WISeR
Wasteful and Inappropriate Service Reduction (WISeR) Model
The Wasteful and Inappropriate Service Reduction (WISeR) Model helps protect American taxpayers by leveraging enhanced technologies, such as Artificial Intelligence (AI) and Machine Learning (ML), along with human clinical review, to ensure timely and appropriate Medicare payment for select items and services. The voluntary model encourages care navigation, encouraging safe and evidence-supported best practices for treating people with Medicare.
Innovation
Leverages enhanced technologies like AI and ML alongside human clinical review to expedite prior authorization, aiming for auto-approvals where possible and a 72-hour turnaround time, while introducing a 'gold carding' exemption program for consistently compliant providers.
The Problem
Waste in health care can not only harm patients but also contributes to up to 25% of health care spending in the United States. Wasteful, low-value services often have limited clinical evidence of effectiveness, may not align with an individual’s specific health condition or needs, or can lead to complications and further unneeded services.
The Solution
The WISeR Model helps reduce clinically unsupported care by working with companies experienced in using enhanced technologies to expedite and improve the review process for a pre-selected set of services that are vulnerable to fraud, waste and abuse.
Expected Outcomes
The WISeR Model helps ensure people with Medicare receive the most appropriate care that supports the best health outcomes while decreasing costs and easing administrative burden on providers and suppliers who go through the prior authorization process.
Strategy
The WISeR Model empowers patients to partner with their health care providers on the most clinically appropriate care plan; protects the taxpayer by decreasing fraud, waste and abuse; and focuses providers on care that has the most impact on the well-being of people with Medicare.
Model Goals
- Promote evidence-based prevention by introducing clinical review with enhanced technology to ensure patients are receiving the most appropriate and effective care
- Empower people to achieve their health goals by setting the stage for beneficiaries partnering with their clinicians on the most effective care plans
- Drive choice and competition for people by applying new tools in Original Medicare
- Reduce inappropriate, unnecessary and invasive procedures that can significantly harm patients
Patient Eligibility
People enrolled in Original Medicare receiving select targeted services in one of the six model states.
- Enrolled in Original Medicare
- Receiving care in New Jersey, Ohio, Oklahoma, Texas, Arizona, or Washington
- Receiving specific targeted services such as skin substitutes, knee arthroscopy, or electrical nerve stimulation
Provider Eligibility
Providers and suppliers delivering selected services in specific care settings within the six designated states.
- Located in New Jersey, Ohio, Oklahoma, Texas, Arizona, or Washington
- Delivering selected services in Hospital Outpatient Departments (HOPDs), Ambulatory Surgery Centers (ASCs), office, or home settings
- Must submit prior authorization requests or be subject to pre-payment medical review for selected services
Care Categories
Wound Care
Orthopedics
Pain Management
CMS Benchmarks & Thresholds
financial
operational
population
Operational Friction Summary
Where operational friction is highest for this model.
Providers must identify patients considering targeted low-value services early enough to either gather robust clinical evidence for AI review or navigate them to conservative, evidence-supported treatments.
Community health organizations must track the real-time status of AI/ML determinations and turnaround times to prevent care delays and monitor for inappropriate denial patterns.
Financial accountability hinges on securing appropriate determinations before the clinical intervention occurs, as retrospective denials for targeted services are difficult to overturn.
Success requires aligning the clinical behavior of specialists (who are traditionally incentivized by procedural volume) with the model's goal of reducing wasteful, low-value services.
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