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NORMAN J CLEMENT RPH., DDS, NORMAN L. CLEMENT PHARM-TECH, MALACHI F. MACKANDAL PHARMD, BELINDA BROWN-PARKER, IN THE SPIRIT OF JOSEPH SOLVO ESQ., INC.T. SPIRIT OF REV. IN THE SPIRIT OF WALTER R. CLEMENT BS., MS, MBA. HARVEY JENKINS MD, PH.D., IN THE SPIRIT OF C.T. VIVIAN, JELANI ZIMBABWE CLEMENT, BS., MBA., IN THE SPIRIT OF THE HON. PATRICE LUMUMBA, IN THE SPIRIT OF ERLIN CLEMENT SR., EVELYN J. CLEMENT, WALTER F. WRENN III., MD., JULIE KILLINGSWORTH, RENEE BLARE, RPH, DR. TERENCE SASAKI, MD LESLY POMPY MD., CHRISTOPHER RUSSO, MD., NANCY SEEFELDT, WILLIE GUINYARD BS., JOSEPH WEBSTER MD., MBA, BEVERLY C. PRINCE MD., FACS., NEIL ARNAND, MD., RICHARD KAUL, MD., IN THE SPIRIT OF LEROY BAYLOR, JAY K. JOSHI MD., MBA,AISHA GARDNER, ADRIENNE EDMUNDSON, ESTER HYATT PH.D., WALTER L. SMITH BS., IN THE SPIRIT OF BRAHM FISHER ESQ., MICHELE ALEXANDER MD., CUDJOE WILDING BS, MARTIN NJOKU, BS., RPH., IN THE SPIRIT OF DEBRA LYNN SHEPHERD, BERES E. MUSCHETT, STRATEGIC ADVISORS

Praying for Pope Francis: A Beacon of Hope in the Age of Artificial Intelligence and the Tyranny of Qlarant Artificial !!STUPIDITY!!

Pope Francis’s advocacy for ethical AI contrasts sharply with the actions of companies like Qlarant, which utilize flawed algorithms that disproportionately target Black and Brown healthcare professionals. 

The text highlights the Pope’s concerns about AI, particularly lethal autonomous weapons and the need for human control over AI systems, while he battles pneumonia. 

Qlarant’s AI, intended to combat healthcare fraud, is criticized for its reliance on correlation rather than causation, leading to the unjust persecution of doctors and pharmacists serving marginalized communities. 

This situation is likened to historical persecutions like those of Giordano Bruno and Galileo, who faced condemnation for challenging established norms. 

The author urges accountability for Qlarant and calls for AI to be used to uplift, not oppress, emphasizing the importance of human dignity in the age of AI. 

The central message underscores that technology must always serve humanity, guided by ethical responsibility.

As the world watches with bated breath, Pope Francis, the spiritual leader of over a billion Catholics, battles pneumonia in both lungs.

At 88 years old, his health is a matter of global concern, not only for his followers but for anyone who values his profound moral leadership in an era increasingly dominated by technology.

The Pope’s recent messages on artificial intelligence (AI) have been a clarion call for ethical responsibility, urging humanity to ensure that machines never decide to take human life. Yet, as we pray for his recovery, we must also confront the darker side of AI, exemplified by companies like Qlarant, whose flawed algorithms are perpetuating a modern-day inquisition against Black and Brown healthcare professionals in the United States.

In July 2024, Pope Francis addressed global leaders at an AI ethics conference in Hiroshima, Japan, a city synonymous with the catastrophic consequences of unchecked technological advancement. The Pope’s message was clear.  “No machine should ever choose to take the life of a human being.”   He called for a ban on lethal autonomous weapons and emphasized the need for human control over AI systems. His words were a reminder that technology must serve humanity, not the other way around.

The Pope’s vision for AI is rooted in the principles of transparency, accountability, and respect for human dignity. He has been a vocal advocate for an international treaty to regulate AI, ensuring that its development and use align with ethical standards. His 2024 peace message, dedicated to the theme of artificial intelligence, urged global leaders to prioritize human control over AI decisions, warning that human dignity itself depends on it.

