Digital Capability, Open-Source Use, and Interoperability Standards Within the National Health Service in England: Survey of Health Care Trusts
Notice bibliographique
Résumé
Background: In 2016, the National Health Service (NHS) England sought to drive digital transformation within select NHS trusts through the Global Digital Exemplar (GDE) program. While the program did advance the NHS's integration with digital technologies, disparities in digital maturity persisted between GDE-funded and nonfunded NHS trusts. The Department of Health and Social Care (DHSC) launched a data strategy in 2022 that aimed to develop the appropriate technical infrastructure and data architecture to enable more effective and efficient use of its data. Given the diversity in digital capabilities, open-source adoption, and interoperability standards within NHS services, official guidance has continued to struggle to provide effective unification. Data about capabilities and technologies from application development teams in the NHS trusts, crucial for advancing these areas, remains insufficient. Objective: This study aimed to further document the capabilities and technologies used in the NHS to develop digital capacity, comparing those with standard funding against those with additional GDE funding. This comparative analysis provides a foundational understanding for evaluating current practices and identifying potential areas for improvement in the NHS digital transformation efforts. Methods: This study was conducted using Freedom of Information (FOI) requests and systematic website searches. The Freedom of Information Act (FOIA) allows individuals to request information held by public authorities. This process supports transparency and accountability by ensuring public access to government data. Data were compiled from two sources: (1) FOI requests submitted to NHS trusts between July 2020 and December 2020, and (2) systematic website searches for technology conducted between August 2020 and July 2021. A series of chi-square tests was conducted to validate and strengthen the robustness of the FOI questions. Results: A total of 191 (84.5%) of the then 226 NHS trusts completed the FOI request, and 161 of the 191 (84%) had software and app development, website, or innovation teams. A total of 112 (69.6%) teams developed front-facing service user websites and apps. Out of 191, 150 (93.2%) worked with clinical staff to formulate innovative ideas, 55 (34.2%) carried out developments for other trusts and external entities, 35 (21.7%) had attempted to secure an innovation grant, and 138 (86%) disclosed the technologies they use. A total of 25 (15.5%) said they always used open-source technology, and 24 (17%) disclosed technologies associated with interoperability standards in their responses. Conclusions: The NHS must adopt a cohesive strategy and refine policies to ensure the success of its digital, open-source technology and interoperability standards initiatives. Five recommendations toward greater organizational interoperability are made by the authors. Future research should examine digital innovation across NHS trusts, focusing on barriers such as limited resources, organizational culture, and technical expertise. Identifying these challenges is essential for developing strategies to reduce disparities and promote equal progress.
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».