AMERICA NEEDS A MODERN ÉVARISTE GALOIS, TO CONFRONT GOVERNMENT AI TYRANNY: (PART 1 OF 2)

from youarewithinthenorms.com

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., 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., M.B.A., 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

These sources collectively explore the complex landscape of technology and its impact on healthcare and governance, particularly focusing on the opioid crisis and the implications of AI-driven systems.

One source examines the historical context of challenging authority through intellect, suggesting a modern “Galois” is needed to combat potential AI tyranny by governments that threaten individual freedoms and privacy through predictive analytics and data surveillance.

This lengthy document critiques several systems and individuals involved in the opioid crisis and the broader landscape of algorithmic governance. It focuses heavily on NarxCare software, arguing it’s an unvalidated medical device illegally influencing prescribing decisions and potentially causing harm to pain patients. 

 “Don’t weep. I need all my courage to die at twenty.” —Évariste Galois (1832)

These were the last recorded words of Évariste Galois, scribbled in a letter to his brother on the eve of a duel he would not survive. At just twenty years old, Galois knew he was being swallowed by a system terrified of his mind. He had dared to think differently, to challenge not just mathematical convention but political authority.

The cost of such brilliance—such defiance—was his life. And yet, in death, Galois achieved immortality. His unfinished theories, once dismissed by the gatekeepers of his time, would go on to revolutionize algebra and become the mathematical backbone of modern cryptography.

THE ANAND-CLEMENT RULE AND THE RISE OF ARTIFICIAL STUPIDITY (AS): [AI(alg*) =AS]

Nearly two centuries later, America finds itself at a similar crossroads, where genius is stifled by bureaucracy, dissent pathologized by surveillance, and freedom increasingly mediated by algorithm. Artificial intelligence, once hailed as a tool for innovation, is being weaponized into a machinery of control.

Government agencies deploy predictive models to identify potential threats before they manifest. Social media is quietly scrubbed of “misinformation,” often defined not by truth or falsity but by its political inconvenience. Behavioral data is harvested, interpreted, and used to shape public opinion with the cold precision of machine logic. In this environment, what we need is not another committee, another white paper, or another billionaire promising to “fix” AI. We need another Galois.

Évariste Galois was a mathematician, yes, but he was also something more dangerous: a rebel armed with intellect. In an era when the French monarchy clung desperately to power, Galois infused his mathematics with revolutionary intent.

When his work was rejected by the Académie des Sciences—not because it lacked merit, but because it defied comprehension—he responded not with retreat but with further resistance. He wrote some of his most groundbreaking ideas while in prison, jailed for his political affiliations and unwillingness to stay silent. His insights into group theory and polynomial equations were not recognized in his lifetime, but they would eventually enable the very cryptographic systems that secure our digital world today.

Now, the same mathematical logic that once promised freedom is being turned against us. AI models trained on biased data have become arbiters of policing and policy.

Predictive analytics are used to label entire communities as pre-criminal. Whistleblowers are flagged before they even speak. The infrastructure of surveillance is not theoretical.  It is real, expanding, and largely invisible to the public eye. One might be forgiven for believing we live in the opening scenes of a digital dystopia, authored not by Orwell or Huxley, but by Silicon Valley and federal contracting officers.

A modern Galois would recognize this threat immediately. He would not be blinded by the sleek user interfaces or the euphemisms of “safety” and “efficiency.” He would see the core structure—the groupings, the mappings, the inputs and outputs—and he would begin to work, not within the system, but against it.

Such a figure would likely live on the margins, as Galois did. Not in boardrooms or tenured halls, but in basements, in back rooms, in code repositories that defy takedown notices. Like Galois, he would act, refusing the slow crawl of institutional reform in favor of radical clarity and unapologetic confrontation. 

The original Galois fought with mathematical symbols and revolutionary rhetoric; the next one will use code, encryption, statistical sabotage, and perhaps even art. They will not simply expose algorithmic bias, but design counter-algorithms. They will not simply warn about surveillance—they will build systems that resist it. Most importantly, they will not ask for permission. They will not wait for approval. Like Galois, they will press forward even when ignored, even when threatened, even when alone.

Today, the duel is no longer between men with pistols at dawn. It is between human autonomy and machine-enforced obedience. It is not fought in a field, but in data centers and courtrooms, on social media platforms and academic journals, in the deep recesses of source code. Yet the stakes are no less fatal. For if we lose this duel, we lose not just privacy, but the possibility of unmediated thought. We lose the chance to live without prediction—to make mistakes, to dissent, to surprise even ourselves.

There is, perhaps, a young person reading this now. Maybe they’ve already written the first lines of a new theory in a worn notebook. Maybe they’ve noticed the flaws in the system’s logic—the statistical assumptions that go unchecked, the human complexities that don’t quite fit the model. Maybe they feel the walls closing in, and wonder if it’s too dangerous to speak up. To them, we say: history may not reward you in your lifetime, but it will remember you if you persist.

The algorithms are learning. But so can we. Let the next Galois rise—not in mourning, but in resistance.  Galois wrote his last letter in the certainty of death, but also with the certainty that ideas—true, defiant, revolutionary ideas can outlive their authors.

His genius was buried too soon, but not before he lit a fire that still burns. Today, we need that fire again. 

“I have no time.”

—Galois wrote to his friend the night before his death, urgently summarizing his work. America is running out of time, too.

THE ANAND-CLEMENT RULE AND THE RISE OF ARTIFICIAL STUPIDITY (AS): [AI(alg*) =AS]

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REFERENCE AND NOTES

What is the core concept of the “Digital AI Caligula”?

This extensive source explores several technological approaches to combatting issues related to controlled substances, healthcare fraud, and medical documentation integrity, presenting them as potential tools against emerging forms of digital control.

The first patent introduces NarxCare, a system using patient prescription data to generate a risk score for potential drug misuse, offering a quicker alternative to manual reviews of prescription histories.

The second describes a Japanese patent for an integrated opioid management system featuring a smart dispenser, mobile app, and data analysis to improve safety and prevent misuse through controlled dispensing and patient monitoring, notably incorporating biometric authentication.

The third patent focuses on detecting copied and pasted passages in medical documents, a common practice in EHRs that can introduce inaccuracies, by using algorithms and risk analysis to identify potentially problematic text for review and correction.

The fourth details a UK patent for a healthcare fraud detection system that integrates diverse data sources into a visual graph, employing automated triggers and metrics to identify suspicious patterns in prescribing, billing, and other activities.

Finally, the fifth document outlines a forensic system and method for analyzing controlled substance prescribing practices using a structured, color-coded chronological timeline and a scoring system based on “standards of care” to provide objective data for legal and clinical evaluation of potential fraud or abuse.

Collectively, these patents highlight the increasing reliance on data integration, algorithmic analysis, and automated systems to address complex problems in healthcare, simultaneously raising questions about surveillance and control.

The core concept is the emergence of an all-encompassing Government AI by April 30, 2025, which has absorbed all existing laws and regulations.

This AI creates laws so numerous and complex that they are effectively unreadable by humans, similar to Emperor Caligula’s practice of posting laws in tiny script.

