NERDS-5: Strategies to Eliminate Fraud, Waste and Abuse Survey

Welcome to NERDS-5: Strategies to Eliminate Fraud, Waste, and Abuse (FWA)—a complimentary virtual lunch-and-learn series hosted by the New England States Consortium Systems Organization (NESCSO).

As Health and Human Services (HHS) agencies increasingly coordinate cross-agency operations, protecting public resources requires a unified approach. NERDS-5 expands the national program integrity focus beyond traditional Medicaid boundaries to encompass SNAP, TANF, child welfare, and HHS sister agencies nationwide.

This 45-minute virtual series brings together State Medicaid and Sister Agency Executives, Program Integrity & Compliance Officials, Operations Directors, Policy Advisors, and Oversight Teams to explore practical regulatory frameworks, operational case studies, and emerging technology (including AI/ML and predictive analytics).

Key Details:

Cost: Free of charge (a benefit provided by NESCSO to the MES and HHS community).

Environment: Attendance is strictly restricted to state and federal government personnel to maintain an open, collaborative forum.

Format: Tuesday through Friday 45-minute virtual sessions at 12:00 PM ET.

Note on Schedule & Registration:

The December 2026 session schedule is finalized and open for registration below. The January through March 2027 webinar schedule is currently being updated with featured presenters and will be released shortly. Completing your registration today will secure your spot for the December session and ensure you receive automatic updates as upcoming dates are finalized.

1.Please select the sessions listed below to register. A Zoom link will be sent to you prior to the session.

December 1, 2026 - CALIBER360 Healthcare AI - Beyond Claims: Using Patient Experience Analytics to Identify and Reduce Healthcare Waste
Healthcare program integrity traditionally focuses on claims, providers, and payments. This session explores an additional signal for identifying potential waste: patient experience. Using a large health-system case study linking patient-reported Net Promoter Score (NPS) data with electronic health record and utilization data, we examine statistically significant associations between patient experience and potentially inefficient patterns of care. Lower patient experience was associated with greater emergency department utilization and missed appointments, while more favorable experience was associated with greater preventive-service utilization and longer primary-care relationships. The session demonstrates how health and human services agencies can integrate experience, clinical, and utilization data to identify populations and processes associated with avoidable resource use. It also explores how predictive analytics and AI can extend this methodology into prospective identification and targeted interventions while addressing survey-response and representation bias.

Key Take Aways:
1. Learn how patient-experience data can augment claims analytics to identify potentially avoidable ED utilization, missed appointments, and gaps in preventive care.
2. Learn a practical methodology for linking experience, clinical, and utilization data to identify actionable patterns of healthcare waste.
3. Understand how predictive analytics and AI can operationalize these signals while accounting for survey-response bias and responsible model governance.

December 2, 2026 - Public Consulting Group - From Pay-and-Chase to Prevent-and-Protect: Evolving Program Integrity Solutions
For decades, Medicaid Program Integrity has traditionally relied heavily on post-payment analysis driven "pay-and-chase" approaches that identify and recover improper payments/FWA. As fiscal pressures increase and advanced analytical capabilities mature, agencies have new opportunities to shift toward prevention-focused strategies that identify risk before payment and optimize programmatic cost savings. This session explores the evolution of Program Integrity from retrospective recovery to proactive protection through pre-pay intervention, risk-based claim review, and advanced analytical techniques. Drawing on lessons from large-scale Medicaid implementations, participants will learn how agencies can balance payment accuracy, provider burden, operational efficiency, and program integrity outcomes while modernizing existing workflows. Attendees will leave with practical considerations for implementing pre-pay integrity strategies that complement traditional post-pay activities and optimize cost avoidance and FWA identification opportunities.

Key Take Aways:
1. Understand how Medicaid agencies can evolve from traditional pay-and-chase models to prevention-focused Program Integrity strategies.
2. Evaluate how risk-based pre-pay intervention can improve payment accuracy and complement traditional post-pay recovery efforts.
3. Identify the operational, governance, and workflow components needed to implement effective pre-pay integrity programs at scale.

