Predictors of 2010–2011 Michigan Medicaid Beneficiary Adverse E-Code Health Care Encounters
Bibliographic record
Abstract
To inform Medicaid medication management and public health policymaking, the authors analyzed the major predictive factors influencing program-approved therapeutic use or poisoning E-coded encounters leading to emergency department visits and hospital admission for the totality of Michigan Medicaid beneficiaries during a 12-month 2010-2011 period. The analytic cohort was composed of 26,134 approved E-code encounters submitted for 19,865 discrete Michigan Medicaid beneficiaries.More than 1% of all beneficiaries experienced at least one adverse medication/agent-related E-code encounter during the period. More such encounters and costlier approved encounters were recorded female subjects, African Americans, dually eligible adults, urban elderly, those with fee-for-service Medicaid coverage, and those residing in urban-density counties.Especially notably for patient safety policymakers, more than 9% of total E-coded encounters for children and adults were primarily attributed by providers to likely preventable poisoning causes such as exposure to household cleaning agents/gases, cosmetic products, illicit drug/alcohol, or secondary tobacco smoke. Encounter costs for the total sample totaled $37 million but ranged considerably up to more than a quarter million dollars.In view of the future expanding Medicaid-covered beneficiary cohorts, the authors propose several key patient safety/public health policy implications for researchers and policymakers striving to serve lower-income health care consumer groups.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".