Rendering medically unnecessary treatments: A couple from AZ, owning multiple medical facilities, together with several con-conspirators targeted elderly vulnerable Medicare patients, many of whom were terminally ill in hospice care, causing medically unnecessary amniotic wound allografts to be applied. From 2022-24, these fraudsters filed $900M+ in fraudulent claims to Medicare for medically unnecessary allografts applied to 500 patients. Medicare and other healthcare programs paid $600M+ in claims, with the fraudsters receiving $330M in illegal kickbacks as a result of the fraud schemes, which they used to further the fraud and diverted for their personal benefit and the benefit of others. (Justice.gov)
Billing for services not rendered: Fraudulent providers and accomplices systematically submit claims for services that were never provided, often using stolen patient identities to fabricate entire medical histories. In a notable case prosecuted in the Northern District of Texas, six defendants defrauded health plans of over $163 million through bogus telemedicine consultations and fabricated medical orders, illustrating the audacity and scale of such operations (Justice Gov).
Falsified patient information: Fraudsters manipulate patient diagnoses to justify unnecessary treatments and surgeries, inflating insurance claims. In Pennsylvania, defendants were charged with fabricating patient conditions to push unnecessary opioid prescriptions and medical services, defrauding insurers of approximately $5.4 million (Justice Gov).
Durable Medical equipment (DME) fraud, waste and abuse: Fraudulent networks profit from bogus claims for medical equipment, such as braces and durable medical goods. A recent lawsuit against multiple organizations exposed an $18.7 million scam involving falsified claims to a health plan, underscoring the extensive collusion across entities to exploit insurance payouts (lawstreetmedia)
Financial risks: Fraudulent activities inflate healthcare costs, burdening consumers with higher premiums and jeopardizing insurer solvency. In 2023 alone, healthcare fraud, waste and abuse incurred estimated losses exceeding $100-300 billion, a staggering toll on both insurers and policyholders (justice.gov, NHCAA).
Regulatory risks: Regulators are intensifying scrutiny on fraud, imposing substantial fines and stringent oversight on non-compliant insurers. In 2023 alone, the top 10 recoveries under the False Claims Act (FCA) totaled $1.8B. Moreover, insurers risk losing lucrative government contracts, such as Medicare and Medicaid, if unable to curb fraudulent activities effectively (top 10 recoveries) .
Health systems dropping plans: Mounting financial pressures from fraudulent claims are prompting health systems to abandon Medicare Advantage plans. In 2023, several major hospital networks cited unsustainable losses and operational challenges, exacerbating insurer woes and threatening market stability (Becker's Hospital Review).
Reputational risks: Beyond financial penalties, the fallout from fraud, waste and abuse tarnishes insurers' reputations, eroding trust among stakeholders and customers alike. A 2023 study revealed that reputational damages from fraud, waste and abuse scandals can eclipse financial penalties by a substantial margin, undermining market position and customer loyalty (LegalTech Solutions) (CEPR).
Advantages of AI in fraud, waste and abuse detection: Artificial Intelligence (AI) offers significant advantages over traditional methods in identifying and combating fraud, waste and abuse. AI can analyze large datasets from various sources, identifying patterns and connections that may indicate fraudulent activity. This includes examining company ownership, stakeholder relationships, linkages between providers, and common identifiers among patients.
Impact of a ‘home-grown’ solution versus external vendor on FWA
Implementing AI systems for fraud, waste and abuse detection, however, comes with its own set of challenges. Building these systems in-house requires significant investments in time, cost, and human resources. According to industry reports, developing AI-based fraud, waste and abuse detection systems involves extensive data collection, algorithm development, and continuous model training. Insurers may need to allocate substantial budgets for technology infrastructure, hire specialized data scientists, and ensure ongoing maintenance and updates to keep the systems effective. These expensive data science resources and AI/ML infrastructure costs are substantial, and there is no guarantee that the solution will offer results from day one. This high investment underscores the importance of carefully planning and budgeting for AI integration to maximize its benefits and ensure long-term sustainability in fraud, waste and abuse detection efforts. Pursuing such an initiative with a partner with the skills and expertise in this area will prove to be far more effective, less risky and more cost effective than an insurer trying to build an AI system themselves.