Yet, while the Pope champions ethical AI, companies like Qlarant are using AI in ways that undermine the very principles he espouses. Qlarant’s algorithms, marketed as tools to combat healthcare fraud, are instead perpetuating a system of tyranny that disproportionately targets Black and Brown healthcare professionals, echoing the historical persecution of thinkers like Giordano Bruno and Galileo Galilei.

Qlarant: A Modern-Day Inquisition

Qlarant’s AI-driven models, such as the NBI MEDIC program, claim to predict opioid overdoses and identify fraudulent healthcare practices.

AI (algt*) =AS

However, a closer examination reveals a system riddled with mathematical flaws and ethical oversights. Qlarant’s algorithms rely on correlation rather than causation, mistaking patterns like prescription volume or patient travel distance for indicators of fraud. This approach reduces complex medical practices to simplistic data points, ignoring the nuanced realities of healthcare.

For example, Qlarant’s use of Morphine Milligram Equivalents (MME) to assess risk fails to account for the physiological differences between opioid-naïve patients and long-term users. This one-size-fits-all approach disproportionately flags doctors serving communities with high pain management needs, many of whom are Black and Brown healthcare professionals. These doctors and pharmacists, like Galileo and Bruno before them, are being persecuted not for wrongdoing, but for challenging the status quo.

Qlarant’s scoring system, which labels doctors as high-risk based on prescription volume, is reminiscent of the Inquisition’s condemnation of Galileo for his support of the Copernican model. Just as Galileo was punished for daring to question the geocentric view of the universe, Black and Brown doctors and pharmacists are being penalized for serving communities that mainstream healthcare has often neglected. Qlarant’s algorithms, like the Inquisition’s tribunals, are tools of oppression, masquerading as instruments of justice.

The Persecution of Giordano Bruno and Galileo: A Cautionary Tale

Giordano Bruno, a 16th-century Italian philosopher, was burned at the stake in 1600 for heresy. His crime? Embracing the Copernican model and challenging the dogmatic views of his time. Galileo Galilei, another supporter of the Copernican model, faced the Inquisition and was sentenced to house arrest. Both men were persecuted for their ideas, not for any tangible harm they caused.

Qlarant’s AI models are perpetuating a similar form of persecution. By reducing doctors to data points and ignoring the context of their practices, Qlarant is creating a system where anomaly equals suspicion.  Doctors and pharmacists who serve marginalized communities are being labeled as high-risk, not because they are committing fraud, but because their practices deviate from the norm. This is a modern-day witch hunt, driven by artificial intelligence algorithms that lack the moral clarity Pope Francis has called for.

A Call for Accountability

As we pray for Pope Francis’s recovery, we must also heed his call for ethical AI. Qlarant’s misuse of artificial intelligence is a stark reminder of what happens when technology is divorced from moral responsibility. The company’s algorithms, like the Inquisition’s tribunals, are tools of oppression, targeting those who dare to challenge the status quo.

Pope Francis has reminded us that human dignity must always come first.  In the case of Qlarant, this means holding the company accountable for its flawed methodologies and ensuring that its algorithms do not perpetuate systemic racism and injustice. Just as Galileo and Bruno were eventually vindicated, so too must the Black and Brown healthcare professionals targeted by Qlarant’s AI be given justice.

In the end, the story of Qlarant is a cautionary tale. It reminds us that technology, no matter how advanced, must always serve humanity. As Pope Francis continues to fight for his health, let us also fight for a world where AI is used to uplift, not oppress. Let us ensure that the lessons of history—of Bruno, Galileo, and the countless others who have been persecuted for their beliefs—are not forgotten.

For in the words of Pope Francis, “Human dignity itself depends on it.”

Let us pray for Pope Francis’s swift recovery and for a future where artificial intelligence serves as a force for good, guided by the moral clarity and ethical integrity he so passionately advocates.

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REFERENCES:

The first source is a court transcript from a 2017 trial, featuring the testimony of Kevin McCash, a data analytics manager at Health Integrity, LLC. McCash explains his company’s work analyzing Medicare Part D data to identify prescribers at high risk for fraud, waste, and abuse related to controlled substances. 

