The Electronic Medical Record: Medical Records That Teach Communication Skills
Bibliographic record
Abstract
In 1985 we began developing a hospital information system (HIS) at the Veterinary Medical Teaching Hospital (VMTH) at University of California, Davis. We wanted to provide easy and timely access to medical, surgical, laboratory, radiographic, demographic, and financial information in a patient-oriented, integrated format. In the course of its development, the HIS expanded to include pharmaceutical and supply information as well as images, clinic schedules, and evaluations. The HIS has become a management tool for hospital services such as pharmacy and central supply and a support tool for administrative functions, including clinical service schedules and performance evaluations of students and residents. The HIS is now a comprehensive computerbased system that provides the information we use to care for patients; teach students and residents and evaluate their skills, knowledge, and professional attributes; perform clinical research; support continuing education and service to the community; and administer the hospital. The core of the HIS is the patient-oriented electronic medical record (EMR). Besides serving the traditional function of the patient record as a repository for all-important patient information, the EMR is now a primary tool for teaching critical thinking and communication skills to students and residents. Evoking the vision of Lawrence Weed, it has become a medical record that teaches.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".