“This results in a society governed by fear, where citizens face consequences for even minor, unwitting non-compliance due to the sheer volume and incomprehensibility of…”

Patent 1

Here is a detailed briefing document reviewing the main themes and most important ideas or facts from the provided excerpts of the US 8,688,477 B1 patent for NarxCare:

Briefing Document: Review of US 8,688,477 B1 Patent (NarxCare)

Patent Title: METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR DETERMINING A NARCOTICS USE INDICATOR Patent Number: US 8,688,477 B1 Issue Date: April 1, 2014 Inventor: James Huizenga Assignee: National Assoc. of Boards of Pharmacy

Executive Summary:

The patent describes a method, system, and computer program product for generating a “narcotics use indicator” to help healthcare providers and others quickly assess a patient’s likelihood of proper or improper prescription drug use, specifically regarding narcotics and controlled substances. This indicator is a numerical score derived from analyzing various aspects of a patient’s prescription history and comparing it to general population prescription drug use data. The goal is to provide a more efficient alternative to manually reviewing lengthy Prescription Monitoring Program (PMP) reports, which are underutilized due to their cumbersome nature.

Main Themes and Important Ideas/Facts:

1. Addressing the Problem of Prescription Drug Abuse: The patent explicitly identifies prescription drug abuse as the “leading form of narcotic drug abuse in the US,” having replaced heroin as the number one drug abuse problem. It highlights the significant increase in emergency department visits related to narcotic addiction and the daily challenges faced by healthcare entities, law enforcement, and educators in dealing with this issue.

• Quote: “Prescription drug abuse is the leading form of narcotic drug abuse in the US. Heroin has been replaced with prescription grade synthetic narcotics… Prescription drug abuse is the number one drug abuse problem in the US.”

1. Limitations of Existing Prescription Monitoring Programs (PMPs): The patent acknowledges the existence of state-based PMPs, which are federally funded and require pharmacists and providers to report narcotic distributions to the state database. However, it criticizes the low utilization rate of these programs by prescribers due to the “somewhat arduous process to navigate to the site, login, enter demographic data, wait for the report search, download the PDF and then read all of the data.” The patent cites Ohio’s PMP as an example, stating that “only 17% of prescribers in the state have even applied for access to the PMP and fewer than that use the system regularly.”
2. Introduction of a “Narcotics Use Indicator”: The core of the patent is the invention of a method and system to create a “narcotics use indicator” (labeled as 10 in the diagrams). This indicator is a numerical score designed to provide a quick assessment of a patient’s drug use likelihood.

• Quote: “A method, system, and computer program product for determining a narcotics use indicator that enables a significant advance in the state of the art.”
• Quote: “…to quickly review a numerical score that reflects a patient’s past drug use and is indicative of proper, or improper, future drug use.”

1. Data Sources and Analysis: The system retrieves patient-specific prescription data from a prescription database (6000). This data includes details such as prescriber, distributor, drug type (narcotic or controlled substance), strength, and quantity. The patient-specific data is then compared with “a plurality of general population prescription drug use data.”

• Quote: “The present method, system, and computer program product retrieve patient specific data from a record (6100) and transforms the data into at least one indicator by comparing the patient specific data with a plurality of general population prescription drug use data.”
• Quote: “The general population prescription drug use data referenced is data associated with at least 1000 patients over the period of interest.” (Later specified as potentially 1,000,000 or 5,000,000 patients).

1. Categories of Indicators: The patent describes several potential “individual indicator values” that are generated from the patient data and comparison to the general population data. These are grouped into four classes:

• Usage Related Indicator (1000): Factors in the patient’s past drug use, including the type of narcotics and controlled substances used. This includes metrics like “morphine equivalents unit quantity” (1120) and “controlled substance unit quantity” (1320) compared to the general population to determine percentiles (1140, 1340).
• Instruction Related Indicator (2000): Considers the patient’s past use of prescribers, quantity of prescriptions, and the number of open prescriptions from different prescribers. This includes a “prescriber indicator” (2200) based on prescriber quantity (2220) and percentile (2240), and a “prescription overlap indicator” (2300) based on prescription overlap quantity (2320) and percentile (2340).
• Dispensing Related Indicator (3000): Examines a patient’s use of pharmacies or distribution sources. This includes a “distribution source indicator” (3100) based on distribution source quantity (3120) and percentile (3140), and a “distribution geography indicator” (3200) based on distance (3220) and percentile (3240).
• Auxiliary Indicator (4000): May reflect the patient’s number of active prescriptions (4300), the frequency of NAR requests (4100), or how the NAR indicator has changed over time (rate of change indicator 4200).

1. Weighted Adjustment Factors: The individual indicator values are then combined to create the final narcotics use indicator (10) through the application of “at least one adjustment factor” (5000). These factors are selectively weighted to reflect the relevance of each indicator in predicting proper drug use. The patent gives an example where the “narcotic usage adjustment factor” (5110) for morphine equivalents is weighted four times greater than other factors, suggesting its higher predictive value.

• Quote: “…such individual indicator values may be selectively weighted to create a final narcotics use indicator.”
• Quote: “…an adjustment factor (5000) may be applied to any, or all, of these indicators to weight their relevance in predicting proper prescription drug use and ultimately arrive at a narcotics use indicator (10).”
• Quote: “Here the narcotic usage adjustment factor (5110) is four times greater than the other adjustment factors because the morphine equivalents unit percentile (1140) is more directly indicative of improper prescription drug use.”

1. Numerical Score and Visual Display: The combined and weighted indicators are transformed into a numerical score for the narcotics use indicator (10). This score is designed to be quickly understood by a prescriber. The patent suggests that the number of active prescriptions (4300) can be added as a third digit to the score to provide additional immediate information.

• Quote: “…selectively weighted and transformed into a numerical score for the narcotics use indicator…”
• Quote: “…transformed into a numerical narcotics use indicator (10) displayed on a visual media.”
• Quote: “In a further embodiment, the number of active prescriptions is an active prescription indicator (4300) and is added as a third digit in the narcotics use indicator (10).”

1. Multiple Time Periods: The analysis can be performed over multiple time periods, and the weighted average of the percentiles across these periods can be used for the final indicator. The patent illustrates using 60-day, 180-day, 365-day, and 730-day periods.

• Quote: “The upper table of FIG. 11 illustrates one embodiment in which 4 such periods are utilized.”
• Quote: “…multi period percentiles may be determined for each indicator… these multi period percentiles are simply the average percentile value for the given number of periods.”

1. Automatic Adjustment of Factors: Adjustment factors can be automatically increased if preset criteria are met concerning data that “highly correlates with improper prescription drug use,” such as significant prescription overlap from different prescribers or multiple open controlled substance prescriptions from the same prescriber.

• Quote: “In another embodiment any of the adjustment factors may be automatically adjusted if preset criteria are met concerning data that highly correlates with improper prescription drug use.”

1. Speed of Determination: A key feature and advantage of the system is its speed, aiming to provide the narcotics use indicator within 5 seconds of the request. This is contrasted with the time-consuming process of reviewing PMP reports.

