Establishing an Integrative Medicine Program Within an Academic Health Center: Essential Considerations
Bibliographic record
Abstract
Integrative medicine (IM) refers to the combination of conventional and "complementary" medical services (e.g., chiropractic, acupuncture, massage, mindfulness training). More than half of all medical schools in the United States and Canada have programs in IM, and more than 30 academic health centers currently deliver multidisciplinary IM care. What remains unclear, however, is the ideal delivery model (or models) whereby individuals can responsibly access IM care safely, effectively, and reproducibly in a coordinated and cost-effective way.Current models of IM across existing clinical centers vary tremendously in their organizational settings, principal clinical focus, and services provided; practitioner team composition and training; incorporation of research activities and educational programs; and administrative organization (e.g., reporting structure, use of medical records, scope of clinical practice) and financial strategies (i.e., specific business plans and models for sustainability).In this article, the authors address these important strategic issues by sharing lessons learned from the design and implementation of an IM facility within an academic teaching hospital, the Brigham and Women's Hospital at Harvard Medical School; and review alternative options based on information about IM centers across the United States.The authors conclude that there is currently no consensus as to how integrative care models should be optimally organized, implemented, replicated, assessed, and funded. The time may be right for prospective research in "best practices" across emerging models of IM care nationally in an effort to standardize, refine, and replicate them in preparation for rigorous cost-effectiveness evaluations.
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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 | 0.053 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".