Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study
Notice bibliographique
Résumé
Background: The COVID-19 pandemic revealed major gaps in public health agencies' (PHAs') data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries. Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs' capabilities to support public health data exchange. Methods: Drawing on multiple sources, we identified all potential local, regional, and state HIOs that were operational in the United States as of March 1, 2022. We defined operational as HIOs that facilitated exchange between at least 2 independent entities. We fielded a survey among our census list of 135 HIOs in January-July 2023. The survey confirmed HIO status as well as captured organizational demographics and current and potential support for PHAs. We report descriptive statistics on HIO demographics and connectivity with PHAs. We also include results on services and data available to support PHAs, funding sources to support public health reporting, and barriers to public health reporting. Of the 135 potential HIOs that received the survey, 90 met our definition of an HIO, and 77 completed the survey, yielding an 86% response rate. Results: We found that 66 (86%) of HIOs in 45 states were electronically connected to at least 1 PHA, yielding 187 HIO-PHA connections across all HIOs. Among HIOs connected to PHAs, the most common type of public health reporting supported by HIOs was immunization registry (n=39, 64%), electronic laboratory result (n=37, 63%), and syndromic surveillance (n=34, 61%). In total, 58% (n=38) of HIOs connected to PHAs provided data to address COVID-19 information gaps, and an additional 30% (n=20) could do so. The most common types of data provided to PHAs were hospitalization information (n=54, 93%), other demographic data (n=53, 91%), health information (eg, chronic health conditions; n=51, 88%), and hospital laboratory results (n=51, 88%). A total of 64% (n=42) of HIOs provided at least 1 type of data analytic service to PHAs to support COVID-19 pandemic response efforts. Top HIO reported barriers to support PHA activities included limited PHA funding (n=21, 32%) and PHAs' competing priorities (n=15, 23%). Conclusions: Our results show that many HIOs are already connected to PHAs and that they are assuming an emerging role to facilitate public health reporting. HIOs are well-positioned to provide value-added support for public health data exchange and address PHAs' information gaps, as ongoing federal efforts to modernize public health data infrastructure and interoperability continue.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».