Ability of Nurse Clinicians to Predict Unplanned Returns to Hospital Within Thirty Days of Discharge
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of nurse clinician discharge flow coordinators (DFCs) to identify medical patients at risk of unplanned return to the hospital emergency department within 30 days of discharge and whether a higher predicted risk of return was correlated with preventability. DESIGN: Prospective cohort study of patients discharged from medical wards at 2 hospital sites of the McGill University Health Center between September 1, 2011, and January 1, 2012. METHODS: Univariate and multivariate analyses of factors including the ability of DFCs to predict 30-day unplanned returns to the hospital. Assessment of the preventability of returns to the hospital was performed by chart review using prespecified criteria. The ability of DFCs to predict returns was compared to the LACE criteria (length of stay, acute admission through the emergency department, comorbidities, and emergency department visits in the past 6 months). RESULTS: We found that 25.0% (95% CI, 21.3-28.5) of our patients returned to the emergency department within 30 days. The DFC predictions were found to be significant in both univariate and multivariate analysis. Patient age, sex, and length of stay were not significant predictors in univariate or multivariate analysis; 13.9% (95% CI, 8.2-19.6) of returns were preventable and a further 25.8% (95% CI, 18.1-33.5) were potentially preventable with added services in the community. There was a trend toward more preventable or potentially preventable returns with higher predicted probability of return. In contrast the LACE criteria did not have a good predictive capacity in our patient population. CONCLUSION: In a large urban center, experienced nurse clinician DFCs were able to predict 30-day emergency department returns with reasonable accuracy. They were also able to identify the returns to the hospital that were most likely to be preventable. Our data suggests that DFCs can be used to target patients identified as having an increased probability of return with interventions that may be able to reduce the burden of return to hospital.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".