European Hospitals’ Transition Toward Fully Electronic-Based Systems: Do Information Technology Security and Privacy Practices Follow?
Bibliographic record
Abstract
BACKGROUND: Traditionally, health information has been mainly kept in paper-based records. This has deeply changed throughout approximately the last three decades with the widespread use of multiple health information technologies. The digitization of health care systems contributes to improving health care delivery. However, it also exposes health records to security and privacy breaches inherently related to information technology (IT). Thus, health care organizations willing to leverage IT for improved health care delivery need to put in place IT security and privacy measures consistent with their use of IT resources. OBJECTIVE: In this study, 2 main objectives are pursued: (1) to assess the state of the implementation of IT security and privacy practices in European hospitals and (2) to assess to what extent these hospitals enhance their IT security and privacy practices as they move from paper-based systems toward fully electronic-based systems. METHODS: Drawing on data from the European Commission electronic health survey, we performed a cluster analysis based on IT security and privacy practices implemented in 1723 European hospitals. We also developed an IT security index, a compounded measure of implemented IT security and privacy practices, and compared it with the hospitals' level in their transition from a paper-based system toward a fully electronic-based system. RESULTS: A total of 3 clearly distinct patterns of health IT-related security and privacy practices were unveiled. These patterns, as well as the IT security index, indicate that most of the sampled hospitals (70.2%) failed to implement basic security and privacy measures consistent with their digitization level. CONCLUSIONS: Even though, on average, the most electronically advanced hospitals display a higher IT security index than hospitals where the paper system still dominates, surprisingly, it appears that the enhancement of IT security and privacy practices as the health information digitization advances in European hospitals is neither systematic nor strong enough regarding the IT-security requirements. This study will contribute to raising awareness among hospitals' managers as to the importance of enhancing their IT security and privacy measures so that they can keep up with the security threats inherently related to the digitization of health care organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".