Impact of an innovative inpatient patient navigator program on length of stay and 30‐day readmission
Bibliographic record
Abstract
BACKGROUND: The current climate of increasing patient complexity coupled with rising costs have prompted the need for adaptive innovation. There are limited data describing inpatient interventions targeting improvements in both communication and transitional care. OBJECTIVE: Evaluate the patient navigator (PN) program, an innovative inpatient intervention intended to enhance navigation through the complexity of hospital admissions for patients and providers. INTERVENTION: PNs were dedicated patient-care facilitators without clinical responsibilities integrated as full members of the inpatient care team responsible for enhancing communication between and among patients and providers. DESIGN: Observational retrospective cohort study. PATIENTS: All patients admitted to the general medical service between July 2010 and March 2014. SETTING: Academic medical center. MEASUREMENTS: Primary outcomes were hospital length of stay (LOS) and 30-day readmission rate matched by case mix group, age category, and resource intensity weight. RESULTS: Our matched cohort included 5628 admissions (4592 patients) exposed and 2213 admissions (1920 patients) not exposed to PNs. Admissions with PNs were 1.3 days (21%) shorter than admission without PNs (6.2 vs 7.5 days, P < 0.001). Thirty-day readmission rate was not different between the 2 groups (13.1 vs 13.8%, P = 0.48). CONCLUSION: Implementation of this intervention was associated with a reduction in LOS without an increase in 30-day readmission.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".