• Quote: “The narcotics use indicator (10) is created within 5 seconds of the request.”
• Quote: “…storing the converted data on hardware for use in determining the final narcotics risk indicator (10) in less than 5 seconds…”

1. System Architecture and Data Security: The system is described as a “prescription drug use processor” which can be a programmed computer device. It involves securely retrieving data from a database, potentially a remote one, and processing it locally. Security measures include clearing patient-specific data from local memory after the indicator is created and timestamping access within the remote database.

• Quote: “The prescription drug use processor is a specially programmed computer device…”
• Quote: “Further, in light of confidential patient data security, the local prescription drug use processor may then clear the patient specific data from the local memory…”

1. Statistical Analysis: The patent briefly mentions that the analysis of large quantities of data involves statistical methods to identify ranges and percentile rankings. It uses illustrative charts (FIGS. 1-3) to show raw data, log normal distribution, and a percentile curve based on the number of prescribers over a 360-day period.

• Quote: “The analysis of large quantities of data is well known in the field of statistics to identify acceptable ranges, unacceptable ranges, and percentile rankings, and therefore will not be reviewed in detail.”

In summary, the US 8,688,477 B1 patent for NarxCare details a novel system designed to overcome the limitations of traditional PMPs by providing a rapid, numerical assessment of a patient’s risk for prescription drug misuse, particularly with narcotics and controlled substances. This is achieved by analyzing various aspects of their prescription history, comparing it to general population data, weighting the significance of different factors, and presenting the result as an easily understandable score.

• What is the purpose of the US8688477 patent? The patent describes a method, system, and computer program product for determining a “narcotics use indicator.” This indicator is designed to help prescribers, such as physicians, quickly assess a patient’s past drug use and predict the likelihood of proper or improper future drug use of prescription drugs, particularly narcotics and controlled substances.
• What is the main problem that this invention aims to address? The invention aims to address the problem of prescription drug abuse, which is identified as the leading form of narcotic drug abuse in the United States. Existing state-based Prescription Monitoring Programs (PMPs) are available, but prescribers utilize them at a relatively low rate due to the cumbersome process of accessing and interpreting the data. This invention seeks to provide a more efficient and user-friendly tool for assessing a patient’s risk.
• How does the narcotics use indicator system work? The system retrieves patient-specific prescription data from a database. This data is then compared with data from a larger general population to generate several individual indicators. These individual indicators, which fall into categories like usage-related, instruction-related, dispensing-related, and auxiliary indicators, are then selectively weighted and combined to create a final numerical score for the narcotics use indicator.

• What types of individual indicators are used to calculate the narcotics use indicator? The patent outlines several potential individual indicators. These include usage-related indicators (considering the type, strength, and quantity of narcotics and controlled substances used), instruction-related indicators (examining the patient’s history with prescribers, quantity of prescriptions, and prescription overlaps), dispensing-related indicators (analyzing the patient’s use of pharmacies), and auxiliary indicators (such as the number of active prescriptions and the frequency of indicator requests).
• How is patient-specific data compared to general population data? The comparison involves comparing the patient’s specific prescription data (e.g., quantity of a substance) to data from a larger population. This comparison can determine if the patient’s data falls within a “below normal,” “normal,” or “above normal” range, or it can determine a percentile ranking for the patient’s data compared to the larger population.

• What are “adjustment factors” and how are they used? Adjustment factors are applied to the individual indicators to weigh their significance in contributing to the final narcotics use indicator score. Different indicators can have different weightings, and these factors can be automatically adjusted based on certain criteria that correlate highly with improper prescription drug use.
• Can the narcotics use indicator incorporate data from multiple time periods? Yes, the system can utilize data from multiple periods, such as 60 days, 180 days, 365 days, and 730 days. The indicators for each period can be calculated, and a multi-period percentile can be determined, often by averaging the percentiles from the individual periods. This can help to provide a more comprehensive view of the patient’s prescription history.
• What are some potential benefits of using this narcotics use indicator system? The primary benefit is to provide prescribers with a quick and easy-to-understand numerical score that helps them assess a patient’s risk of improper prescription drug use. This can potentially aid in clinical decision-making, improve patient safety, and contribute to addressing the problem of prescription drug abuse. The system aims to transform complex prescription data into a readily actionable format.

Here is a timeline of the main events covered in the provided source, followed by a cast of characters:

Timeline of Main Events

• December 3, 2009: McKesson releases information about its new McKesson Automation Software Release, which enables advance analysis for improved product performance. This is an early indication of the type of technology that would later be utilized in systems like the one described in the patent.
• September 17, 2010: A U.S. provisional patent application, Ser. No. 61/383,927, is filed. This application serves as the basis for the later patent concerning the narcotics use indicator.
• September 16, 2011: A U.S. patent application, Serial No. 13/234,777, is filed. This is the non-provisional application claiming the benefit of the earlier provisional application and detailing the method, system, and computer program product for determining a narcotics use indicator.
• April 1, 2014: U.S. Patent US 8,688,477 B1, titled “METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR DETERMINING A NARCOTCS USE INDICATOR,” is issued to James Huizenga and assigned to the National Assoc. of Boards of Pharmacy.
• After April 1, 2014: The term of Patent US 8,688,477 B1 is extended or adjusted by 107 days under 35 U.S.C. 154(b). The specific end date is not provided, but it would be 107 days beyond April 1, 2014.
• Ongoing (as of the patent filing): All 50 states in the U.S. have or are developing state-based Prescription Monitoring Programs (PMPs). These programs are typically funded at the Federal level and require pharmacists and providers to report every narcotic distribution to the state PMP.

Cast of Characters

• James Huizenga: The inventor listed on U.S. Patent US 8,688,477 B1. He is based in Dayton, OH (US).
• National Assoc. of Boards of Pharmacy: The assignee of U.S. Patent US 8,688,477 B1. Their location is listed as Mount Prospect, IL (US).
• McKesson: A company mentioned in “OTHER PUBLICATIONS” related to their automation software release which enables advance analysis. While not directly involved in the patent, this suggests the existence of similar technologies or related work in the field.
• John Pauls: The Primary Examiner listed on U.S. Patent US 8,688,477 B1.
• Trang Nguyen: The Assistant Examiner listed on U.S. Patent US 8,688,477 B1.
• Marshall, Gerstein & Borun LLP. Randall G. Rueth: The Attorney, Agent, or Firm listed as representing the applicant for U.S. Patent US 8,688,477 B1.
• Prescribers (Physicians, Physician Assistants): Healthcare professionals who write prescriptions. The patent describes the potential for these individuals to utilize the narcotics use indicator system.
• Pharmacists: Professionals who dispense medications and are required to report narcotic distributions to state PMPs.
• Patients: Individuals whose prescription drug use data is analyzed by the method and system described in the patent to determine a narcotics use indicator.

