Online access to medical records: finding ways to minimise harms
Bibliographic record
Abstract
Currently, GP practices in England should be offering their adult patients online access to a brief summary of their general practice medical record, to be followed as soon as possible by access to the full record. This mandate came into effect in April 2015.1 The vision is that all adults will have online access to all their health and social care records by 2020.1 Potentially, online access is more convenient for patients, empowers and enables patients to take better control of their health and health behaviour, helps patients navigate a complex system, and may make services more efficient, thereby reducing costs.2,3 The policy is also underpinned by ethical arguments about autonomy and individual rights: the health information in the record belongs to the patient who has at least equal rights of access as healthcare providers.2 A recent systematic review found that patients reported benefits of online access in terms of experience, satisfaction, and feeling able to take control of their own health care, with possible advantages to patient safety when patients have online access to medication lists.4,5 However, the same review concluded that we do not know whether online access translates into better health or health care for patients or whether it improves service efficiency.4,5 Like any policy, there is also potential for unintended harm and this is our focus here, particularly those harms related to privacy and confidentiality. There has been no study on this topic as yet.4,5 Online patient access is in the process of being …
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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 | 0.069 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".