Integrating Exercise into the Electronic Medical Record: A Case Series in Oncology
Bibliographic record
Abstract
ABSTRACT The Exercise is Medicine campaign initiated by the American College of Sports Medicine is intended to advance the integration of exercise into formalized medical care through strategic linkages between health care systems, providers, health-related digital technologies, and available exercise programs. Exercise is established as a fundamental element of comprehensive cancer care and given the critical role of the electronic medical record (EMR) in health care communication, optimizing the use of the EMR by qualified exercise professionals and for exercise-related referrals may improve clinical outcomes. The purpose of this article is to describe the strategies, facilitators, barriers, and opportunities in implementing exercise information in the EMR in three cancer centers in North America: The University of Alabama at Birmingham, the Sylvester Comprehensive Cancer Center, and the Princess Margaret Cancer Centre. The collective experience of three cancer centers identifies the diverse opportunities and challenges in connecting exercise programming with the EMR. The implementation of exercise programming, resources, and linkages in the EMR is complex, involves numerous stakeholders, and can be mapped against the Consolidated Framework of Implementation Research. Methods of establishing communication or referral pathways to exercise programs described here can serve as precedents for similar endeavors. Further research is needed to determine whether implementation strategies that target identified implementation science constructs can facilitate the implementation of exercise programming via EMR where the Consolidated Framework for Implementation Research may serve as a useful empirical framework.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".