Problems with the electronic health record
Bibliographic record
Abstract
One of the most significant changes in modern healthcare delivery has been the evolution of the paper record to the electronic health record (EHR). In this paper we argue that the primary change has been a shift in the focus of documentation from monitoring individual patient progress to recording data pertinent to Institutional Priorities (IPs). The specific IPs to which we refer include: finance/reimbursement; risk management/legal considerations; quality improvement/safety initiatives; meeting regulatory and accreditation standards; and patient care delivery/evidence based practice. Following a brief history of the transition from the paper record to the EHR, the authors discuss unintended or contested consequences resulting from this change. These changes primarily reflect changes in the organization and amount of clinician work and clinician-patient relationships. The paper is not a research report but was informed by an institutional ethnography the aim of which was to understand how the EHR impacted clinicians and administrators in a large, urban hospital in the United States. The paper was also informed by other sources, including the philosophies of Jacques Ellul, Don Idhe, and Langdon Winner.
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 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 | 0.184 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".