Assessing staff awareness and effectiveness of educational training on IT security and privacy in a large healthcare organization
Bibliographic record
Abstract
Background: The increased use of health information systems and information technology (IT) in healthcare heightens the risk of security and privacy breaches. Necessary measures such as effective IT training and education are required to meet the challenges of protecting patient information. Purpose: The objective of the study was to determine the effectiveness of existing educational and awareness modules in delivering the key messages around IT security and privacy. Methods: The study was conducted in a large healthcare organization in Western Canada from September 2016 to March 2017. Using proportionate stratified random sampling, an online survey was distributed to all professional groups including clinical and non-clinical staff. In total, 586 participants responded to questions pertaining to whether or not they were aware of the IT education material, common potential breaches, and knowledge in preventing IT security and privacy breaches. Data were analyzed in SPSS version 19. Results: The study found that most of the participants (80.9%) completed the online IT training. Staff perceived the online training as effective (57.5%). There was a significant positive correlation between staff perception about the effectiveness of IT security educational material and satisfaction with IT security in the organization ( r =0.34, P <0.01). Those who completed the training were 4.2-times (CI=2.0–8.8) more likely to correctly report the action upon receiving spam emails than those who had not completed the training. The most common type of breach stated was not knowing how to encrypt emails when sending emails outside the organization. Only a small proportion of clinical (25.5%) and non-clinical staff (30.4%) reported knowing how to encrypt emails. Also, participants identified various strategies for improving the module content and compliance. Conclusion: Online training provides a basic understanding of IT security and privacy concepts to prevent potential breaches. The training should be an integral part of healthcare staff continuing education to protect patient information. Keywords: IT security modules, privacy in healthcare, healthcare staff and IT breaches, information storage & retrieval, IT security & privacy, computerized medical records system
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".