Will Privacy Concerns Derail the Electronic Health Record? Balancing the Risks and Benefits
Bibliographic record
Abstract
The introduction of information technologies and the electronic record in health care is thought to be a key means of improving efficiencies and effectiveness of the health care system; ensuring critical information is readily available at the point of care, decreasing unnecessary duplication of tests, increasing patient safety (particularly from adverse drug events), and linking providers and patients spatially and temporally across the continuum of care as health care moves out of the traditional hospital setting to the community and home. There is a steady movement in many countries towards eHealth and a fully implemented, in some cases, pan-regional or pan-national electronic heath record. A number of barriers and challenges exist in EHR implementation. These include lack of resources (both capital and human resources), resistance to change and adoption of new technologies, and lack of standards to ensure interoperability across separate applications and systems. From the public’s perspective, maintaining the security, privacy, and confidentiality of personal health information is a prominent concern and privacy of personal health information still looms as a potential stumbling block for the implementation of a omprehensive, shared electronic record. There are some steps that can be taken to increase the public’s comfort level and to ensure that these new systems are designed and used with security and privacy in mind.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".