‘Trying to find information is like hating yourself every day’: The collision of electronic information systems in transition with patients in transition
Bibliographic record
Abstract
The consequences of parallel paper and electronic medical records (EMR) and their impact on informational continuity are examined. An interdisciplinary team conducted a multi-site, ethnographic field study and retrospective documentation review from January 2010 to December 2010. Three case studies from the sample of older patients with hip fractures who were transitioning across care settings were selected for examination. Analysis of data from interviews with care providers in each setting, field observation notes, and reviews of medical records yielded two themes. First, the lack of interoperability between electronic information systems has complicated, not eased providers' ability to communicate with others. Second, rather than transforming the system, digital records have sustained health care's 'culture of documentation'. While some information is more accessible and communications streamlined, parallel paper and electronic systems have added to front line providers' burden, not lessened it. Implementation of truly interoperable electronic health information systems need to be expedited to improve care continuity for patients with complex health-care needs, such as older patients with hip fractures.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".