Reducing GLP-1 Prior Authorization Friction: A Managed Care Playbook

ABSTRACT

High-cost glucagon-like peptide-1 therapies force payers to balance access and affordability, but many delays and abandonment events are driven less by coverage policy than by avoidable utilization management (UM) friction inside the prior authorization (PA) workflow. This article proposes a practical, reproducible PA Friction Index that payers and pharmacy benefit managers can calculate from existing UM and claims metadata to identify where friction concentrates, monitor equity-sensitive effects, and prioritize fixes that improve time-to-start metrics without loosening clinical safeguards. This commentary outlines the index’s domains and measurable signals, describes where automation helps vs harms (and the guardrails needed), and provides a 90-day implementation checklist focused on standardizing criteria, expanding electronic PA, reducing rework, and continuously monitoring outcomes such as time to decision, abandonment, and appeals.

Am J Manag Care. 2026;32(6):e179-e182. https://doi.org/10.37765/ajmc.2026.89957

Takeaway Points

This article provides a practical measurement tool and a 90-day playbook to reduce avoidable glucagon-like peptide-1 prior authorization (PA) friction, thereby improving initiation, reducing abandonment, and protecting budgets. These insights can help readers achieve the following:

• Compute a PA Friction Index from routine PA logs and claims to identify where delays and rework occur.
• Target fixes (standardized criteria, fewer resubmissions, faster renewals) to the highest-friction drivers rather than loosening coverage broadly.
• Use automation safely for documentation and routing; require transparency, audit trails, and human override for denial decisions.
• Track equity by geography and product line so process improvements reduce, not widen, access gaps.

High-cost glucagon-like peptide-1 (GLP-1) therapies have forced payers to balance affordability and access, but many of the most painful failures occur after a patient is already eligible on paper. The bottleneck is often utilization management (UM) friction: preventable workflow steps that delay initiation, increase provider burden, and raise the odds that a patient abandons therapy before the first fill.¹ Reducing friction is one of the fastest, most scalable ways to improve access without broadening coverage criteria or triggering unintended spend.

Policy and regulation are also moving the industry toward more standardized and electronic prior authorization (PA). The CMS Interoperability and Prior Authorization final rule (CMS-0057-F) requires impacted payers to implement application programming interface (API)–based PA capabilities and report PA decision-time performance for covered medical items and services. Although CMS-0057-F does not directly apply its PA API/process requirements to drug PA, the rule reinforces a broader shift away from opaque, manual, and difficult-to-measure utilization management workflows.^2-4 Yet for GLP-1s, requests still commonly flow through fragmented portals, fax, and attachment-heavy workflows, creating avoidable rework.

This playbook proposes a practical, reproducible PA Friction Index that payers and pharmacy benefit managers (PBMs) can calculate using existing UM and claims metadata. The goal is not to debate whether PA should exist; it is to measure where avoidable friction lives, fix it quickly, and monitor whether access improves without increasing inappropriate utilization.

Is Friction the Point?

A reasonable objection to friction reduction is that friction may be intentional. Administrative complexity can function as a utilization deterrent by slowing or discouraging initiation—and it may be effective at suppressing use. This playbook does not assume that payers are unaware of this dynamic; rather, it argues that relying on opacity and process burden as cost-control mechanisms is increasingly untenable for 4 reasons.

First, friction is a blunt instrument. It deters appropriate and inappropriate use alike, shifts administrative costs to providers and patients, and produces arbitrary access outcomes based on time, literacy, persistence, and clinic resources rather than clinical appropriateness. Patients with limited English proficiency, complex work schedules, or fragmented care are disproportionately likely to abandon requests that would otherwise be approved.^5,6 A plan that intends to limit initiation can achieve the same volume target with explicit clinical criteria—and measure whether the right patients are being excluded—rather than relying on process attrition that cannot be audited for clinical appropriateness.

Second, the regulatory environment is changing. CMS-0057-F requires impacted payers to report PA decision times and implement standardized electronic PA (ePA) by 2027, making hidden friction visible and comparable.^2,3 Federal oversight has documented inappropriate denials in Medicare Advantage PA decisions, and policy makers have raised concerns about algorithmic denial patterns.^7,8 Plans that rely on friction rather than defensible criteria face growing scrutiny and legal exposure.

Third, purchaser and employer expectations are shifting. Large employers increasingly audit PA turnaround times and abandonment rates as part of vendor selection and renewal. A plan that cannot demonstrate operational efficiency risks losing contracts to competitors who can, even if both plans have identical coverage policies.

