Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to determine whether there is a relationship between cognitive impairment among Medicare patients and hospital readmissions. Although there has been research on cognitive impairment and readmissions, seldom action has been done in regard to economic costs with hospitals. The Affordable Care Act (ACA) established the Hospital Readmission Reduction Program in 2012. Hospitals may not be fully reimbursed for Medicare patient readmissions within 30 days (). STUDY DESIGN: An ethnographic approach was utilized with purposive sampling.This was a nonrandomized purposive sampling intervention study using data from Epic health systems database. METHODS: The intervention spanned over 5 months and the MoCA (Montreal Cognitive Assessment) intervention was conducted in the hospital in a 3-phase study. The purpose of the study was for quality improvement and to detect cognitive impairment among Medicare readmitted patients. RESULTS: The result shows cognitive impairment is prevalent among the Medicare population. Seventy-one (61%) had evidence of cognitive impairment (i.e., obtained a score below 25). The mean MoCA score for the 71 patients identified as having evidence of cognitive impairment was 17.84 (SD, ±5.06; range, 5-24). MoCA is useful in the acute care setting for identifying patients who are at increased risk for readmission. A randomly assigned controlled clinical trial test is warranted to further validate the association between cognitive impairment and readmissions. IMPLICATIONS FOR CASE MANAGEMENT: The ACA aims to improve case management by improving effective outcomes for individuals, care coordination among hospital professionals, economic efficiency, cost-effectiveness, and the collaborative process that services the patient. Hospitals across the country are implementing polices that adhere to patient-centered care. Before the ACA was passed, health care services were value metric. The ACA regulates hospitals toward holistic care or quality metrics. Case management will be critical, as hospitals look toward innovative methods to evaluate their patients.
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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 | 0.000 | 0.000 |
| 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".