The EDIT Survey: Identifying Emergency Department Information Technology Knowledge and Training Gaps
Bibliographic record
Abstract
OBJECTIVE: To review Emergency Department internet connectivity, cyber risk factors, perception of risks and preparedness, security policies, training and mitigation strategies. METHODS: A validated targeted survey was sent to Canadian ED physicians and nurses between March 5, 2019 and April 28, 2019. RESULTS: There were 349 responses, with physicians making up 84% of the respondents (59% urban teaching, 35% community teaching, 6% community non-teaching hospitals). All had multiple passwords, 93% had more than 1 user account, over 90% had to log repeatedly each workday, 52% had to change their passwords every 3 months, 75% had multiple methods of authentication and 53% reported using a terminal where someone else was already logged in. Passwords were used to review laboratory and radiology data, access medical records and manage patient flow. Majority of the respondents (51%) did not know if they worked with internet linked devices. Only 7% identified an 'air gapped' computer in their facility and 76% used personal devices for patient care, with less than a third of those allowing the IT department to review their device. A total of 26 respondents received no cyber security training. CONCLUSION: This paper revealed significant computer-human interface dysfunctionality and readiness gaps in the event of an IT failure. These stemmed from poor system design, poor planning and lack of training. The paper identified areas with technical or training solutions and suggested mitigation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".