National Development and Regional Differences in eHealth Maturity in Finnish Public Health Care: Survey Study
Notice bibliographique
Résumé
BACKGROUND: eHealth increasingly affects the delivery of health care around the world and the quest for more efficient health systems. In Finland, the development of eHealth maturity has been systematically studied since 2003, through surveys conducted every 3 years. It has also been monitored in several international studies. The indicators used in these studies examined the availability of the electronic patient record, picture archiving and communication system, health information exchange, and other key eHealth functionalities. OBJECTIVE: The first aim is to study the national development in the maturity level of eHealth in primary health care and specialized care between 2011 and 2020 in Finland. The second aim is to clarify the regional differences in the maturity level of eHealth among Finnish hospital districts in 2020. METHODS: Data for this study were collected in 2011, 2014, 2017, and 2020, using web-based questionnaires from the Use of information and communication technology surveys in Finnish health care project. In total, 16 indicators were selected to describe the status of eHealth, and they were based on international eHealth studies and Finnish eHealth surveys in 3 areas: applications, regional integration, and data security and information and communications technology skills. The indicators remain the same in all the study years; therefore, the results are comparable. RESULTS: All the specialized care organizations (21/21, 100%) in 2011, 2014, 2017, and 2020 participated in the study. The response rate among primary health care organizations was 86.3% (139/161) in 2011, 88.2% (135/153) in 2014, 85.8% (121/141) in 2017, and 95.6% (130/136) in 2020. At the national level, the biggest developments in eHealth maturity occurred between 2011 and 2014. The development has since continued, and some indicators have been saturated. Primary health care lags behind specialized care organizations, as measured by all the indicators and throughout the period under review. Regionally, there are differences among different types of organizations. CONCLUSIONS: eHealth maturity has steadily progressed in Finland nationally, and its implementation has also been promoted through various national strategies and legislative changes. Some eHealth indicators have already been saturated and achieved an intensity of use rate of 100%. However, the scope for development remains, especially in primary health care. As Finland has long been a pioneer in the digitalization of health care, the results of this study show that the functionalities of eHealth will be adopted in stages, and deployment will take time; therefore, national eHealth strategies and legislative changes need to be implemented in a timely manner. The comprehensive sample size used in this study allows a regional comparison in the country, compared with previous country-specific international studies.
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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 | 0,016 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».