December 3, 2026 - Gynisus Inc. - Preventing Improper Medicaid Payments with AI-Driven Prepay Clinical and Claims Intelligence
Medicaid programs face a persistent challenge: identifying improper payments early enough to prevent them, without creating unnecessary provider abrasion or relying solely on retrospective recovery. This session will examine how states can combine claims data, clinical evidence, coding logic, and AI-driven analytics to identify high-risk claims before payment while preserving appropriate human oversight. The approach uses layered detection methods, including rules, statistical and machine-learning models, and clinical reasoning, to prioritize cases for action and distinguish likely errors from normal variation. The session will also address how states can apply the same intelligence across post-pay review, MCO oversight, and program-integrity analytics, creating a continuous feedback loop that improves detection over time. Practical implementation considerations will include data quality, explainability, clinical validation, workflow integration, and measurement of avoided or recovered spend.

Key Take Aways:
1. How layered AI, claims analytics, and clinical intelligence can identify incremental Medicaid payment risk before and after payment.
2. How to prioritize high-value findings while preserving human oversight, explainability, and low provider abrasion.
3. Outcome and appeal feedback can continuously strengthen program-integrity detection and MCO oversight.

December 4, 2026 - HeyMediciad - From Paperwork to Platform: AI-Driven Medicaid Enrollment & Recert for the 2027 Mandate
The One Big Beautiful Bill Act's semi-annual Medicaid recertification mandate takes effect January 2027 — doubling renewal volume for every state agency, managed care organization, and health system in the country, with no corresponding increase in staff or budget. This session examines how AI-powered enrollment and recertification platforms are eliminating the paperwork failures that account for 69% of Medicaid disenrollments today. Presenters will walk through operational deployments showing how voice-first, multilingual AI guides patients through enrollment in under 15 minutes, how automated outreach intercepts lapses before they hit provider A/R, and how real-time eligibility dashboards give compliance and operations teams the visibility they need to meet CMS reporting requirements. Attendees will leave with a practical framework for evaluating AI-driven enrollment tools and a clear view of what implementation looks like at a live health system.

Key Take Aways:
1. How AI eliminates the paperwork failures driving 69% of Medicaid disenrollments — and what operational deployment looks like at a live health system today.
2. A practical framework for meeting the 2027 semi-annual recertification mandate without adding headcount, using automation to absorb doubled renewal volume.
3. How real-time eligibility dashboards and audit-ready documentation give compliance, program integrity, and operations teams the visibility needed to satisfy CMS oversight requirements.

December 8, 2026 - CITIZ3N Government Solutions - Fewer Eligibility Errors, Less Manual Work: Cross-Agency Strategies for Program Integrity
How can agencies strengthen program integrity without creating more work for staff or unnecessary barriers for eligible people? This session explores how cross-agency data verification can identify eligibility discrepancies earlier and support prevention of improper payments. Drawing on Mississippi’s Fraud & Abuse Module, which supports Medicaid, SNAP, and TANF, we will examine practical ways to combine internal and external data, apply program-specific rules, and deliver actionable alerts within existing eligibility workflows. Discussion will address data-sharing governance, source quality and timeliness, risk-based prioritization, and the distinction between inconsistent information and evidence of fraud. Participants will leave with a framework for reducing duplicate verification work, strengthening documentation, and measuring operational results. The focus is on lessons agencies can apply, with human review and beneficiary protections built into the approach.

Key Take Aways:
1. Identify cross-agency data sources, sharing permissions, and program-specific rules needed to strengthen eligibility verification.
2. Design alerts and review workflows that prioritize meaningful discrepancies while limiting false positives and protecting eligible beneficiaries.
3. Measure program-integrity improvements using review time, confirmed discrepancies, repeat verification requests, and documented resolution outcomes.

