Barriers and Consequences of Prior Authorization for Neurologic Medications
Bibliographic record
Abstract
Importance: Prior authorization (PA) is widely used by insurers to control health care costs and promote high-value care, but it can create significant barriers to accessing medications. This is particularly concerning in neurology, where timely treatment is critical to avoid disease progression and optimize patient outcomes. Objective: To assess the consequences, barriers, and facilitators of PA policies affecting access to pharmacologic treatment in 6 common neurologic conditions-Alzheimer disease, Parkinson disease, multiple sclerosis, migraine, cerebrovascular disease, and epilepsy-with focus on impacts on patients, clinicians, and administrators. Evidence Review: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines for scoping reviews were followed, and the study protocol was registered on Open Science Framework. MEDLINE and Embase were searched up to November 1, 2024, using Ovid for studies that assessed the role of PA as a primary or secondary outcome for the 6 included neurologic conditions, or for neurology broadly if strongly applicable to the study aim, after the signage of the Affordable Care Act in March 2010. Abstract screening and full-text review were done in duplicate. Key information was charted in extraction, including study characteristics, demographics, methods, results, and implications for relevant stakeholders. The results were aggregated and thematically analyzed. Findings: A total of 364 studies were identified using our search strategy on Ovid, 278 records were screened, and 20 studies were included in this review. The most frequently identified consequences for patients were delays in care (60%) and increase in disease activity (25%). The most frequently identified consequence for clinicians (35%) and administrators (15%) was time burden. The most common facilitators were the use of clinical pharmacists or technicians (20%) and health system specialty pharmacies (15%). Conclusions and Relevance: According to the results of this scoping review, PA can contribute to significant access barriers for people with neurological conditions and is associated with burden for all stakeholders involved. Reforms to PA can work towards more equitable access to medications for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".