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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.035 | 0.039 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".