The business of integrative medicine in a large hospital system
Bibliographic record
Abstract
The number of patients seeking complementary and alternative medicines combined with conventional treatments has grown considerably over the past decade. To meet the growing demand, a dedicated oncology integrative medicine program was initiated in the Beaumont Health System to address the needs of this patient population. Due to its resounding success and patient satisfaction, as evidenced by patient utilization and testimonials and physician referrals, the program was expanded across the healthcare system to every medical specialty. This study outlines how the program was implemented and its business model. A number of methods were used to evaluate the feasibility of starting the program and determine the services required. Financial analyses were developed to understand the costs associated with starting the program without financial assistance. In 2006, an Integrative Medicine program was launched in the Beaumont Cancer Institute (Royal Oak, MI). The initial offering for patients was clinical massage; however, the program rapidly expanded. Currently, services include clinical massage, a clinical massage training program, Reiki, guided imagery, acupuncture, and naturopathic medicine. Patients and physicians expressed satisfaction with the increasing number of complementary services offered at the institution, and the services are heavily utilized. In 2012, the program had more than 18,000 patient visits, of which, 10,191 were for clinical massage, 6,515 for acupuncture, and 1,030 for naturopathic medicine. In this study of developing and implementing an Integrative Medicine program in a large healthcare system, it is shown that a successful program could be initiated with the appropriate planning and support from administration. The program is shown to be financially viable, as the Integrative Medicine (IM) department has become self-sufficient and no longer requires financial support from other hospital departments, and the numerous testimonials indicate that the program has been rewarding for practitioners, staff, and patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".