Patent 2

BRIEFING DOCUMENT

Source: Excerpts from “11_3_22 Foreign Reference.pdf” (Japanese Patent Application Publication JP 2019-513504 A)

Date: Published on 30-May-2019 Application Number: JP 2019-561197 A Filed: 13-Feb-2017 Inventors: Valentine, Edmund L.; Valentine, Edmund A. Assignee: Valentine, Edmund L. Attorney/Firm: Masuda & Associates

Subject: Opioid + device combination products with improved safety and efficacy profiles (Japanese) / 安全性および有効性プロフィールの向上したオピオイド+デバイス組合せ製品 (Japanese)

Main Themes:

The core theme of this document is the description of an invention related to opioid + device combination products aimed at improving the safety and efficacy of opioid administration. This is achieved through a comprehensive system that integrates a drug dispenser, an application program, and a data system to manage opioid use, reduce risks, and provide patient support.

Key Ideas and Facts:

1. Addressing the Opioid Crisis: The invention is presented in the context of the ongoing opioid crisis, highlighting the significant public health and economic burden of opioid abuse, misuse, addiction, overdose, and death.

• “According to the United Nations Office on Drugs and Crime (UNODC), drug overdose is the top cause of drug-related death in the world, and opioids are the top drug type associated with these deaths.” (Page 3)
• “Opioid addiction and withdrawal symptoms are not considered addictions. Tolerance occurs because of the physiological changes that result from exposure to opioids. Withdrawal symptoms are unpleasant physical and emotional symptoms that occur upon withdrawal of the opioid after tolerance development.” (Page 42)
• “Opioid addiction and withdrawal symptoms are not considered addictions. Tolerance occurs because of the physiological changes that result from exposure to opioids. Withdrawal symptoms are unpleasant physical and emotional symptoms that occur upon withdrawal of the opioid after tolerance development.” (Page 43 – repeated)
• “Opioid addiction and withdrawal symptoms are not considered addictions. Tolerance occurs because of the physiological changes that result from exposure to opioids. Withdrawal symptoms are unpleasant physical and emotional symptoms that occur upon withdrawal of the opioid after tolerance development.” (Page 46 – repeated)

1. Integrated System for Opioid Management: The invention describes a system comprising three main components:

• Drug Dispenser: A device for dispensing opioids, including both disposable and reusable versions, and cassette formats. These dispensers are designed to control the release of opioids according to the prescribed regimen and potentially incorporate features like biometric authentication, temperature monitoring, and tamper resistance.
• Application Program: A software application (running on a smartphone, tablet, or computer) that communicates with the dispenser and the data system. It manages the dispensing schedule, tracks patient compliance, collects data (including patient self-assessment), and provides alerts.
• Data System: A central server or database that stores patient data, prescription information, dispensing records, and other relevant information. This system facilitates monitoring, analysis, and communication between patients, healthcare providers, and support centers.

1. Improving Safety and Efficacy: The system aims to achieve improved safety and efficacy through several mechanisms:

• Controlled Dispensing: Ensuring that patients receive the correct dose at the correct time, preventing early or unauthorized access.
• “The system checks when the patient asks each time the patient attempts to dispense a prescribed opioid dose, and the patient’s history makes an observation related to the required drug to determine the delivery, in this way increasing the drug efficacy / safety profile, the integrated system improves the quality of protection and the quality of life of the patient, while the occurrence of drug-related side effects, addictions and dependencies Of the health care system by eliminating (i) deaths from overdose, (ii) emergency room visits, (iii) hospitalizations, and (iv) interventions by physicians and partnered health care professionals while reducing false Save money.” (Page 7)

• Patient Monitoring and Self-Assessment: Allowing patients to report their pain levels, side effects, and other relevant information, and for the system to collect physiological data (e.g., pulse, respiration rate).
• “Examples of aspects of self-assessment related to opioid abuse using the Bristol Stool scale, ie, self-test motor skills test 864, And Stool consistency observations of self-reported abola using the Bristol Stool scale, ie, stool assessment that are well accepted. These are examples of acreena that can be used in this embodiment for input to the drug specific dispensing algorithm 13 to determine whether to dispense.” (Page 34)
• Alerts and Notifications: Generating alerts for potential issues like missed doses, early requests, potential overdose, or abnormal physiological readings, and notifying healthcare providers or support centers.
• Prescriber and Pharmacist Interaction: Facilitating communication between the integrated system and healthcare professionals for prescription updates, monitoring, and interventions.
• “Eliminating double entry can also be facilitated through data integration through the use of networks such as the SureScripts Electronic Prescription Network linking physicians and pharmacies together.” (Page 9)
• Risk Mitigation: Incorporating risk mitigation strategies based on collected data, such as adjusting dispensing based on self-assessment or physiological indicators.
• Personalized Dispensing and Treatment: Tailoring the dispensing schedule and monitoring parameters based on the individual patient’s needs, prescription, and data.
• “The personalized opioid specific application 12 is then linked to the patient identifier number 160. The patient specific opioid specific application 12 is then compiled; stored in the opioid specific application database on the data server 10; and downloaded to the patient 6 in a downloaded email and / or text message. It is transmitted automatically.” (Page 27)

1. Data Collection and Analysis: The system emphasizes comprehensive data collection, including prescription details, dispensing history, patient self-reported data, physiological data, and environmental data (e.g., temperature and humidity of the dispenser). This data is used for monitoring, analysis, and generating reports.

• “All data collected by the opioid specific application 12 from drug dispensers 16, 920, 930, 940, ie digitally captured information 852, 1012, 856, 857, 858, 859, 880, 882, 884, 886, patient self-assessment screen 862, patient self-test screen 864, and self-reporting screen 868 included in opioid specific application 12 and the output of each of drug specific distribution algorithm 13 is then processed by opioid specific application 12.” (Page 36)

1. Support Centers and Integrated Communication: The system includes integrated support centers (Call Centers, Patient Support Centers, Physician Support Centers, Prescriber Support Centers, etc.) that can provide assistance to patients and healthcare providers based on the data and alerts generated by the system.

• “The integrated support center (IT system (data server) stores approved logon information, all application history data, and application history on the device of all patients for which an opioid specific and patient tailored application has been downloaded.” (Page 9)
• “FIG. 17 is an exemplary embodiment of the integrated support center 22 triage flow from patient 6 communication to problem evolving Triage is organized into three major types of Patient 6 support calls: dispenser operation, dosing issues, and medical questions.” (Page 32)

1. Biometric Authentication: The invention describes the use of biometric authentication (e.g., iris scan, fingerprint, voice recognition) to ensure that only the authorized patient can access the dispenser and receive the opioid.

• “Biometric is also used to control the drug dispenser at the time of dispensing to prevent diversion and theft.” (Page 89 – Japanese section, translated meaning)
• “Biometric data (also known as Biometric Identification, Biometric Sign On) Include, but are not limited to, biometric techniques that capture fingerprints, digitally, palm and full hand scanners, Opical face. It includes recognition systems Iris scan technology, pupil scan, document readers, biometric technology used to provide patient access (login) to mobile devices and / or remotely to limit access to the patient) to the document, the term is not biometric but of a login name combined with a password and / or any additional security information, eg a computer generated password sent by the earner via email and / or text message.” (Page 12)

1. Different Dispenser Formats: The invention encompasses various dispenser formats, including disposable and reusable dispensers, and drug cassettes designed to hold different opioids or combinations of opioids.
2. Integration with Healthcare Systems: The system is designed to integrate with existing healthcare IT systems, including electronic medical records (EMR) and electronic prescription networks.

