Abstract 232: Medication Cost Savings Using an On-line Drug Discount Program
Bibliographic record
Abstract
Objective: To evaluate the frequency of drug discount card utilization and to estimate cost savings associated with heart failure (HF) medication prescriptions. Methods: We conducted a retrospective study of all HF prescriptions filled through the NeedyMeds.org drug discount card program nationwide, from January 2009 to December 2016. We evaluated the frequency of drug discount card prescriptions (across pharmacy types, pharmacy location, by prescriber specialty and by drug class) and calculated cost savings (average per drug discount card and total program dollars saved) for entire study period and for each year (from 2009 to 2016). Findings: A total of 381,347 prescriptions for medications that can be used for HF with drug discount cards were identified during the study period (83.7% at national, 5.7% at regional and 9.8% at local pharmacies). Most prescriptions were filled at urban locations (89.1% in urban clusters, 7.6% in urbanized areas) and in ZIP-codes with lower median household income (65.5%). Angiotensin-converting enzyme inhibitors and selected angiotensin receptor blockers were the most prescribed drugs with discount cards (44.1%) followed by beta blockers (27.5%), diuretics (21.5%), and mineralocorticoid receptor agonists (3.9%). The number of HF prescriptions with drug discount cards increased from 2577 in 2009 to 64,750 in 2016. Increase in the number of prescriptions was also noted for all drug classes from 2009 to 2016. Overall 224,049 prescriptions for HF medications (59% of the total) benefited from the program resulting in total savings of $4,739,204 with a median cost saving of $9.30 (41.5%) per prescription. Conclusion: Use of a drug discount program resulted in cost savings on HF prescription medications (approximately $9 in savings per prescription) compared to the original cost charged by pharmacies. While these drug assistance programs may reduce financial burden, continued efforts should be made to improve adherence to medications and for better outcomes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".