The second source is a white paper from the U.S. Attorney’s office discussing challenges in opioid cases. It highlights the importance of data analysis to identify vulnerabilities that lead to abuse and overutilization. 

He describes a doctor analysis project that uses 17 factors to calculate a risk score. The testimony focuses on how this data was applied to the defendant doctors, Ruan and Couch, and their high-risk scores. 

The white paper promotes Qlarant (formerly Health Integrity), as a company with expertise in data analytics, risk scoring, and fraud detection in healthcare. 

It suggests Qlarant’s unique qualifications and potential to assist U.S. Attorneys’ Offices in combating the opioid epidemic. HOWEVER!!!

#1. TESTIMONY OF KEVIN MCCASH: UNITED STATES DISTRICT COURT SOTHERN DISTRICT OF ALABAMA

#2. QLARANT: US ATTORNEY-WHITE PAPER

#3. Qlarant: In a War Where Evidence is Ammunition the Smoking Gun Can Be Buried Data

#4. Qlarant: Define and Improve Quality Detect Unforeseen Risk. Find Clarity in A Sea of Data.

Opioid Crisis: Timeline, Key Players, and Data Analysis

Timeline of Events

• Prior to 2009: Qlarant (formerly Health Integrity) gains expertise in human services and healthcare.
• 2009: Qlarant becomes the sole contractor providing surveillance and detection of prescription drug abuse to the Centers for Medicare & Medicaid Services (CMS) nationally.
• December 2014 – April 2016: Health Integrity runs monthly updates on their doctor analysis algorithm. Dr. John Patrick Couch scores 1,000 out of 1,000 seventeen consecutive times.
• January 2015 – December 2015: Health Integrity’s data analysis project, using a rolling 12-month time frame, analyzes data for this period.
• December 2014 – August 2015: Dr. Xiulu Ruan scores 996 eight times and 992 one time.
• September 2015 – April 2016: Dr. Xiulu Ruan scores 1,000 eight times.
• May 20, 2015: Implied end date of prescribing activity for Dr. Ruan and Dr. Couch (though their data is still analyzed for the full year).
• September 2016: The U.S. Department of Justice publishes “Addressing the Heroin and Opioid Crisis.”
• January 26, 2017: Kevin McCash testifies in the trial of John Patrick Couch and Xiulu Ruan in the Southern District of Alabama, regarding the doctor analysis project conducted by Health Integrity.
• April 2018: Chief Richard Biehl of the Dayton, Ohio Police Department writes a report on the opioid epidemic for the U.S. Department of Justice.
• 2019: Qlarant continues to work with law enforcement to combat the opioid crisis, using data analysis to identify trends and targets.
• Ongoing: Qlarant continues to refine algorithms for statistical significance in data analytics.

Cast of Characters

• Kevin McCash, Ph.D.: Data Analytics Manager at Health Integrity, LLC (later Qlarant Integrity Solutions, LLC). His expertise is in applied physics. He is responsible for the doctor analysis project, using Medicare Part D data to identify prescribers at high risk for fraud, waste, and abuse related to controlled substances. He testifies about the data analysis and scores of Drs. Ruan and Couch.
• John Patrick Couch, M.D.: Defendant in the trial. Physician identified as a high-risk prescriber by Health Integrity’s data analysis project.
• Xiulu Ruan, M.D.: Defendant in the trial. Physician identified as a high-risk prescriber by Health Integrity’s data analysis project.
• Callie V.S. Granade: United States District Judge presiding over the trial of John Patrick Couch and Xiulu Ruan.
• Christopher Bodnar: Prosecuting attorney for the U.S. Government in the case against Drs. Couch and Ruan.
• Deborah A. Griffin: Prosecuting attorney for the U.S. Government in the case against Drs. Couch and Ruan.
• Lynn Dekle: Law Clerk
• Roy Isbell, CCR, RDR, CRR: Official Court Reporter for the trial of John Patrick Couch and Xiulu Ruan.
• Mr. Willson: Attorney representing one of the defendants (presumably Couch, but unclear).
• Mr. Knizley: Attorney representing one of the defendants (presumably Ruan, but unclear).
• Richard Biehl: Chief of the Dayton, Ohio Police Department. Author of a report on the opioid epidemic for the U.S. Department of Justice.
• Sandy Love: President of Qlarant Integrity Solutions. Contact person for Qlarant for potential collaborations with USAO offices.
• John Doe: (Composite character) Hypothetical “pill mill” doctor used as an example in the Qlarant document to illustrate how data analysis can lead to convictions.