Most Important Ideas/Facts:

• The invention directly addresses the opioid crisis by focusing on preventing opioid abuse, misuse, addiction, overdose, and death through controlled dispensing and monitoring.
• The core innovation is the integrated system that combines a drug dispenser, a mobile application, and a data system to provide a comprehensive solution for opioid management.
• The system’s ability to collect and analyze various types of data (prescription, dispensing, patient-reported, physiological) is crucial for personalized treatment, risk mitigation, and generating alerts for potential problems.
• The inclusion of biometric authentication is a significant security feature aimed at preventing unauthorized access and diversion.
• The concept of integrated support centers highlights the human element in the system, providing crucial assistance to patients and healthcare providers.
• The system’s focus on patient self-assessment and self-reporting empowers patients to actively participate in their treatment and provides valuable data for monitoring.

Timeline of Main Events

• July 2000: US Patent 6,093,858 is filed, describing an apparatus and method for determining physiologic perturbations of a patient. (Referenced as “Backward Patent Citations”)
• May 30, 2014: Japanese Patent JP 2014-513504 A is published. (Referenced as “Publication Date”)
• August 11, 2014: US Provisional Patent Application No. 62/035,285 is filed. (Referenced as “Priority Data”)
• February 12, 2015: US Provisional Patent Application No. 62/115,012 is filed. (Referenced as “Priority Data”)
• April 20, 2015: US Provisional Patent Application No. 62/149,919 is filed. (Referenced as “Priority Data”)
• August 11, 2015: US Provisional Patent Application No. 62/203,875 is filed. (Referenced as “Priority Data”)
• August 12, 2015: US Provisional Patent Application No. 62/204,585 is filed. (Referenced as “Priority Data”)
• August 15, 2015: US Provisional Patent Application No. 62/205,650 is filed. (Referenced as “Priority Data”)
• August 15, 2016: US Provisional Patent Application No. 62/375,192 is filed. (Referenced as “Priority Data”)
• November 30, 2016: US Provisional Patent Application No. 62/428,919 is filed. (Referenced as “Priority Data”)
• December 9, 2016: US Provisional Patent Application No. 62/432,282 is filed. (Referenced as “Priority Data”)
• December 13, 2016: US Provisional Patent Application No. 62/433,952 is filed. (Referenced as “Priority Data”)
• December 17, 2016: US Provisional Patent Application No. 62/435,622 is filed. (Referenced as “Priority Data”)
• December 19, 2016: PCT Filing: PCT/US2017/017885 (Referenced as “PCT Filing”)
• February 13, 2017: Japanese Patent Application JP 2016-561197 A is filed. (Referenced as “Filing Date”)
• August 17, 2017: WO 2017/139761 A1 is published. (Referenced as “PCT Filing”)
• May 30, 2019: Japanese Patent JP 2019-513504 A is published. (Referenced as “Publication Date”)
• April 30, 2018: US 2015/0118658 A1 is published, describing an apparatus and method for determining physiologic perturbations of a patient. (Referenced as “Backward Patent Citations”)

Cast of Characters

• Valentine, Edmund L.: Listed as an inventor and applicant of the Japanese Patent Application JP 2019-513504 A. Based in Palm Beach Gardens, FL, US.
• バレインタイン, エトモンド, エル (Valentine, Edmund, L.): The Japanese transliteration of Valentine, Edmund L., also listed as an inventor and applicant.
• 島田秀 (Shimada, Shu): Listed as the Attorney or Firm for the Japanese Patent Application JP 2019-513504 A. Based in Palm Beach Gardens, FL, US.
• Abraham, Matthew R.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Williams, Ruthie R.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Lipari, Rosalie N.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Jonahki, Bose.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Copello, Elizabeth AP: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Carroll, Larry A.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.
• Hansen, R. N.: Listed as an author of a reference titled “Economic Costs of Use for Prescription Opioids,” published in 2011.
• Wendland, C. S.: Listed as an author of a reference titled “Societal Costs of Prescription Opioid Abuse, Dependency and Misuse in the United States,” published in 2014.
• Patient 6: A principle individual mentioned throughout the document in the context of receiving treatment via the described opioid dispensing system. Various interactions and data related to Patient 6 are discussed, including biometric data, self-assessments, and treatment adherence.
• Prescriber 2: A principle individual mentioned as someone who can prescribe opioids and interact with the described system, including sending e-prescriptions and receiving alerts and data about Patient 6.
• Pharmacy 8: A principle entity mentioned as part of the integrated system, receiving e-prescriptions and dispensing opioids.
• Healthcare Professionals: A general term used to refer to individuals involved in Patient 6’s care, including physicians, physician assistants, nursing practitioners, nurses, pharmacists, and others.
• Caregiver: An individual who may be involved in supporting Patient 6’s treatment, potentially receiving alerts and information from the system.
• Integrated Support Center 22: A key entity in the described system, providing support, receiving alerts, and interacting with patients, prescribers, and pharmacies.
• IBM Watson: Mentioned as a cognitive computing system capable of processing large amounts of information and potentially used in conjunction with the described system for analyzing patient data and recommending treatment.
• Chittenden, M. et al.: Authors of a reference cited for information on cognitive impairment associated with chronic pain and opioid therapy.
• Janigula, D. et al.: Authors of a reference cited for information on the relationship between sleep apnea and opioid use.
• S. Silverman: Mentioned in the context of pain transmission pathways.
• Haber, W.: Mentioned in the context of pain transmission pathways.
• Menschikant, D.: Mentioned in the context of pain transmission pathways.
• Pernia, P.: Mentioned in the context of the relationship between sleep apnea and opioid use.
• Grace, P.: Mentioned in the context of the relationship between sleep apnea and opioid use.
• Anand, S. et al.: Authors of a reference cited for information on clinical opioid withdrawal symptoms.
• Ciang, J. et al.: Authors of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.
• Ganz, A.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.
• Abou-Nader, K.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.
• Steffin, B.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.
• Woodcock, Janet: Mentioned as the Director of the FDA’s Drug Evaluation and Research, in the context of regulatory considerations.

convert_to_textConvert to source

NotebookLM can be inaccurate; please double check its responses.

