Structured handoff to improve communication from inpatient to outpatient dialysis units: A quality improvement project
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage kidney disease requiring dialysis encounter high hospital readmission rates. One contributor is poor communication between hospitals and outpatient dialysis facilities. We hypothesized that improved communication may reduce 30-day hospital readmissions for patients on dialysis at an urban, safety net hospital. METHODS: We created a standardized discharge handoff tool that is easy to use and provides concise data for dialysis centers. The handoff tool is a novel, electronic MACRO template (called a "dot-phrase") to be included in discharge documentation. Instructions for the dot-phrase and electronic facsimile (e-faxing) were sent to Internal Medicine residents immediately prior to their rotation on an inpatient Renal service. We then measured the intervention implementation rate and its impact on hospital readmission metrics. RESULTS: We compared 3 months of preintervention and 6 months of postintervention data, identifying 82 and 135 index discharges in each respective study period. Patients were predominantly male (56.2%) and receiving hemodialysis (89.8%); a minority (9.2%) were undomiciled at the time of discharge. Mean age was 60.5 years (SD 14.0). Renal discharges followed by 30-day Renal readmission were not statistically lower in the postintervention group for the index discharge alone (26.8% vs. 20.0%, p = 0.12), but were for overall discharges (51.2% vs. 25.7%, p < 0.0001). The dot-phrase was used in 95.4% of discharge summaries, and 74.7% of discharge summaries were e-faxed within 24 h of discharge. CONCLUSION: There was high uptake of a standardized discharge handoff tool among Internal Medicine residents on a Renal inpatient service. Using a handoff tool and e-faxing may improve communication with outpatient dialysis centers and may reduce readmissions among some patients but is likely insufficient to fully address high readmission rates. Subsequent intervention iterations would benefit from further collaboration with outpatient dialysis units for customization of the handoff tool to meet local communication needs.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".