Assessing Discharge Communication and Follow-Up of AKI: An Opportunity for Quality Improvement
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) affects up to 20% of hospitalizations and is associated with increased chronic kidney disease, mortality, and healthcare costs. Proper documentation of AKI in discharge summaries is critical for optimal monitoring and treatment of these patients once discharged. This study aimed to evaluate the accuracy and quality of documentation of episodes of AKI at a tertiary care centre, in British Columbia, Canada. Methods: This was a retrospective chart review study of adult patients who experienced AKI during hospital admission between January 1, 2018, and December 31, 2018. Laboratory data was used to identify all admissions complicated by AKI defined by KDIGO criteria. A random sample of 300 AKI admissions stratified by AKI severity (e.g., stage, 1, 2, and 3) were identified for chart review. Discharge summaries were reviewed for documentation of the following: presence of AKI, severity of AKI, AKI status at discharge, practitioner and laboratory follow up plans, and medication changes. Results: Of the 300 discharge summaries reviewed, 38 were excluded. AKI was documented in 140 (53%) of discharge summaries and was more likely to be documented in more severe AKI: stage-1 38%; stage-2 51%; stage-3 75%. Of those with their AKI documented, 94 (67%) documented AKI severity and 116 (83%) mentioned the AKI status at discharge. 239 (91%) of discharge summaries mentioned a follow up plan with a practitioner, but only 23 (10%) had documented nephrology follow up. For laboratory investigations, 92 (35%) of summaries had documented recommendations. In summaries that included medications typically held during AKI, only about half made specific reference to those medications. For those with NSAIDS listed, 64% mentioned holding, and 9% mentioned a discharge plan. For those with ACEi/ARB, 38% mentioned holding these medications, and 46% mentioned a discharge plan. In ones with diuretics listed, 35% mentioned holding, and 51% included a discharge plan. Conclusions: We found suboptimal quality and completeness of discharge reporting in patients hospitalized with AKI. This may contribute to inadequate follow up and post hospitalization care for this patient population. Strategies are required for increasing the presence and quality of AKI reporting in discharge summaries.
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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 | 0.057 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".