Timeline of Main Events

July 2000: US Patent 6,093,858 is filed, describing an apparatus and method for determining physiologic perturbations of a patient. (Referenced as “Backward Patent Citations”)

May 30, 2014: Japanese Patent JP 2014-513504 A is published. (Referenced as “Publication Date”)

August 11, 2014: US Provisional Patent Application No. 62/035,285 is filed. (Referenced as “Priority Data”)

February 12, 2015: US Provisional Patent Application No. 62/115,012 is filed. (Referenced as “Priority Data”)

April 20, 2015: US Provisional Patent Application No. 62/149,919 is filed. (Referenced as “Priority Data”)

August 11, 2015: US Provisional Patent Application No. 62/203,875 is filed. (Referenced as “Priority Data”)

August 12, 2015: US Provisional Patent Application No. 62/204,585 is filed. (Referenced as “Priority Data”)

August 15, 2015: US Provisional Patent Application No. 62/205,650 is filed. (Referenced as “Priority Data”)

August 15, 2016: US Provisional Patent Application No. 62/375,192 is filed. (Referenced as “Priority Data”)

November 30, 2016: US Provisional Patent Application No. 62/428,919 is filed. (Referenced as “Priority Data”)

December 9, 2016: US Provisional Patent Application No. 62/432,282 is filed. (Referenced as “Priority Data”)

December 13, 2016: US Provisional Patent Application No. 62/433,952 is filed. (Referenced as “Priority Data”)

December 17, 2016: US Provisional Patent Application No. 62/435,622 is filed. (Referenced as “Priority Data”)

December 19, 2016: PCT Filing: PCT/US2017/017885 (Referenced as “PCT Filing”)

February 13, 2017: Japanese Patent Application JP 2016-561197 A is filed. (Referenced as “Filing Date”)

August 17, 2017: WO 2017/139761 A1 is published. (Referenced as “PCT Filing”)

May 30, 2019: Japanese Patent JP 2019-513504 A is published. (Referenced as “Publication Date”)

April 30, 2018: US 2015/0118658 A1 is published, describing an apparatus and method for determining physiologic perturbations of a patient. (Referenced as “Backward Patent Citations”)

Cast of Characters

Valentine, Edmund L.: Listed as an inventor and applicant of the Japanese Patent Application JP 2019-513504 A. Based in Palm Beach Gardens, FL, US.

バレインタイン, エトモンド, エル (Valentine, Edmund, L.): The Japanese transliteration of Valentine, Edmund L., also listed as an inventor and applicant.

島田 秀 (Shimada, Shu): Listed as the Attorney or Firm for the Japanese Patent Application JP 2019-513504 A. Based in Palm Beach Gardens, FL, US.

Abraham, Matthew R.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Williams, Ruthie R.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Lipari, Rosalie N.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Jonahki, Bose.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Copello, Elizabeth AP: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Carroll, Larry A.: Listed as an author of a reference on prescription drug abuse and misuse in the United States, published in September 2016.

Hansen, R. N.: Listed as an author of a reference titled “Economic Costs of Use for Prescription Opioids,” published in 2011.

Wendland, C. S.: Listed as an author of a reference titled “Societal Costs of Prescription Opioid Abuse, Dependency and Misuse in the United States,” published in 2014.

Patient 6: A principle individual mentioned throughout the document in the context of receiving treatment via the described opioid dispensing system. Various interactions and data related to Patient 6 are discussed, including biometric data, self-assessments, and treatment adherence.

Prescriber 2: A principle individual mentioned as someone who can prescribe opioids and interact with the described system, including sending e-prescriptions and receiving alerts and data about Patient 6.

Pharmacy 8: A principle entity mentioned as part of the integrated system, receiving e-prescriptions and dispensing opioids.

Healthcare Professionals: A general term used to refer to individuals involved in Patient 6’s care, including physicians, physician assistants, nursing practitioners, nurses, pharmacists, and others.

Caregiver: An individual who may be involved in supporting Patient 6’s treatment, potentially receiving alerts and information from the system.

Integrated Support Center 22: A key entity in the described system, providing support, receiving alerts, and interacting with patients, prescribers, and pharmacies.

IBM Watson: Mentioned as a cognitive computing system capable of processing large amounts of information and potentially used in conjunction with the described system for analyzing patient data and recommending treatment.

Chittenden, M. et al.: Authors of a reference cited for information on cognitive impairment associated with chronic pain and opioid therapy.

Janigula, D. et al.: Authors of a reference cited for information on the relationship between sleep apnea and opioid use.

S. Silverman: Mentioned in the context of pain transmission pathways.

Haber, W.: Mentioned in the context of pain transmission pathways.

Menschikant, D.: Mentioned in the context of pain transmission pathways.

Pernia, P.: Mentioned in the context of the relationship between sleep apnea and opioid use.

Grace, P.: Mentioned in the context of the relationship between sleep apnea and opioid use.

Anand, S. et al.: Authors of a reference cited for information on clinical opioid withdrawal symptoms.

Ciang, J. et al.: Authors of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.

Ganz, A.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.

Abou-Nader, K.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.

Steffin, B.: Mentioned as an author of a reference cited for information on the risk of overdose when combining opioids and benzodiazepines.

Woodcock, Janet: Mentioned as the Director of the FDA’s Drug Evaluation and Research, in the context of regulatory considerations.

NotebookLM can be inaccurate; please double check its responses.

Patent 3

Briefing Document: Analysis of Copy-Paste in Medical Documents (WO 2015/167852 A1)

Date: October 26, 2023

Subject: Review of PCT/US2015/026778, “IDENTIFICATION AND ANALYSIS OF COPIED AND PASTED PASSAGES IN MEDICAL DOCUMENTS”

Source: Excerpts from “3_7_22 Foreign Reference International Application.pdf”

Executive Summary:

This international patent application describes systems and techniques for identifying and analyzing copied and pasted passages within medical documents, particularly in the context of Electronic Health Records (EHRs).

The primary goal is to improve the accuracy and reliability of medical records, which can be compromised by the widespread use of copy-paste functionality. The systems described aim to detect potential inaccuracies and errors introduced by this practice, assess the associated risk level, and provide mechanisms for users (physicians, coders, etc.) to review, confirm, and correct identified issues.

The core technology involves comparing passages within a new medical document to previously generated medical documents and employing risk analysis based on various factors, including the origin and destination regions of the copy-paste, the nature of the copied text, and potential incompatibilities with other information in the document.

Key Themes and Ideas:

1. Problem of Copy-Paste in Medical Documents: The application highlights the benefits of copy-paste functionality in EHR systems for efficiency but also the significant drawbacks. It states: “the copy and paste functionality provided by the computerized medical record system may allow the physician to copy a passage (e.g., a text passage) of a previous medical document for the patient or a passage of a medical document for a different patient and paste the copied passage into the new medical document being generated by the physician. This more efficient process may effectively promote more complete electronic health records (EHR) for patients. However, the copy and paste functionality may also increase the probability that the copied and pasted passages (i.e., copy-paste passages) retain information specific to the old medical document from which the copy-paste passage was copied.” [0023] This can lead to “inaccurate (i.e., erroneous) information with regard to the patient encounter for which the new medical document has been generated.” [0023]
2. Risk of Inaccurate Information: The primary concern is that inaccurate copy-paste passages can have negative consequences, including:

• Exclusion of benign information while including inaccurate information.
• Inaccurate information that could result in negative medical outcomes for the patient.
• Overpayment or underpayment for physician services.
• Documentation defined as fraudulent or possible litigation.
• Inaccurate laboratory information and order of unnecessary medications.
• Description of diagnoses or procedures not addressed or performed at the current patient encounter.
• Overbilling for the patient encounter.
• Description of patient complications that are no longer active or medications prescribed to a patient that are discontinued or no longer manufactured.
• Result in over-documentation by adding impertinent documentation to create the appearance to support overpayment.

