Assessing the Digital Advancement of Public Health Systems Using Indicators Published in Gray Literature: Narrative Review
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 assess digital health systems, questions arise about the inclusion of indicators of information and communications technology maturity and readiness, digital (health) literacy, and interest in DiPH tools by the society and workforce, as well as the maturity of the legal framework and the readiness of digitalized health systems. Existing tools frequently target one of these domains while overlooking the others. In addition, no review has yet holistically investigated the available national DiPH system maturity and readiness indicators using a multidisciplinary lens. OBJECTIVE: We used a narrative review to map the landscape of DiPH system maturity and readiness indicators published in the gray literature. METHODS: As original indicators were not published in scientific databases, we applied predefined search strings to the DuckDuckGo and Google search engines for 11 countries from all continents that had reached level 4 of 5 in the latest Global Digital Health Monitor evaluation. In addition, we searched the literature published by 19 international organizations for maturity and readiness indicators concerning DiPH. RESULTS: Of the 1484 identified references, 137 were included, and they yielded 15,806 indicators. We deemed 286 indicators from 90 references relevant for DiPH system maturity and readiness assessments. The majority of these indicators (133/286, 46.5%) had legal relevance (targeting big data and artificial intelligence regulation, cybersecurity, national DiPH strategies, or health data governance), and the smallest number of indicators (37/286, 12.9%) were related to social domains (focusing on internet use and access, digital literacy and digital health literacy, or the use of DiPH tools, smartphones, and computers). Another 14.3% (41/286) of indicators analyzed the information and communications technology infrastructure (such as workforce, electricity, internet, and smartphone availability or interoperability standards). The remaining 26.2% (75/286) of indicators described the degree to which DiPH was applied (including health data architecture, storage, and access; the implementation of DiPH interventions; or the existence of interventions promoting health literacy and digital inclusion). CONCLUSIONS: Our work is the first to conduct a multidisciplinary analysis of the gray literature on DiPH maturity and readiness assessments. Although new methods for systematically researching gray literature are needed, our study holds the potential to develop more comprehensive tools for DiPH system assessments. We contributed toward a more holistic understanding of DiPH. Further examination is required to analyze the suitability and applicability of all identified indicators in diverse health care settings. By developing a standardized method to assess DiPH system maturity and readiness, we aim to foster informed decision-making among health care planners and practitioners to improve resource distribution and continue to drive innovation in health care delivery.
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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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.038 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".