AMCP Partnership Forum: Optimizing Prior Authorization for Appropriate Medication Selection
Bibliographic record
Abstract
Prior authorization (PA) and step therapy (ST) are utilization management tools that have been in use by managed care organizations for decades. These processes require that health care providers obtain advanced approval to qualify a specific product for coverage from a health plan before it is delivered to the patient. These tools are intended to ensure that patients have access to evidence-based medications while payers remain good stewards of limited health care resources. PA and ST are growing in use to support appropriate use of medications and manage associated costs but may pose challenges related to administrative burden and access to care. In June 2019, the Academy of Managed Care Pharmacy (AMCP) conducted a multistakeholder forum to identify processes for optimizing PA and ST utilization management programs. Health care leaders representing academia, health plans, integrated delivery systems, pharmacy benefit managers, employers, federal government agencies, national health care provider organizations, and patient advocacy organizations participated in the forum. Participants explored current operations of these programs, evaluated stakeholder perspectives on opportunities to improve these programs, and provided recommendations for next steps. They also reviewed current federal and state legislative and regulatory activities to reform PA and ST processes and offered guidance to support program improvements. The goal of the forum was to gather stakeholder input to inform the development of recommendations to improve efficiencies around PA and ST processes; provide recommendations to address administrative burdens; increase the visibility of the clinical and economic value of PA and ST utilization management programs; collect, review, and disseminate data-driven, real-world experiences of PA programs that support clinical and economic value; collect and disseminate best practices around PA appeals and denial processes; and improve channels of communications between health insurance providers, health care professionals, and patients to minimize care delays and improve clarity of coverage authorization requirements. DISCLOSURES: This AMCP Partnership Forum was sponsored by Mallinckrodt Pharmaceuticals, Merck, the National Pharmaceutical Council, and Takeda. These proceedings were prepared as a summary of what occurred at the forum to represent common themes; they are not necessarily endorsed by all attendees nor should they be construed as reflecting group consensus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".