National Development and Regional Differences in eHealth Maturity in Finnish Public Health Care: Survey Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".