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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".