We hope this comprehensive timeline and character list are helpful!

Data-Driven Investigations into Opioid Prescribing: A Study Guide

I. Study Guide Outline

This study guide is designed to help you review and understand the key concepts presented in the provided documents related to data analysis, the opioid crisis, and the role of companies like Qlarant in assisting law enforcement.

A. Key Players and Organizations

• United States Attorney’s Office (USAO)
• Centers for Medicare & Medicaid Services (CMS)
• Health Integrity, LLC (now Qlarant Integrity Solutions, LLC)
• Qlarant
• Drug Enforcement Administration (DEA)
• Health and Human Services Office of Inspector General (HHS/OIG)

B. Key Concepts

• Data Analysis in Identifying Fraud, Waste, and Abuse (FWA):How data analysis is used to identify trends and targets in prescription drug data.
• The use of algorithms to identify prescribers at high risk for FWA.
• The limitations of data analysis in proving actual fraud or abuse.
• The NBI MEDIC Program:What the National Benefit Integrity Medicare Drug Integrity Contractor (NBI MEDIC) contract is.
• The purpose of the NBI MEDIC program to detect and prevent FWA in Medicare Part C and D.
• Key Risk Indicators and Algorithms:Understanding the factors used in the doctor analysis project (e.g., number of Schedule II prescriptions, travel distances, etc.).
• How these factors are used to calculate a risk score.
• The scoring system (0-1000) and its interpretation.
• Prescription Drug Monitoring Programs (PDMPs):The role of PDMPs in identifying opioid prescribing trends and inappropriate prescriber behavior.
• Data Team Composition and Best Practices:The ideal composition of a data team (e.g., former members of agencies like HHS-OIG and DEA, physicians, pharmacists, data analysts).
• Best practices for data team algorithms, including collaboration with law enforcement.
• Qlarant:Understanding Qlarant’s role as a contractor for CMS and its expertise in data analysis, risk scoring, and FWA detection.
• How Qlarant collaborates with law enforcement entities like the FBI and DEA.
• The data sources Qlarant utilizes (e.g., claims data, PDMP data, law enforcement records, etc.).
• Challenges and Limitations:The importance of not relying solely on law enforcement to solve the opioid epidemic and the need for partnerships.
• The need for statistically significant outcomes to be pursued.

II. Quiz (Short Answer)

Answer each question in 2-3 sentences.

1. What is the primary purpose of the Doctor Analysis Project, as described by Kevin McCash?
2. Explain what Medicare Part D covers and why it is relevant to the opioid crisis.
3. Describe two of the 17 factors considered in the Doctor Analysis Project and explain how they are measured.
4. What does a score of 1,000 indicate in the context of the Doctor Analysis Project?
5. According to the Data Science & Technology Reports, how are US Attorney’s Offices combatting the Opioid crisis?
6. Explain the role of a Prescription Drug Monitoring Program (PDMP) in addressing the opioid crisis.
7. What are the key components of an effective data team for analyzing prescription drug data, according to the Data Science & Technology Reports?
8. How did Qlarant identify Dr. Doe as a “pill mill” doctor in the provided case example?
9. What data sources does Qlarant utilize to detect fraud, waste, and abuse in the Medicare and Medicaid programs, per the US-Attorney-Office-White-Paper.pdf?
10. Explain two examples of analytical results presented in the US-Attorney-Office-White-Paper.pdf that can be used as key indicators of opioid misuse.

