Using healthcare encounter data to identify high-cost users among adults with a history of homelessness: a validation study
Bibliographic record
Abstract
People experiencing homelessness are often considered frequent healthcare users. Although their service use is not uniform, it can be difficult to identify the highest-cost users without access to comprehensive cost data. This study validated a set of algorithms that apply healthcare encounter data to identify high-cost users among adults with a history of homelessness. Administrative healthcare cost data were compared across common frequent user definitions for emergency department (ED) visits and hospitalizations. Sensitivity, specificity, positive predictive values, and negative predictive values were derived for a set of seven algorithms. Twenty-three percent of the cohort was high-cost users. Optimal algorithms to identify high-cost users were ≥1 hospitalization with 78% sensitivity and 96% specificity and ≥1 hospitalization or ≥6 ED visits with 82% sensitivity and 89% specificity. The positive predictive values indicate that 85% of people with ≥1 hospitalization in a year and 69% of people with ≥1 hospitalization or ≥6 ED visits in a year were correctly classified as high-cost users. This study offers a straightforward method to identify high-cost users among adults with a history of homelessness. The optimal algorithms can be used to inform resource planning and service evaluation to ensure high-needs groups receive appropriate and tailored interventions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".