Utilization of a Visit-Based Sepsis Assessment to Prevent Hospital Readmissions
Bibliographic record
Abstract
Sepsis results in 270,000 deaths annually in the United States. Despite the current healthcare focus on sepsis, there exist few postacute best-practice standards to rapidly identify health changes in home healthcare patients to prevent and reduce hospital readmissions due to sepsis. We systematically examined whether an evidence-based process and intervention triggering home healthcare clinicians to activate a Positive Sepsis Assessment would reduce the likelihood that the patient would be readmitted to the acute care hospital. Over 24 months, we tracked the rate of sepsis readmissions to acute care hospitals through the initial phase of early recognition education; assessment, review, and revision of best-practice algorithms; standardized documentation; and proactive care management, in conjunction with the patient's primary care provider. During our review of the last 12 months of data on home care patients triggering the Positive Sepsis Assessment 130 patients were identified to have potential signs of sepsis. Ninety-seven of these patients received early medical intervention in place and were not readmitted to the hospital. Our findings suggest that a multidisciplinary home healthcare team utilizing standard sepsis education and sepsis algorithm on every patient during every visit can reduce and prevent readmissions.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".