Psychiatric electronic health records in the era of data breaches – What are the ramifications for patients, psychiatrists and healthcare systems?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To update psychiatrists and trainees on the realised risks of electronic health record data breaches. METHODS: This is a selective narrative review and commentary regarding electronic health record data breaches. RESULTS: Recent events such as the Medibank and Australian Clinical Labs data breaches demonstrate the realised risks for electronic health records. If stolen identity data is publicly released, patients and doctors may be subject to blackmail, fraud, identity theft and targeted scams. Medical diagnoses of psychiatric illness and substance use disorder may be released in blackmail attempts. CONCLUSIONS: Psychiatrists, trainees and their patients need to understand the inevitability of electronic health record data breaches. This understanding should inform a minimised collection of personal information in the health record to avoid exposure of confidential information and identity theft. Governmental regulation of electronic health record privacy and security is needed.
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.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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 it