Fourth, once friction is measured, plausible deniability disappears. The PA Friction Index separates “We chose this policy” from “We implemented the policy poorly.” A plan with high rework rates and long decision latency cannot credibly claim that its access outcomes reflect clinical intent. Measurement creates accountability: If a plan chooses to maintain high friction after seeing the data, that choice becomes visible to regulators, purchasers, and members.

This playbook is not a call to remove clinical controls. It is a call to separate transparent policy levers (criteria, step therapy, renewal requirements, quantity limits) from avoidable process waste (redundant documentation, inconsistent portals, repeated missing-information loops). If a plan intends to limit use, it should do so through published, clinically grounded rules and measure outcomes rather than through opaque delay and rework that cannot be evaluated for appropriateness or equity.

What Happens to Utilization When Friction Is Reduced?

Reducing friction may increase initiation rates, which is a legitimate concern. The appropriate response is concurrent monitoring, not opacity. When implementing the PA Friction Index, plans should track initiation per 1000 eligible members, approval and denial rates by clinical indication, approved-but-not-filled rates within 14 to 30 days, average time to first fill, and net cost trends by line of business. If utilization rises beyond actuarial expectations, the plan can adjust explicit policy levers (criteria stringency, step-therapy requirements, quantity limits) rather than reintroducing friction. This approach allows UM decisions to be auditable, defensible, and equitable, which process-based deterrence cannot achieve.

What Friction Looks Like in the Real World

Friction is not the presence of PA; it is the unnecessary variability and rework inside PA. In practice, friction shows up as (1) unclear or changing criteria, (2) data requests that duplicate information the payer already has, (3) manual submission channels that require multiple touches, and (4) avoidable loops (missing-information requests, peer-to-peer reviews, resubmissions, and appeals).^1,5 These mechanisms lengthen the time to decision and raise the chance that a patient never starts therapy—even when a final approval is ultimately granted.

For GLP-1s, friction is amplified by benefit complexity (medical vs pharmacy benefit), specialty programs, and step-therapy rules that can differ by diagnosis, plan design, and channel. These features do not automatically imply “bad policy”; they become harmful when operational execution is inconsistent and unpredictable.⁵

THE PA FRICTION INDEX

Definition

The PA Friction Index is a reproducible score (0-100) designed to quantify avoidable PA workflow burden for GLP-1 requests, including initiation and renewal requests that may vary by indication, product, benefit channel, continuation-of-therapy status, and step-therapy requirements within a plan, line of business, or PBM book. It is calculated from operational signals that most payers already capture in UM platforms (submission channel, time stamps, status/reason codes, and touchpoints). It can be stratified by provider group, geography, and patient subpopulations to surface where friction concentrates.^6,9

Four Domains (25 Points Each)

• Criteria clarity and standardization: How often are requests delayed because requirements are unclear, inconsistent, or not available at the point of prescribing?
• Submission burden: How many nonclinical “moves” does it take to submit a complete request (attachments, portal logins, manual forms), and what share uses true ePA?
• Decision latency: How long do clean, complete requests wait for a decision?
• Rework burden: How often do requests bounce (missing information, denial, peer-to-peer review, appeal) before resolution?^1,9

For GLP-1s, these domains should be stratified by indication, product, benefit channel, continuation-of-therapy status, and step-therapy requirements because the same drug class can create different PA pathways across lines of business.

Scoring Method (Simple and Reproducible)

Choose 2 to 4 measurable indicators per domain (Table). Convert each indicator to a 0-to-1 score (higher = worse friction) using a benchmark you can explain (eg, internal target, prior-year baseline, or percentile). Average indicator scores within a domain, and multiply by 25. The total score is the sum of the 4 domain scores (0-100).

Minimum Data Needed

At a minimum, the data needed are request identifier, drug, line of business, submission channel (ePA, portal, fax, phone), submission time stamp, decision time stamp, decision status, reason codes (eg, missing information, not covered, step therapy), number of resubmissions, peer-to-peer flag, and appeal flag/outcome. Most payers already have this data. When available, link to pharmacy claims to approximate time to fill and early abandonment (ie, approved but no fill within 14-30 days).^10,11

Why It Matters

A friction index is useful only if it changes priorities. It converts operational burden into a management signal: Leaders can see whether delays stem from policy design, submission channel, documentation gaps, or preventable execution failures. It also enables controlled pilots: Reduce friction in one channel or provider segment and then monitor time to start, abandonment proxies, and net cost trends.¹²

