Fixed and flexible formularies as cost-control mechanisms
Bibliographic record
Abstract
The purpose of this review is to consider the prevalent types of fixed and flexible formularies, the general economic principles on which they are based and the evidence for their effectiveness in controlling rising drug expenditures. The principal-agent relationship and economic model underlying the various types of formularies are described. The principal-agent model describes a relationship where there is an asymmetry of information between two parties involved in a particular task. As a result of this asymmetry of information, the party with less information (the principal) allows the party with more information (the agent) to make decisions about that task or activity for them. In the case of formularies and cost-control, the principal is the payer. Depending on the incentives offered by the formulary, the agent can alternately be the prescriber, dispenser or patient. The success of a formulary type to control costs is dependent on two main factors. First, the payer (the principal) must identify the agent for whom it is reasonable to create incentives that incorporate the financial risks associated with use of the drugs. Second, the payer must develop a structure that best aligns the principal and agent objectives. The principal-agent framework serves as the vehicle through which the authors examine five major types of formularies (i.e., closed, best available price, reference-based pricing, tiered and open formularies) and their inherent incentives and limitations. The evidence for their effectiveness as cost-control mechanisms is reviewed and the system factors that can affect formulary success will be discussed. Finally, the authors' observations are summarized and interpreted, and suggested implications for future use of formularies in controlling the costs of pharmaceutical use are offered.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".