Medicare-Medicaid Eligible Beneficiaries and Potentially Avoidable Hospitalizations
Bibliographic record
Abstract
OBJECTIVE: Potentially avoidable hospitalizations have been identified by experts as leading to poor health outcomes and costly care. Potentially avoidable hospitalizations are particularly common among full-benefit dual eligible beneficiaries. This paper examines potentially avoidable hospitalizations rates by setting, state, and medical condition, and the average cost of these events. METHODS: This analysis identifies potentially avoidable hospitalizations using diagnosis codes identified by an expert panel. Settings of care are determined using a timeline file, which assigns an individual to a specific setting on a particular day. POPULATION/DATA SOURCE: The analysis uses several different datasets from the Chronic Conditions Data Warehouse. The study population includes fee-for-service beneficiaries who were eligible for both Medicare and full Medicaid benefits for at least one month during the calendar year. The study years are 2007 to 2009. RESULTS: In 2009, among our study population, 26 percent of hospitalizations were potentially avoidable; and the rate was 133 per 1,000 person-years. Potentially avoidable hospitalizations were much more likely for those beneficiaries who were in institutions--16 percent of beneficiaries in our study population were in an institution, yet comprised 45 percent of all potentially avoidable hospitalizations. The range in rates across the states was considerable, with more than a threefold difference across states. Five conditions were responsible for nearly 80 percent of potentially avoidable hospitalizations. From 2007 to 2009, the national and state rates were fairly consistent. DISCUSSION: This analysis indicates that the potentially avoidable hospitalization rate among MME beneficiaries was consistently high from 2007 to 2009. This bears monitoring in the future to see if the Centers for Medicare & Medicaid Services' various initiatives have led to a reduction in rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".