Bibliographic record
Abstract
Electronic health records (EHRs) enable patients to access their health records anytime, from anywhere with internet connectivity.Yet not all Americans benefit from these innovations.EHRs can be hard to access for people with a range of disabilities.This lack of access perpetuates inequity and, thus, demands ethical and legal attention.Some federal laws and regulations require accessible EHRs, but even these protections can fall short.This article argues that more clearly defined obligations for EHR developers and clinicians are necessary.Accessing Health Information Electronic health records (EHRs) have made obtaining health data easier than ever before.EHRs are effectively "digitized medical chart[s]" 1 that allow clinicians to readily access and manage patients' information.Integrating EHRs into clinical practice can increase efficiency and improve quality of care.1,2 Specifically, EHRs allow clinicians to coordinate treatment plans with other clinicians and to detect and mitigate errors.Patients, too, can review parts of their health records 24 hours a day, 365 days a year, from anywhere with an internet connection.Patients' reading and understanding of key information in their EHRs can motivate communication and adherence.However, not all patients can reap these benefits.Americans with disabilities experience significant health inequity, and inaccessible EHRs could exacerbate that inequity.Moreover, issues that impede access for patients with disabilities could also affect other populations, such as elderly patients and patients with limited education.2 Inaccessible EHRs are at odds with clinicians' legal 3 and ethical duties 4,5,6,7 to practice inclusively.Thankfully, current federal regulations require covered providers to ensure that information technology is accessible to those with disabilities.8 Although federal disability rights laws do not apply to technology developers 3,9 and can go underenforced, 10 ethical duties of both clinicians and EHR developers provide a foundation on which to ground health systems' parallel duties to ensure that patients with disabilities can meaningfully access and use their health data.Inaccessibility of EHRs Many websites and apps are inaccessible to people with disabilities.They might use small font, include content written at a high literacy level, rely on complex and hard-tonavigate user interfaces, lack the capacity to customize, or be incompatible with
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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 | 0.127 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".