Electronic Medical Record System Use in Canada: Integrating Physiology Flowsheets
Bibliographic record
Abstract
Abstract Background: Longitudinal data of pulmonary physiology (pulmonary function tests, PFTs) is important in diagnosis and management of both respiratory and non-respiratory diseases that have secondary effects on lungs. Large amounts of data need to be amalgamated in physiology flowsheets within electronic medical records (EMR), which summarize trends of multiple PFT reports in one document. We present the process around evaluation and implementation of a physiology flowsheet with discreet data elements. Methods: Alberta Health Services (AHS) has chosen a single vendor for its EMR, an Epic-based system (Epic Systems Corporation). A new clinical tool was written and implemented/piloted within the pulmonary department of the EMR. The physiology flowsheet was tested, modified, and real patient data was entered for those followed longitudinally within AHS Pulmonary Function Laboratories. A pre- and post-implementation survey was carried out with different front-line users to evaluate their experiences. Results: From this pilot implementation, we found that majority of EMR users reported variable ease and satisfaction with the current access to PFT’s. Flowsheets were deemed helpful, once longitudinal data was available. Consistently respondents reported that the EMR slows patient encounters. Healthcare providers also reported flowsheets to be useful for patient education and their self-reflection related to disease processes. Patient surveys were not conducted. Conclusions: Current data transfer of PFT results to EMR requires manual entry, which is time-consuming, though clinically useful. The incorporation of raw data from PFT software to EMR is of great importance in both clinical assessments and patient education; however, a systems-based approach is needed.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".