Frequent Utilizers of Emergency Departments: Characteristics and Intervention Opportunities
Bibliographic record
Abstract
Patients who frequently use emergency departments (EDs) have been termed “frequent or high utilizers.” They disproportionally account for almost one-quarter of all ED visits and pose a tremendous socioeconomic and staff labor burden to health care systems. Although there is no agreed upon definition of how many ED visits define frequent use, this patient population shares common sociodemographic, diagnostic, and service use characteristics, in addition to high incidences of mental health and substance abuse problems. Various interventions have been successfully employed to reduce frequent ED visits. Case management (CM) is the most successful intervention. Although primary care access is a necessary element for EDs, successful reduction models must encompass additional interventions, including proper identification of frequent visitors, intensive CM with staff skilled in mental health and substance abuse interventions, partnership with community mental health and substance abuse treatment entities, and referrals to stable housing. This article reviews the common characteristics of ED frequent utilizers and reduction interventions . [ Psychiatr Ann. 2018;48(1):42–50.]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".