Impact of pharmacist discharge counseling on hospital readmission and emergency department visit
Bibliographic record
Abstract
Objective: The enactment of the Affordable Care Act (ACA) in 2010 imposes payment penalty on hospitals with high hospital readmission rates. In an effort to reduce readmissions, a pharmacist discharge counseling program was implemented to facilitate transition of care to the outpatient setting. Our study objective was to evaluate the impact of the program on hospital readmissions and visits to the emergency department (ED).Methods: This was a single-center, retrospective cohort study conducted at a not-for-profit, teaching community hospital with 462 total beds. Pharmacists provided counseling to patients discharged from the medicine floor between November 2013 and January 2014, and included those considered to be high-risk (e.g., taking 5 scheduled medications and had diseases such as congestive heart failure and diabetes mellitus). Descriptive analysis was performed and outcomes were compared between patients who did and did not receive pharmacist counseling.Results: Of a total of 889 discharged patients, 488 (55%) received counseling from a pharmacist. For the entire cohort, mean age was 55 ± 20 years; Charlson Comorbidity Index (CCI) score was 2.74 ± 2.95; and length of hospitalization was 4 ± 4 days. These parameters were not statistically different between the two groups. Within 30-days after hospital discharge, significantly fewer subjects who received counseling, compared with those who did not, were readmitted to the hospital (11.3% vs. 15%, p = .009) or visited the ED (10.6% vs. 15%, p = .005).Conclusions: Discharge counseling provided by pharmacists during transitions of care at a community hospital significantly reduced 30-day readmission and ED visit rates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".