Benefit-risk of Patients' Online Access to their Medical Records: Consensus Exercise of an International Expert Group
Bibliographic record
Abstract
BACKGROUND: Patients' access to their computerised medical records (CMRs) is a legal right in many countries. However, little is reported about the benefit-risk associated with patients' online access to their CMRs. OBJECTIVE: To conduct a consensus exercise to assess the impact of patients' online access to their CMRs on the quality of care as defined in six domains by the Institute of Medicine (IoM), now the National Academy of Medicine (NAM). METHOD: A five-round Delphi study was conducted. Round One explored experts' (n = 37) viewpoints on providing patients with access to their CMRs. Round Two rated the appropriateness of statements arising from Round One (n = 16). The third round was an online panel discussion of findings (n = 13) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups. Two additional rounds, a survey of the revised consensus statements and an online workshop, were carried out to further refine consensus statements. RESULTS: Thirty-seven responses from Round One were used as a basis to initially develop 15 statements which were categorised using IoM's domains of care quality. The experts agreed that providing patients online access to their CMRs for bookings, results, and prescriptions increased efficiency and improved the quality of medical records. Experts also anticipated that patients would proactively use their online access to share data with different health care providers, including emergencies. However, experts differed on whether access to limited or summary data was more useful to patients than accessing their complete records. They thought online access would change recording practice, but they were unclear about the benefit-risk of high and onerous levels of security. The 5-round process, finally, produced 16 consensus statements. CONCLUSION: Patients' online access to their CMRs should be part of all CMR systems. It improves the process of health care, but further evidence is required about outcomes. Online access improves efficiency of bookings and other services. However, there is scope to improve many of the processes of care it purports to support, particularly the provision of a more effective interface and the protection of the vulnerable.
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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 | 0.253 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".