Pharmaceutical Care, MTM, & Payment: The Past, Present, & Future
Bibliographic record
Abstract
Central to any discussion of payment reform is the need for a rational scientific medication use system to ensure that drug-related morbidity and mortality are minimized. The care provision process is based on a comprehensive assessment of all of a patient's drug-related needs and it behooves pharmacists to conduct a comprehensive assessment as do all other health professions. This comprehensive assessment is the foundation of medication therapy management (MTM) services provided within the practice of pharmaceutical care. Care can be delivered in the community by clinically oriented pharmacists, although building a practice is hard work much different from the business of dispensing medications. The number of pharmacists needed to provide comprehensive MTM services for every American is projected to range from 30,000 to 100,000 based on data/experiences from Minnesota, Ontario, and elsewhere. These individuals may benefit from some type of provider recognition so that society can differentiate between pharmacists who provide comprehensive MTM services and those in drug distribution roles. Approaching the legislature and policymakers with cost savings data, partnering with the business community, and focusing on dual eligible patients and those with unmet mental health needs are important strategies to make this practice transformation a reality.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".