Variations in Resource Intensity and Cost Among High Users of the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: High users of emergency department (ED) services are often identified by number of visits per year, with little exploration of the distribution/pattern of visits over time. The purpose of this study was to examine patient- and encounter-level factors and costs related to periods of short-term resource intensity among high users of the ED within a tertiary care teaching facility. METHODS: We identified all adults with at least three visits to the Ottawa Hospital ED within a 1-year period from April 1, 2012, to March 31, 2013. Within this high-user cohort, we then measured intensity of use by calculating average daily visit rates to identify individuals with a cluster of ED visits. Those with at least three ED visits/7 days at any point during follow-up were considered patients with clustered ED use (i.e., a period of short-term resource intensity). Detailed clinical and administrative data were used to compare patient- and encounter-level characteristics and cost profiles between the clustered and nonclustered groups. Analyses were repeated using varying cut points to define high users (at least five and at least eight visits per year). RESULTS: Of the 16,153 patients identified as high ED users during the study period, 13.5% had their visits clustered within a short period of time. These clustered users were more likely to be homeless, to require psychiatric services, and to leave without being seen by a physician and less likely to be admitted to the hospital. Approximately one in three (31.2%) high ED users with clustered visits returned for the same medical problem (namely pain-related disorders, shortness of breath, and cellulitis) within a 1-week period. Similar trends were observed when the high-user cohort was restricted to those with at least five and at least eight ED visits/year. Finally, patients with short-term intensity periods had lower direct and indirect costs per encounter than those without. CONCLUSIONS: Using a novel methodology that accounts for both number and intensity of ED encounters over time, we were able to identify specific subpopulations of high ED users. Further work is required to determine if this methodology has utility for targeting care pathways within this heterogeneous and high-risk patient group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".