Impact of a Computer-based Patient Record System on Data Collection, Knowledge Organization, and Reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To assess the effects of a computer-based patient record system on human cognition. Computer-based patient record systems can be considered "cognitive artifacts," which shape the way in which health care workers obtain, organize, and reason with knowledge. DESIGN: Study 1 compared physicians' organization of clinical information in paper-based and computer-based patient records in a diabetes clinic. Study 2 extended the first study to include analysis of doctor-patient-computer interactions, which were recorded on video in their entirety. In Study 3, physicians' interactions with computer-based records were followed through interviews and automatic logging of cases entered in the computer-based patient record. RESULTS: Results indicate that exposure to the computer-based patient record was associated with changes in physicians' information gathering and reasoning strategies. Differences were found in the content and organization of information, with paper records having a narrative structure, while the computer-based records were organized into discrete items of information. The differences in knowledge organization had an effect on data gathering strategies, where the nature of doctor-patient dialogue was influenced by the structure of the computer-based patient record system. CONCLUSION: Technology has a profound influence in shaping cognitive behavior, and the potential effects of cognition on technology design needs to be explored.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it