Rethinking the Design Space of EHRs towards Modeling Tools: A Pathway for Healthcare to join the “Design Disciplines” (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Given the increasing complexity of patient cases due to an aging population and multimorbidity, it has become essential to capture encounters between patients and physicians with greater nuance. The current method of using Electronic Health Records (EHRs) is limited in its ability to do so. This is because EHRs mostly arrange discrete concepts using static spatial allocation of widgets to denote data types which are navigated by physicians in order to cognitively formulate their relationships between elements on the go. This results in cognitive overload experienced by physicians as well as data fragmentation. Moreover, it lacks the ability to express the discrete elements with the non-discrete. For example, expressing why (non-discrete) a lab (discrete) was ordered for a patient. This paper is proposed by Aurora, a group of people working on resolving these challenges with the proposed framework. The Aurora framework introduces the ideal properties for modeling and design in healthcare such as expressiveness and information synthesis. Moreover, it builds a solution using domain specific languages to achieve these properties.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".