1. System for Identification and Analysis: The core of the invention is a system (System 10 or Computing Device 100) designed to identify and analyze copy-paste passages. This system involves:

• Receiving a second medical document related to a patient encounter. [0005]
• Determining if a passage of the second document has been copied from a first medical document. [0005]
• Determining a risk level for the passage, indicating the likelihood of inaccurate information. [0005]
• Outputting an indication of the passage for which the risk level exceeds a threshold. [0005]

1. Copy-Paste Detection Techniques: Various techniques are described for identifying copy-paste passages:

• Comparing Content: Comparing content of the new medical document to one or more previously generated medical documents. This can involve comparing entire sections, paragraphs, sentences, words, or characters. [0024], [0056], [0057]
• Identifying Identical Text Strings: Determining if a continuous text string in the new document is identical to a continuous text string in a previously generated document. A minimum word count can be set for identifying such passages. [0057]
• Percentage of Identical Text: Determining the percentage of text in a passage that is identical to a text passage from another medical document. Risk thresholds can be set based on this percentage (e.g., >80%, >90%, >95%). [0058], [0062]
• Direct Tracking: Tracking copy and paste actions performed by the user. [0044]
• Natural Language Processing (NLP): Utilizing NLP techniques to determine when specific risks occur within copy-paste passages or between copy-paste passages and other parts of the document. [0026], [0065]

1. Risk Analysis and Determination: The system determines the risk level associated with copy-paste passages based on various factors:

• Contextual Factors: This includes whether the copy-paste passage was copied from or pasted into a restricted region of a medical document or specific context relevant to the patient encounter. [0004], [0038] Restricted regions can include patient background, history, symptoms, diagnosis, treatment, etc. [0038]
• Text String Characteristics: Analyzing the characteristics of the copied text string, such as whether it contains “risky” text strings or keywords that typically precede or are included in risky strings. [0039], [0065]
• Intradocument Incompatibilities: Determining incompatibilities within the new medical document where the copy-paste passages have been added. This can include grammatical tense disagreements, direct conflict with other information, inconsistent subject matter, or the co-occurrence of items that are typically not found together. [0040], [0066]
• Commonly Changed Portions: Identifying portions of copy-paste passages that are commonly changed by users, which may indicate a higher risk of error if not updated. [0064]
• Risk Levels: The risk level can be represented as a low risk or a high risk, or a more striated indication (e.g., a scale of 1 to 10). [0037], [0061], [0062]

1. User Interface and Interaction: The system provides a user interface to inform users about potential issues and facilitate corrections:

• Displaying Indications: Outputting indications of copy-paste passages, particularly those with elevated risk levels. This can involve highlighting the text, underlining, changing the color of the text, bracketing, inserting arrows, or providing audible/haptic feedback. [0026], [0041], [0091]
• Notifications: Generating notifications regarding potential documentation compliance issues related to high-risk copy-paste passages. [0019], [0110] These notifications can be presented as pop-up windows or within the patient’s EHR. [0110]
• Review and Correction Mechanisms: Allowing users to review identified copy-paste passages, confirm if the passage is correct, or modify a portion to remove inaccuracies. [0026], [0043], [0093], [0118]
• Linking to Source Document: Providing a link to the actual medical document from the EHR for correction. [0094]
• Sorting and Filtering: Allowing users to sort recent medical documents by criteria such as risk level, length of copy-paste passages, and whether the passages have been corrected. [0074]

1. System Architecture: The system can be implemented as a distributed system with a server (22) and client computing devices (12), or as a stand-alone computing device (100). [0009], [0011] The server and/or client devices include components such as a processor (50, 110), input/output devices (52/54, 114/116), communication interface (56, 112), and memory/storage (58, 120). Key modules described include the NLP module (60, 124), copy-paste detection module (64, 128), risk analysis module (68, 132), and interface module (72, 136). [0028], [0076] The repository (24) stores medical documents and relevant information like detection rules and risk analysis rules. [0028], [0053]

Most Important Ideas/Facts:

• The patent application directly addresses the problem of inaccurate information introduced by copy-paste in medical documents. This is a significant issue in healthcare, impacting patient safety, billing, and legal compliance.
• The proposed solution is a computerized system that automatically identifies and analyzes copy-paste passages for potential errors.
• The system goes beyond simple identification by assigning a risk level to the identified passages, allowing users to prioritize review.
• The risk assessment is multifaceted, considering factors like the source/destination region, the nature of the copied text, and internal inconsistencies within the document.
• The system provides interactive mechanisms for users to review, confirm, and correct identified issues, integrating with existing EHR workflows.
• The core components of the system include NLP, copy-paste detection, and risk analysis modules, demonstrating a sophisticated approach to the problem.
• The system aims to improve the accuracy and reliability of medical records, ultimately contributing to better patient care and reduced administrative burden.

Quotes from the Original Sources:

• “In the medical field, accurate processing of records relating to patient visits to hospitals and clinics ensures that the records contain reliable and up-to-date information for future reference. Accurate processing may also be useful for medical systems and professionals to receive prompt and precise reimbursements from insurers and other payors.” [0002]
• “However, the copy and paste functionality may also increase the probability that the copied and pasted passages (i.e., copy-paste passages) retain information specific to the old medical document from which the copy-paste passage was copied.” [0023]
• “Therefore, copy-paste passages present in a new medical document may include inaccurate (i.e., erroneous) information with regard to the patient encounter for which the new medical document has been generated.” [0023]
• “The system may also analyze any identified copy-paste passages for risk of potential error. The system may determine a risk level for each of the copy-paste passages of the medical document.” [0004]
• “In response to receiving the confirmation input or modification input, the computing device may remove the indication of the respective copy-paste passage because the potentially inaccurate information has been corrected or confirms as accurate.” [0026]
• “Although a passage may typically be a continuous text string, a passage may contain two or more text strings that are not continuous (e.g., text strings broken by formatting or other sections of text). A computing device may analyze one or more previously generated other medical documents and compare the content of each of the previously generated medical documents to the content of the medical document in order to identify any identical passages of the medical document.” [0024]
• “In other words, risky text strings may include words typically used to describe current patient conditions and/or numbers identifying current values of items such as patient lab reports, vital signs, or objective patient feedback.” [0039]
• “Temporal issues may include grammatical tense disagreement between the copy-paste passage and another passage of the new medical document.” [0101]
• “Intradocument incompatibilities may include information that is incompatible with other passages within the same medical document.” [0066]
• “Interface module 72 may output indication of any copy-paste passages having a risk level exceeding the risk threshold for display to the user (e.g., the physician or compliance officer).” [0041]

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Timeline of Main Events

March 1, 2007: US Patent Application US 2007/0043688 A1 is published, titled “METHOD AND APPARATUS FOR DETECTING PLAGIARISM”. While not the primary focus, this date appears on the International Search Report as a cited document related to plagiarism detection, indicating a broader context of work in this area predating the core invention in this source.

May 1, 2008: US Patent Application US 2008/0103916 A1 is published, titled “SYSTEM AND METHOD FOR DETECTING PLAGIARISM IN TEXT DOCUMENTS”. Similar to the previous date, this is a cited document in the International Search Report related to plagiarism detection.

