Medication Reconciliation in Veterans Receiving Dialysis at Edward Hines VA Hospital: A Quality Improvement Initiative
Bibliographic record
Abstract
Background: Polypharmacy, commonly defined as use of 5 or more medications, is associated with higher morbidity, fall risk, functional decline and disability. We aimed to identify prevalence of medication discrepancy and possible risk factors in veterans receiving dialysis at Hines VA hospital. Methods: Eligible patients were asked to bring all pill bottles at least once during the study period, October 2022 to March 2023. Medical records were then reviewed to identify discrepancies. Collected data included age, race, sex, cause of end stage renal disease (ESRD), dialysis vintage time, dialysis modality, no: of providers involved, no: of prescribed medications and presence of care giver at home. Cognitive screening was done using Montreal Cognitive Assessment Test (MoCA). Results: A total 48 patients participated in the study. Baseline characteristics are shown in Table 1. 32 patients had at least one medication discrepancy. Compared to patients that completed MoCA (36/48), those that declined (12/48) had higher percentage of medication discrepancies (75% vs. 63%) despite having a caregiver (71% vs. 61%) (Image 1). 11 of the 12 patients declining MoCA were receiving hemodialysis (HD). Conclusions: We found high prevalence of medication discrepancy in our veteran ESRD patients. MoCA scores were not associated with medication discrepancies, but those declining MoCA or with poor overall interest tended to have more discrepancies. Our study limitation is small sample size of a single center veteran population. Funding: Veterans Affairs Support - Baseline characteristics of study population Age in years Average age: 68 Race 29-African American 17-White 2-Other race Sex 46-Male 2-Female Cause of ESRD 29-DM 6-HTN 3-GN 10-Other Dialysis vintage time in years Average 3.7 years Modality of dialysis (HD vs. PD) 41-HD 7-PD No: of providers involved in care Average: 3 No: of prescribed medications Average: 9 Mean MoCA score (N = 36) 20 (total score = 30) Image 1
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".