Creating Clinical and Economic “Wins” Through Integrated Case Management
Bibliographic record
Abstract
OBJECTIVE: This article describes an innovative integrated approach to case management using a standardized complexity assessment grid and communication tool, which is designed to identify barriers to improvement in 4 domains: biological, psychological, social, and health system; to create and implement holistic care plans based on "anchored barriers; and to document ongoing targeted outcomes. PRACTICE SETTINGS: Adult and pediatric case and disease managers working for hospitals or clinics, health care delivery systems, general medical health plans, care management vendors, government agencies, and employers can effectively employ integrated case management procedures. INTEGRATED CASE MANAGEMENT: Integrated case management augments traditional care coordination by allowing trained medical or mental health managers to assist with cross-disciplinary barriers without handoffs; to connect multidomain barriers to mutually agreed-upon care plan goals and activities; and to measure clinical, functional, fiscal, quality of life, and satisfaction outcomes as a part of the management process, especially in high-cost, complex patients. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Integrated case management provides a step-by-step interdisciplinary approach for helping complex patients that has the potential to maximize clinical and functional value, while reducing total health-related costs.
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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 | 0.071 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".