Where Automation Helps vs Harms

Automation is most valuable when it standardizes work and routes clean GLP-1 requests quickly, not when it produces opaque denials. Industry measurement shows persistent gaps in end-to-end ePA adoption.⁹ Safe automation zones include ePA-first submission with required structured fields and real-time benefit or PA requirement checks, fast-path renewals or continuation-of-therapy pathways when objective criteria are met, and smart routing to the correct reviewer to reduce repeated missing-information cycles.^5,13

Where automation can harm is also predictable: Model-driven “deny” recommendations without transparent criteria, weak clinical oversight, or inadequate appeals can increase wrongful denials and erode trust. Federal oversight has documented inappropriate denials in Medicare Advantage PA.^7,8 Guardrails should include clear clinical accountability, explainable denial reasons tied to published policy, bias testing, and continuous monitoring of overturn rates and downstream safety signals.^1,7

Equity Lens: How Friction Concentrates and What to Monitor

Friction is rarely evenly distributed. Patients with limited English proficiency, unstable work schedules, transportation barriers, or fragmented care are less able to complete repeated documentation loops. Medicaid populations can face additional administrative hurdles, and PA reforms in public programs are explicitly focused on delays and burdens.^3,6

At a minimum, stratify friction domains and outcomes by geography, language preference, dual-eligibility status, and other equity-relevant variables available to the plan. Track time to decision, approval-to-fill interval, approved-but-not-filled rates within 14 to 30 days, and appeal initiation and overturn rates.^10,11,14,15

WHAT PAYERS CAN IMPLEMENT IN 90 DAYS

The goal is not to remove PA; it is to remove avoidable work. A practical 90-day sequence is outlined as follows.

Days 1 to 15: Baseline and Governance

• Publish a single GLP-1 criteria source of truth by indication, product, benefit channel, and continuation-of-therapy status, clearly identifying what counts as required documentation and what does not.
• Build a minimal-friction dashboard using the Table indicators and set internal targets.
• Establish a utilization and cost monitoring baseline to track initiation per 1000 members and net cost trends.

Days 16 to 45: Fix the Highest-Yield Failure Modes

• Reduce missing-information loops: prepopulate payer-known data, clarify required fields, and remove redundant attachments.
• Expand ePA use and enforce structured fields to reduce rework.
• Add continuation-of-therapy fast paths and limit unnecessary peer-to-peer reviews.^1,13

Days 46 to 90: Scale, Segment, and Monitor Outcomes

• Pilot a “gold card” pathway for consistently clean submissions with audit criteria.¹⁶
• Track initiation rates, time-to-start, abandonment rates, and net cost trends. If utilization exceeds expectations, adjust explicit policy levers rather than reintroducing opacity.
• Codify automation guardrails and publish an annual review of PA performance (speed, rework, appeals).⁷

CONCLUSIONS

GLP-1 affordability debates will continue, but a large portion of the access problem is operational. Friction may currently serve payer interests by suppressing utilization, but it does so in ways that are arbitrary, inequitable, and increasingly visible to regulators and purchasers. By measuring friction with a simple index and addressing high-rework steps first, payers and PBMs can shorten time to start, reduce abandonment, and improve equity while retaining the ability to manage utilization through transparent, clinically grounded policy levers that are auditable and defensible.

Acknowledgments

The author used ChatGPT, GPT-5.5 Thinking (OpenAI) to assist with drafting, organization, and language editing. The author reviewed, revised, fact-checked, and approved the final manuscript and takes full responsibility for its content.

Author Affiliations: Independent Researcher and Analyst.

Source of Funding: None.

Author Disclosures: The author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design; drafting of the manuscript; and critical revision of the manuscript for important intellectual content.

Address Correspondence to: Aayush Sisodia, MSHI, BDS, Independent Researcher and Analyst. Email: aayushsisodia19@gmail.com.

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13. National Council for Prescription Drug Programs. SCRIPT Electronic Prior Authorization Transactions Overview. National Council for Prescription Drug Programs; August 2013. Accessed May 6, 2026. https://www.ncpdp.org/ncpdp/media/pdf/ncpdp_script_epa_standard.pdf

14. Tzang CC, Wu PH, Luo CA, et al. Metabolic rebound after GLP-1 receptor agonist discontinuation: a systematic review and meta-analysis. EClinicalMedicine. 2025;90:103680. doi:10.1016/j.eclinm.2025.103680

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