FRAUD WASTE ABUSE ANIL NAIR CEO IRIS HEALTH

FRAUD, WASTE & ABUSE ANIL NAIR CEO, IRIS HEALTH SERVICES LLC

Fraud is defined as the wrongful or criminal deception intended to result in financial or personal gain. Fraud includes false representation of fact, making false statements, or by concealment of information

What exactly is FWA? FWA can be divided into three distinct categories: Fraud: intentional concealment or deception to gain something of value (e. g. Billing for services at an inflated rate). Both fraud and abuse can expose a provider or vendor to criminal and civil liability Waste: excessive use of health services, although not necessarily intentional wrongdoing (e. g. Prescribing a medically unnecessary procedure based on comfort level). Abuse: practices that are inconsistent with accepted sound fiscal, business, or medical practices, and result in an unnecessary cost or in reimbursement for services that are not medically necessary or that fail to meet professionally recognized standards for health care. Unsubstantiated payment for services, sometimes intentional, exploiting gaps in policy (e. g. Misusing codes on a claim).

EXAMPLES OF HEALTHCAR E FRAUD • Misrepresentation of the type or level of service provider • • Misrepresentation of the individual rendering service • Billing for services that have not been properly documented • Billing for items and services that are not medically necessary • Seeking payment or reimbursement for services rendered for procedures that are integral to other procedures performed on the same date of service (unbundling) • Seeking increased payment or reimbursement for services that are correctly billed at a lower rate (upcoding) • Misusing codes on a claim Billing for items and services that have not been rendered

Member driven Fraud, Waste and Abuse • “Identity Theft” - using ID card that belongs to someone else. EXAMPLES OF HEALTHCAR E FRAUD • “Doctor shopping” - visiting several doctors to obtain multiple prescriptions. • Concealing information about past medical history. • Adding a non-eligible family member to the policy. • Failing to remove someone from a policy when that person is no longer eligible. • Filing false claims for services or medications not received. • Forging or altering bills or receipts.

*Willis Limited : Healthcare Fraud, Waste and Abuse

Non-existent diagnosis!

Repeated Billing

THERE ARE WAYS TO ROOT OUT WASTE…. . but it requires a continuous cycle of intervention and standardization. Most payer organizations live in a type of limbo — let’s call it the “wasteland. ”

Payers need to develop robust mechanism by deploying latest technology , experience and expertise to DETECT DIAGNOS E REDUCE PREVENT

DETECT • Identify outliers in claims and claim lines • Benchmark procedure codes and costs against comparable peer claims • Identify attributes that most likely contribute to anomalies (e. g. , provider specialty, location, type of service, etc. ) DIAGNOSE • Absent or inadequate medical policies and gaps in adherence • Product or network design inefficiency • Inadequate claims edits or adjudication processes • Data quality or timeliness issues REDUCE • Incorrect approvals and denials, resulting in a higher volume of adjustments and appeals • Delays in claims pends/holds • Higher penalties and increased audits PREVENT • Make informed decisions around whether the expected medical cost savings outweigh the potential administrative costs of incorrectly identifying an anomaly.

Not all FWA cases are intentional. • A good number of these are mere inefficiencies and unintentional errors that unfortunately, lead to duplicate tests and incorrect billings. Reason : Dense, complex billing and reimbursement system that health providers must navigate. • The latest version of the medical coding guide is the ICD-10. Implemented in 2014, the ICD-10 was specifically designed to deter fraud and discourage abuse with the increased level of detail contained in every code. But the staggering number of codes (67, 000 diagnosis codes and 87, 000 procedure codes), plus the length and structure of each (seven alphanumeric characters in length), has also led to many coding errors. • Inexperience with the restructured codes, the lack of training in coding and overworked staff are recognized factors that could contribute to the mistakes in reimbursement claims that insurance carriers have to put up with.

* Simple billing and coding errors that lead to one-off overpayments are one thing; but manipulating the system with the intent to commit fraud and profit is another. *Inspector General (OIG) and Department of Justice (Do. J)

The objective SHOULD BE to identify potentially fraudulent or erroneous transactions BEFORE payment is made. This makes for a better practice over the pay-and-chase model that not only creates tension with the medical service provider (if it’s a legitimate mistake), but also doesn’t guarantee the return of overpayments.

