Supportive Care for Superutilizers of a Managed Care Organization
Bibliographic record
Abstract
Background: Ohana Health Plan, Inc., (OHP) is one of the first managed care organizations offering supportive care services targeted to superutilizers. Bristol Hospice Hawaii, LLC, partnered with OHP to provide interdisciplinary supportive care services to home-bound OHP members. Objectives: The purpose of this study was to measure symptom relief, satisfaction, resource utilization, and cost savings associated with supportive care. Design: Prospective study. Setting: Over 12 months, 27 superutilizer members residing in the community were referred by OHP, 21 members were enrolled into supportive care. Measurements: Data were collected upon admission and repeatedly thereafter using the Edmonton Symptom Assessment Scale (ESAS) and the Missoula–Vitas Quality of Life Index (MVQOLI). The Family Satisfaction with Advanced Cancer Care (FAMCARE) Scale was administered at discharge. Emergency department (ED) visits and hospital utilization were tracked. Results: Median age was 63 years; more than half had cardiac diagnoses. Majority of members were Hawaiian and other Pacific Islander. Median length of stay in supportive care was 90 days. Five (23%) members enrolled in hospice following supportive care. Symptom improvement occurred in pain ( p < 0.0001), anxiety ( p = 0.0052), and shortness of breath ( p = 0.0447). This model has shown a 79.5% reduction of ED visits per thousand members and a 75% reduction of hospitalizations per thousand. Overall net savings was 36%. Discussions and documentation of end-of-life wishes increased from 23% to 85%. Conclusion: Supportive care is highly effective in reducing costs associated with superutilizers. Our experience demonstrates the effectiveness of supportive care approaches in this population through improved care and lower health care costs overall.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".