Development of a real-time general medicine 30-day readmissions notification system
Bibliographic record
Abstract
Hospital readmissions present a costly problem for healthcare systems. Engaging care providers in reviewing readmissions may reveal opportunities for reducing readmissions and improving quality. We developed a real-time alerting method that e-mails providers when a discharged patient returns for care (emergency department [ED] or hospital admission) within 30 days. We analyzed the content of alerts to consider frequency of presentation and demographic characteristics of readmitted patients. From 3/15/2011 to 8/31/2011, 1544 alerts (943 ED returns; 601 inpatient readmissions) were generated, representing 621 unique patients (average return time: 12.8±8.5 days). Forty-eight faculty received alerts; 88.8% of alerts were sent to the correct discharging provider. Real-time alerting allows providers to re-engage with readmitted patients and offers a means for evaluating performance. Such systems may also elucidate reasons for readmission by detailing practice patterns and populations at risk for readmission as well as help to design targeted interventions to reduce hospital returns.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".