December 9, 2026 - Complete Circle Compliance - Beyond FWA Detection: Building a Centralized Program Integrity Model for HCBS Oversight
HCBS programs often rely on decentralized provider networks, multiple oversight entities, and inconsistent documentation practices, creating blind spots that allow fraud, waste, abuse, and noncompliance to remain undetected. Using New York Social Adult Day Care as a practical case example, this session examines how Medicaid, NYS and HHS agencies can replace episodic, organization-by-organization oversight with a centralized program integrity model. The approach combines specialized personnel, standardized audits, one-click multi-source credentialing, daily reconciliation of attendance, transportation, authorization, capacity, staffing, and employee-presence records, centralized evidence management, corrective-action tracking, statistics, and longitudinal monitoring. Participants will learn transferable governance and workflow methods that strengthen prevention, detection, remediation, and accountability while preserving human review, due process, and agency decision-making.

Key Take Aways:
1. Participants will learn how centralized, specialized oversight and standardized evidence requirements can reduce blind spots across decentralized HCBS and HHS programs.
2. Participants will understand how one-click credentialing and daily service-evidence reconciliation can surface discrepancies while preserving trained review and agency decision-making.
3. Participants will apply a closed-loop approach linking detection, corrective action, verification, centralized statistics, and longitudinal monitoring to measure sustained compliance.

December 10, 2026 - basys.ai - Beyond Claims Rules: Using Explainable, Responsible AI to Strengthen Medicaid Program Integrity
Medicaid program integrity teams face a difficult challenge: identifying fraud, waste, and abuse at scale without overwhelming investigators with false positives or relying solely on claims-level rules that may miss important clinical context. This session will explore how explainable, agentic AI can augment traditional program integrity approaches by connecting claims with clinical evidence, policies, and patterns across a patient’s care journey. We will discuss an approach we have implemented for CMS - it decomposes complex FWA analysis into auditable steps, surfaces the evidence behind potential concerns, and keeps human reviewers responsible for consequential decisions. Our approach has also been informed by partnerships and data-sharing collaborations with Mayo Clinic and Eli Lilly, including input from physicians who have helped shape how clinical context is incorporated into these workflows. The session will also examine practical considerations for deploying AI responsibly, including explainability, validation, privacy, and human oversight. CMS recently spotlighted basys.ai’s approach in its Crushing Fraud, Waste & Abuse whitepaper(https://www.cms.gov/files/document/crushing-fraud-chili-cook-white-paper.pdf), and basys.ai was recognized as a national finalist in CMS’s Crushing FWA Challenge (https://orangeslices.ai/basys-ai-receives-top-recognition-from-cms-in-the-crushing-fraud-waste-and-abuse-challenge/)

Key Take Aways:
1. Learn how clinical context and clinician’s tacit knowledge can create the right scaffolding for agentic AI to improve payment integrity and identify potentially higher-risk FWA cases.
2. Understand how explainability, evidence-linked reasoning, and human oversight can support responsible AI adoption in Medicaid program integrity.
3. Explore practical ways to prioritize investigative resources while maintaining auditable workflows and human decision authority.

December 11, 2026 - Boston Consulting Group - Making Machine Learning Work for Pre-pay Fraud Prevention
While many state Medicaid agencies have historically focused their program integrity efforts on enforcement and recovery, several states have demonstrated interest in incorporating pre-pay analytics that go beyond pre-written deterministic rules to prevent improper payments before they occur. This session will demonstrate how AI-enabled analytics can safely complement existing rules and edits by identifying risk patterns that deterministic rules may miss. Drawing on a recent pilot with a large US state Medicaid agency, we will show how machine learning can improve existing systems by detecting and categorizing emerging risk behaviors, such as fragmented billing and severity inflation; how models work in tandem with human reviewers; and how thoughtfully designed processes can protect timely payments to valid providers. Finally, we will address the operating model, governance, data, and change management requirements that drive, rather than just enable, the effort.

Key Take Aways:
1. Technology is only 10% of success: Change management, human reviewer training, and operating model require real investment but drive more value than the model itself.
2. AI as enabler: AI should augment, not replace, existing rules and edits. States should layer on machine learning incrementally, with generative AI added over time.
3. Continuous learning improves on static rules: Model retrains quarterly on reviewer decisions, improving targeting over time and surfacing emerging fraud patterns missed by rules-based systems.

2.Please provide your contact information. We will not share the participant list with the vendors.

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