How digitally advanced is your digital public health system? A narrative review of indicators published as grey literature (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,035 | 0,039 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».