III. Quiz Answer Key

1. The Doctor Analysis Project is a data analysis algorithm that runs against prescription information to identify prescribers who are prescribing schedule II, III, and IV controlled substances to Medicare beneficiaries and identifying those that are at high risk for fraud, waste, and abuse in doing so. It aims to flag potentially problematic prescribing patterns for further investigation.
2. Medicare Part D is the prescription drug benefit for Medicare recipients. It is relevant to the opioid crisis because it involves the tracking and analysis of prescription data, which can be used to identify patterns of abuse and potential fraud.
3. Two factors are the number of Schedule II controlled substance beneficiaries and the distance beneficiaries travel. The number of Schedule II beneficiaries counts how many patients receive these drugs from a specific prescriber, while travel distance measures how far beneficiaries travel to see a prescriber, which can indicate “pill mill” behavior.
4. A score of 1,000 is the highest possible score and indicates that the prescriber’s prescribing patterns are the most abnormal compared to other doctors. It suggests a higher risk for potential fraud, waste, and abuse.
5. US Attorney’s Offices are combatting the opioid crisis by assigning special coordinators and full-time opioid abuse unit prosecutors. They are working closely with law enforcement and also using data and data analysis to identify trends and targets.
6. PDMPs are used to identify opioid analgesic prescribing trends and apply risk indicators for inappropriate prescriber behavior. They help to track prescriptions across different providers, allowing for the detection of doctor shopping and excessive prescribing.
7. An effective data team should consist of former members of agencies such as HHS-OIG and DEA, subject matter experts (physicians and clinical pharmacists), and data analysts. They should tailor algorithms that are sophisticated yet easy-to-use for law enforcement.
8. Qlarant identified Dr. Doe as a “pill mill” doctor by using prescribing data and medical claims. The data showed a high incidence of patients traveling excessive distances to see him, indicating potential abuse of prescription privileges.
9. Qlarant utilizes claims, encounters, pharmacy invoices, beneficiary and provider enrollment files, state licensing-board information, property records, Google maps, ownership/asset and financial filings, and court records. They also include other custom data available from or on behalf of the client.
10. Two examples of analytical results that can be used as key indicators of opioid misuse are patients prescribed more than 100 morphine milligram equivalents per day and patients who have obtained prescriptions from six or more sources during a given prior period. These indicate potentially dangerous levels of opioid consumption and doctor shopping.

IV. Essay Questions

1. Discuss the ethical considerations surrounding the use of data analytics in identifying potential fraud, waste, and abuse in opioid prescribing. How can these tools be used responsibly to avoid unfairly targeting legitimate medical practices?
2. Analyze the role of partnerships between data analysis companies like Qlarant and law enforcement agencies in combating the opioid crisis. What are the benefits and challenges of these collaborations?
3. Evaluate the effectiveness of the Doctor Analysis Project described by Kevin McCash. What are its strengths and weaknesses, and how could it be improved?
4. Compare and contrast the different data sources mentioned in the provided documents that can be used to identify opioid misuse and fraud. What are the advantages and limitations of each?
5. Discuss the challenges in differentiating between legitimate high-volume prescribers and those engaged in fraudulent or abusive practices based solely on data analysis. What additional information or investigation is needed to make such determinations?

