A Direct Experience in a New Accountable Care Organization: Results, Challenges, and the Role of the Neurosurgeon
Bibliographic record
Abstract
The passage of the Affordable Care Act saw the creation of Accountable Care Organizations (ACOs), a new approach to healthcare delivery moving from fee-for-service toward population health. This paper presents a case study of the Memorial Hermann ACO (MHACO), launched in response to the Medicare Shared Savings Program, with goals to align physician and hospital incentives, practice evidence-based medicine, develop care coordination, and increase efficiency. Building blocks included an affiliated primary care network, a clinical integration program (involving shared electronic medical record platforms and quality data reporting), and significant investments in information technology. Presented is the approach taken to form MHACO; the management structure, technology developed, and a 2-year experience. Incorporated in July 2012, the MHACO involved 22 000 Medicare patients. In 2015, Centers for Medicare and Medicaid Services released data showing a composite quality score between 80 and 85 (from a maximum 100) and nearly $53 million in total savings (or 11% of expected expenditure), making MHACO one of the most successful nationally.1 In fewer than 5 years, almost 500 ACOs have developed, and by some estimates, a quarter of Medicare patients are currently enrolled in an ACO. Although ACOs to date have focused on primary care, the future will increasingly involve specialists. At Memorial Hermann, neurosurgeons took an early role in forming collaborative partnerships with the hospital, and started programs that served as precursors to the ACO model. This paper ends with an overview of ACO development, likely changes going forward, and a discussion of the role of specialists in general, and of neurosurgeons in particular.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".