Leveraging Medicaid Claims to Map Complex Chronic Conditions for Emergency Preparedness
Bibliographic record
Abstract
CONTEXT: Populations with complex chronic conditions (CCCs), especially those reliant on medical technology, face disproportionate risks during disasters. Medicaid enrollees make up a large portion of these vulnerable populations, yet states often lack rapid identification systems to inform disaster planning. PROGRAM: Florida Medicaid developed a claims-based method to identify enrollees with CCCs, including those dependent on medical technology, to support emergency preparedness and response activities. IMPLEMENTATION: Using the Florida Medicaid Management Information System, the team applied a validated diagnostic and procedural code framework to classify enrollees into 12 CCC categories. Data were analyzed by age, geography, and technology dependence. Health plans received real-time reports before and after hurricanes in 2022 to 2024. EVALUATION: Among 4.4 million enrollees, 7.2% had CCCs, and 18.2% of those were technology dependent. Geographic mapping showed higher concentrations in rural and coastal areas prone to disasters. Health plans reported using the data to contact members and coordinate services during hurricane recovery. DISCUSSION: This effort demonstrates the feasibility and utility of applying claims data to support disaster management. Other states may adapt this approach to improve emergency response and continuity of care for Medicaid's most medically vulnerable populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".