V. Glossary of Key Terms

• Algorithm: A step-by-step procedure or formula for solving a problem, especially one that is computer-implemented.
• Anomaly Detection: Identifying data points, items, or events that do not conform to expected patterns, indicating potential abnormalities or outliers.
• Controlled Substances: Drugs or chemicals whose manufacture, possession, or use is regulated by a government. Schedules II, III, and IV refer to different categories of controlled substances with varying restrictions.
• Data Analytics: The process of examining data sets in order to draw conclusions about the information they contain, often involving specialized systems and software.
• DEA (Drug Enforcement Administration): A United States federal law enforcement agency under the Department of Justice tasked with combating drug trafficking and distribution.
• FBI (Federal Bureau of Investigation): The domestic intelligence and security service of the United States, which also serves as its principal federal law enforcement agency.
• Fraud, Waste, and Abuse (FWA): Dishonest or illegal activities, misuse of resources, and improper practices in healthcare and other sectors.
• HHS/OIG (Health and Human Services Office of Inspector General): A United States government agency whose mission is to protect the integrity of the Department of Health and Human Services programs and the health and welfare of the beneficiaries of those programs.
• Medicare Part D: The prescription drug benefit program under Medicare.
• Morphine Milligram Equivalent (MME): A standardized measure used to compare the potency of different opioid medications.
• NBI MEDIC (National Benefit Integrity Medicare Drug Integrity Contractor): A government contract focused on detecting and preventing fraud, waste, and abuse in Medicare prescription drug programs.
• Opioid Potentiators: Substances that can increase the effects of opioids.
• Pill Mill: A term for a healthcare provider who prescribes medications, usually controlled substances, without sufficient medical examination or for non-legitimate medical purposes.
• Predictive Modeling: Using statistical techniques to predict future outcomes based on historical data.
• Prescription Drug Monitoring Program (PDMP): A state-level electronic database that tracks the prescribing and dispensing of controlled substances to patients.
• Qlarant: A company that provides data science, technology, and quality improvement solutions, particularly in the areas of healthcare fraud, waste, and abuse.
• Risk Score: A numerical value assigned to an individual or entity based on the likelihood of a particular outcome, such as engaging in fraudulent behavior.
• USAO (United States Attorney’s Office): The office responsible for representing the federal government in legal matters within a specific judicial district.

Opioid Fraud Prosecution: A Data-Driven Approach

Here’s a consolidated briefing document summarizing the key themes and information from the provided sources:

Briefing Document: Data Analysis in Opioid Fraud Prosecution

Executive Summary:

This document synthesizes information from court transcripts and materials related to Qlarant (formerly Health Integrity) and its role in leveraging data analysis to combat opioid-related fraud, waste, and abuse.

The central themes revolve around using data-driven approaches to identify prescribers and entities engaged in questionable or illegal practices, ultimately supporting law enforcement in investigations and prosecutions.

Qlarant’s expertise lies in its ability to analyze large datasets, identify anomalies, and provide actionable insights to government agencies and law enforcement.

Key Themes and Ideas:

• Data Analysis as a Tool for Identifying Fraud, Waste, and Abuse: The core concept is that patterns in prescription data, when analyzed using specialized algorithms, can highlight potential fraud, waste, and abuse related to controlled substances. This goes beyond simple monitoring; it’s about proactively identifying high-risk individuals and practices.
• “Data analytics is a general term that means the manipulation and analysis of data in order to uncover facts and statistics to make conclusions.” (McCash Testimony)
• Qlarant’s Role as a Government Contractor: Qlarant (formerly Health Integrity) is a key player in this space, acting as a government contractor that holds the NBI MEDIC contract. This contract focuses on identifying and preventing fraud, waste, and abuse in Medicare Part C and Part D (prescription drug coverage).
• “Health Integrity is a government contractor that holds the NBI MEDIC contract, which is the National Benefit Integrity Medicare Drug Integrity contract.” (McCash Testimony)

A CASE OF LEGAL ALGORITHMIC DATA GENOCIDE

• “Qlarant (formerly Health Integrity) has been the only contractor providing surveillance and detection of prescription drug abuse to the Centers for Medicare & Medicaid Services (CMS) nationally since 2009.” (US Attorney Office White Paper)
• The Doctor Analysis Project: Qlarant employs a “doctor analysis project,” an algorithm that analyzes prescription data to identify prescribers who are at high risk for fraud, waste, and abuse related to Schedule II, III, and IV controlled substances. The algorithm uses a variety of factors to calculate a risk score for each doctor.
• Key Risk Factors and Metrics: Several specific risk factors are considered in the analysis, including:
• Number of beneficiaries receiving Schedule II, III, and IV controlled substances.
• 30-day equivalent prescription drug event records for each schedule.
• Quantity of controlled substances dispensed.
• Number of beneficiaries exceeding travel thresholds (distance between patient address and prescriber address).
• Prescribers with no Part B claims (indicating they may not be conducting normal office visits).
• Number of beneficiaries with a drug abuse/misuse diagnosis during an ER visit.
• “This is the number of schedule II controlled substance beneficiaries.” (McCash Testimony)
• “In this particular factor we look at the distance listed between the listed address of a beneficiary and the listed practice address of a prescriber in order to see if that beneficiary had to travel over a specific distance in order to see the particular prescriber.” (McCash Testimony)