For health insurance players invested in the right big data technologies, rooting out FWA has saved time and money. Sorting through all the information available to insurers is not a simple matter. The data is deep and the data is wide. Millions, even billions of healthcare and claims records, with each record having up to 300 attributes.

• Explore deep into the data. • Establishing patterns and compare against benchmark results. • Layered approach • Use advanced analytics NOT ALL CLAIMS THAT ARE IDENTIFIED AS OUTLIERS ARE CASES OF FWA.

Insurers are on the cusp of a new era of claims management, one supported by rapid technological advancements that provide unprecedented visibility into the claims process.

Insurers should start with an “EVERYTHING IS POSSIBLE” attitude to unleash truly transformative ideas.

WHAT’S THE FUSS ABOUT AI? Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Processes include • LEARNING (the acquisition of information and rules for using the information) • REASONING (using rules to reach approximate or definite conclusions) • SELF-CORRECTION

AI IS COMPLEMENT, NOT A SUBSTITUTE Human review remains the key element in determining whether someone intended to be deceitful. Proving fraud rests heavily on proving intentional wrongdoing. A healthcare provider could simply be participating in a study, for example, that means the patterns in their treatments are different from the typical behavior of other physicians in their field. That could be flagged by an AI algorithm because it is different, but it would require human review to understand the conditions of why that difference is present. Eventually, algorithms could be trained to handle even some of that work. But the trick will be figuring out what other data sets to point the AI to so it can learn. That’s where human expertise will be key to progress.

UPCODING • The problem: A simple procedure might be billed as much more complex, and costly, by providers looking to pad the bill. This is known as “upcoding, ” because a different code is used on billing forms for the more expensive service. • CPT 80076 – Hepatic function panel - AED 321 • Unbundled Services Total = AED 451 The AI solution: Here, AI could use anomaly detection to understand what the typical treatment is for a specific condition and detect any deviations by a provider from their peer group. It then flags the anomaly as potential fraud. Bilirubin, total (82247) - AED 44 Albumin (82040) - AED 85 Bilirubin, direct (82248) - AED 50 Phosphatase, alkaline (84075) - AED 68 Protein, total (84155) - AED 68 Transferase, alanine amino (ALT) (84460) - AED 68 Transferase, aspartate amino (AST) (84450) - AED 68

CASE STUDY • A local insurance company in the UAE with a medical portfolio of 500, 000 insured members • 3, 600, 000+ claims from medical service providers annually • Medical Claims Team : 150+ people (not including customer contact center) • Findings • Only claims above a certain threshold are evaluated – significant number of claims ‘slip through the crack’ • Average of 2 out of 10 claims are incorrectly coded – upcoding, unbundling etc *IRIS handles claims processing for over 400, 000 members with a team of 15 Doctors

REDEFINE THE ADJUDICATORS ROLE AI-enabled IT platform should drive • Benefit limit verifications • Co-insurance/ excess allocation to a claim • Suspicious claims alerts The conventional approach to claims management based on an inflexible rule book has been made obsolete by intelligent algorithms that learn from historical cases and continuously evolve. Such a system can systematically identify and correct errors while avoiding unnecessary or ineffective interventions.

WHO WE ARE 2018 Award-winning third party administration company based in the United Arab Emirates providing professional medical claims management solutions to Insurer, Reinsurers and Self-Insured organizations. We handle over 400, 000 lives through 3 offices based in Dubai, Abu Dhabi and Muscat, Oman.

Conforming to the highest standards of Quality Management and Information Security, our role is to protect the interest of the Risk carrier through innovative service delivery solutions.

AI IN ACTION

Automated Benefit Allocation

Role of the Medical Adjudicator : Assess Medical Necessity based on the patient’s previous treatment information and current claim. ALL BENEFIT LIMITS AND PATIENT SHARE CALCULATED BY THE SYSTEM

FRAUD PREVENTION STARTS AT POS

THANK YOU FOR MORE INFORMATION EMAIL : A. NAIR@IRIS. HEALTH CARE WWW. IRIS. HEALTHCA RE