ANAND TARGETED AND ARRESTED SEPTEMBER 25, 2019

• “This particular factor looks into the Medicare part B claims…if it sees that there are no claims at all for that particular physician under part B, this indicates that the physician has not billed Medicare part B as a normal doctor visit and is simply writing prescriptions in large quantities.” (McCash Testimony)
• Risk Scoring: The analysis results in a risk score, ranging from 0 to 1,000, with higher scores indicating a greater risk of fraud, waste, or abuse.
• “Score ranges from zero to 1,000.” (McCash Testimony)
• “Those individuals scoring 1,000 are most at risk for fraud, waste, and abuse.” (McCash Testimony)
• Limitations of Data Analysis: It’s crucial to understand that the data analysis identifies potential risks, not definitive proof of wrongdoing. The testimony emphasizes that a high score does not automatically mean someone is committing fraud. It’s simply a flag for further investigation.
• “Looking at the statistics, you don’t know if someone actually is committing fraud, waste, and abuse, do you?” (McCash Testimony)
• “Nothing about the data can tell you what actually happened in the practice or how patients were treated or what they had — what their morbidities were; is that true?” (McCash Testimony)
• “But it does not mean that they necessarily are committing fraud, waste, and abuse?” (McCash Testimony)
• The Importance of a Multi-Disciplinary Team: Effective data analysis for opioid fraud requires a team with diverse expertise. This includes data scientists, statisticians, physicians, clinical pharmacists, and individuals with experience from agencies like the HHS-OIG and DEA. The collaboration with law enforcement is also critical.
• “Ideally, the data resource team consists of former members of agencies such as the Office of Inspector General (HHS-OIG) and Drug Enforcement Administration (DEA); subject matter experts who are physicians and clinical pharmacists; as well data analysts who can tailor algorithms…Through these types of analyses, specialized data teams partnering with law enforcement have investigated thousands of providers…” (Data Science & Technology Reports)
• Use of Data in Court: Data analysis and the insights derived from it can be used as evidence in court. Qlarant provides expert testimony in trials, supporting the prosecution’s case. The “Dr. John Doe” example illustrates how data analysis identifying a “pill mill” doctor led to a conviction.
• “Analysts and clinicians from the data team testified in the trial, which lead to a conviction on all counts.” (Data Science & Technology Reports)
• Proactive vs. Reactive Approaches: The documents highlight that the data analysis is often proactive, meaning it’s used to identify potential problems before they become widespread or result in significant harm.
• “Dr. John Doe was identified in a proactive data model for excessive distribution of opioids.” (Data Science & Technology Reports)
• Data Sources: A variety of data sources are used in the analysis including: Insurer claims, Prescription Drug Monitoring data, Hospital data, Law enforcement intake records, Incarceration data, State Managed Care data.

Implications:

These sources underscore the growing importance of data analysis in combating the opioid crisis. By identifying patterns and anomalies in prescription data, government agencies and law enforcement can more effectively target their investigations and prosecutions, ultimately reducing fraud, waste, and abuse related to controlled substances. The role of companies like Qlarant, with their specialized expertise and access to data, is likely to continue to expand in this area.

Data Analysis: Combating Opioid Misuse and Fraud

What is the main purpose of data analysis in combating the opioid crisis, according to these documents?

Data analysis is primarily used to identify trends and targets related to opioid misuse, abuse, and fraud. This includes identifying prescribers who may be over-prescribing, detecting patients who are obtaining prescriptions from multiple sources or traveling long distances, and uncovering potential fraud, waste, and abuse within Medicare and other healthcare programs. The aim is to provide law enforcement with solid evidence for investigation and prosecution.

What are the 17 factors used by Health Integrity (now Qlarant Integrity Solutions) to analyze prescribers in the Medicare Part D program?

The 17 factors, as described in the testimony of Kevin McCash, are used to assess the risk associated with prescribers of Schedule II, III, and IV controlled substances. These factors include:

1. Number of Schedule II controlled substance beneficiaries.
2. Number of Schedule III controlled substance beneficiaries.
3. Number of Schedule IV controlled substance beneficiaries.
4. 30-day equivalent prescription drug event records for Schedule II controlled substances.
5. 30-day equivalent prescription drug event records for Schedule III and IV controlled substances.
6. 30-day equivalent prescription drug event records for new fills for Schedule III and IV controlled substances.
7. Quantity of Schedule II controlled substances dispensed to beneficiaries.
8. Average monthly quantity of Schedule II controlled substances dispensed.
9. Quantity of Schedule III controlled substances dispensed to beneficiaries.
10. Average monthly quantity of Schedule III controlled substances dispensed.
11. Quantity of Schedule IV controlled substances dispensed to beneficiaries.
12. Average monthly quantity of Schedule IV controlled substances dispensed.
13. Number of beneficiaries exceeding travel threshold (30 miles urban, 120 miles rural).
14. Percentage of beneficiaries exceeding travel threshold.
15. Prescribers with no Part B claims (indicating they are not billing for office visits).
16. Count of Medicare beneficiaries with a drug abuse or misuse diagnosis during an ER visit.
17. Percentage of Medicare beneficiaries with a drug abuse or misuse diagnosis during an ER visit.

How are these 17 factors used to determine a risk score for prescribers?

The factors are analyzed using an algorithm that generates a score from 0 to 1,000. A score closer to 1,000 indicates a higher risk of fraud, waste, and abuse. The algorithm is updated monthly, using a rolling 12-month time frame. Prescribers are initially assessed for being in the top 5% (95th percentile) based on all 17 factors, then those above the 75th percentile based on the first several quantitative factors are deemed high risk.

What is Qlarant’s role in combating fraud, waste, and abuse related to opioids?

Qlarant is a company that specializes in data analysis, risk scoring, and FWA detection, particularly in healthcare. They have been the CMS contractor responsible for the NBI MEDIC program since 2009, detecting and preventing FWA in Medicare Part C and Part D. They develop algorithms and key risk indicators, analyze data from various sources (claims data, PDMP data, etc.), and partner with law enforcement agencies like the DEA, FBI, and HHS-OIG. Qlarant provides expert testimony in court cases.

What types of data sources are valuable for identifying opioid-related fraud and abuse?

Several data sources are crucial:

• Insurer claims (including Medicaid data)
• Prescription Drug Monitoring Program (PDMP) data
• Hospital data
• Law enforcement intake records
• Incarceration data
• State Managed Care data
• DEA data on orders and fulfillment to distributors
• All-Payer Claims Databases

What are some examples of analytical results used as key indicators of opioid misuse and abuse?

Examples include:

• Patients prescribed over 100 morphine milligram equivalents per day.
• Patients with prescriptions from six or more sources in a given period.
• Patients prescribed over 40 morphine milligram equivalents of methadone daily.
• Patients prescribed opioids for more than 90 consecutive days.
• Patients concurrently prescribed benzodiazepines and opioids.
• Identifying patients who are traveling long distances to obtain narcotics.

How does Qlarant differentiate itself from other data analysis vendors?

Qlarant emphasizes several key differentiators:

• Algorithms developed in collaboration with law enforcement.
• Rigorous supervisory approval of data analysis plans and statistical modeling.
• Use of client data sources combined with third-party data for a complete data inventory.
• Expertise in predictive modeling and analytics.
• Experience collaborating with federal and state law enforcement entities.
• Subject matter expertise of clinical pharmacists and investigators with real-world experience.

Besides data analysis, what other strategies are important in addressing the opioid crisis?

The sources emphasize that partnerships are key. This includes collaboration between federal, state, and local law enforcement, USAO offices, State Attorneys’ General offices, DEA task forces, and state government agencies. Community education is also crucial. A multi-faceted approach is needed as law enforcement cannot solve the crisis alone.