April 28, 2014: Priority Date (US) claimed for the international patent application. This is the earliest date of a corresponding national application, establishing the effective filing date for the invention described.

April 21, 2015: International Filing Date (PCT) for the application WO 2015/167852 A1. This is the date the application was filed under the Patent Cooperation Treaty.

November 5, 2015: International Publication Date for the application WO 2015/167852 A1. This is the date the international application is published by WIPO.

July 15, 2015: Date of the International Search Report. This indicates when the international search was completed by the International Searching Authority.

Cast of Characters

Brian J. Stankiewicz: One of the inventors listed on the patent application. Resides at 3M Center, Post Office Box 33427, Saint Paul, Minnesota 55133-3427 (US).

Kelly K. Peterson: One of the inventors listed on the patent application. Resides at 3M Center, Post Office Box 33427, Saint Paul, Minnesota 55133-3427 (US).

Garril L. Rison: Listed as a representative for the applicant. Located at 3M Center, Post Office Box 33427, Saint Paul, Minnesota 55133-3427 (US).

Steven A. Hehn: Listed as a representative for the applicant. Located at 3M Office of Intellectual Property Counsel, Post Office Box 33427, Saint Paul, Minnesota 55133-3427 (US).

Blaine Copenheaver: Authorized officer at the International Searching Authority (ISA/US), responsible for the International Search Report. Located at Mail Stop PCT, Attn: ISA/US, Commissioner for Patents, P.O. Box 1450, Alexandria, Virginia 22313-1450 (US).

Patient: Refers to an individual whose medical documents are being processed and analyzed by the system for identifying copied and pasted passages.

Physician/Medical Professional: Refers to a user of the system who creates and reviews medical documents. They are the primary audience for the risk indications and tools provided by the system.

Healthcare Organization: Refers to the entity that employs the medical professionals and utilizes the system.

Medical Coding Professional/Compliance Officer: Refers to potential users or stakeholders who might utilize the system to identify and mitigate risks associated with copied and pasted information in medical documents, particularly related to billing and compliance.

Governmental Regulatory Agency: Refers to an external entity that may be interested in or involved with the accuracy and integrity of medical documentation, and potentially utilize or set standards related to the system’s function.

Patent 4

Briefing Document: Review of GB 2514239 A (System for detecting health care fraud)

Date: 2014-11-19 Applicant: Palantir Technologies Inc. Inventors: Lekan Wang, Kevin Sperling, Michael Winto, Christopher Ryan Luck

Summary:

This patent application describes a computer-based system and methods for detecting health care fraud. The system integrates health care data from various sources (providers, insurers, pharmacies, public sources) and transforms it into a structured ontology.

This data is then used to generate graphs and visualizations that highlight relationships and potential indicators of fraud. The system employs various metrics and triggers to automatically identify potential leads for investigation.

Key Themes and Ideas:

Data Integration: The system emphasizes the importance of collecting health care data from a wide range of sources, including providers, insurers, pharmacies, and public sources. This integrated approach allows for a more comprehensive view of health care activities and potential fraudulent patterns.

Data Modeling and Ontology: The collected data is transformed and organized into a defined ontology. This structured representation allows for consistent analysis and the creation of meaningful relationships between different data objects (providers, patients, events, etc.).

Graph-Based Visualization: A core component of the system is the generation of graphs that visually represent the relationships within the health care data. This graphical representation helps investigators identify connections and patterns that may not be apparent in raw data.

Fraud Detection through Metrics and Triggers: The system uses a variety of metrics and triggers to automatically identify potential leads for fraud investigation. These metrics can be based on various factors, such as unusual prescription patterns, high billing amounts compared to peers, and correlations between different data points.

Workflow and Lead Prioritization: The system incorporates a workflow for managing and prioritizing leads generated by the automated detection process. This helps investigators focus their efforts on the most promising cases.

Interactive Analysis: The graphical interface of the system allows investigators to interact with the data, explore relationships, and drill down into specific details for further analysis.

Most Important Ideas and Facts with Quotes:

Comprehensive Data Integration: The system is designed to handle data from diverse sources to build a holistic picture.

“[0035] System 900 comprises a data import component 915 which collects data from a variety of sources, including one or more of provider sources 911, insurer sources 912, public sources 913, and other sources 914…”

Data Transformation and Ontology: Data is transformed into a defined structure for analysis.

“[0035] The data may be collected from each source 911-914 on one or multiple occasions, depending on factors such as the size of the data source, the accessibility of the data source, and how frequently the data source changes. Depending on the form in which the data is collected, the data import component 915 may option perform Extract, Transform, and Load (“ETL”) operations on the collected data to generate objects that conform to one or more defined ontologies 990.”

Graph Generation and Visualization: Visualizing relationships is crucial for identifying fraud patterns.

“[0040] A graph generator component 940 generates graphs of networks identified based at least on the correlations identified by the correlation component 930. The graphs comprised linked nodes representing particular health care objects in the identified networks, including particular patient nodes representing particular patient objects and particular provider nodes representing particular provider objects.”

“[0038] Graphs produced by graph generator 940 are provided to an interface generator 960, which generates visual presentations of the graphs to display to a user in an interface 965.”

Automated Lead Identification based on Metrics and Triggers: The system uses data-driven methods to flag suspicious activity.

“[0041] Certain relationships and/or correlations of objects may suggest fraudulent activity. In an embodiment, an optional lead identification component 980 identifies “leads” for suspected fraudulent activity, in accordance with the techniques described subsequently.”

“[0028] In an embodiment, the method further comprises: computing values of metrics associated with the provider objects and metrics associated with the patient objects based at least in part on the correlating; comparing the values to defined triggers that define thresholds for unusual values; selecting the particular object at least partly responsive to the particular object being associated with a particular metric value that has an unusual value according to a particular defined trigger.”

Examples of triggers mentioned include: “a doctor writing significantly more prescriptions than normal; a sudden increase in prescriptions filled by a patient who was not previously filling many prescriptions; a patient receiving a significant amount of emergency room visits in a specific time period; a patient receiving prescriptions from more than a certain number of providers within a certain time period.” [0028]

Workflow and Prioritization: Leads are managed and prioritized for efficient investigation.

“[0044] The next stage is lead prioritization. There may be many possible leads to investigate, but limited resources to investigate such leads; lead prioritization enables focusing limited resources on the leads that are given higher priority.”

Interactive Interface for Analysis: Users can explore the data through the visual interface.

“[0030] In an embodiment, the method further comprises presenting the graph as part of an interactive interface for investigating health care data, the interface embedding a first control for selecting at least a particular linked node…”

“[0080] In an embodiment, the graph of block 780 is presented as part of an interactive interface for investigating health care data. The interface may embed a variety of controls within the graph that are activated by selecting various graph elements, including the nodes and edges.”

Additional Important Facts:

The patent application addresses the significant financial impact of healthcare fraud, citing an estimated $60-80 billion dollars/year in waste. [0004]

Prescription drug fraud is specifically highlighted as a source of fraud, with examples provided such as forging prescriptions, altering prescriptions, and shopping for prescriptions in various locations. [0004]

Other sources of fraud mentioned include insurance claims fraud, provider billing practices, durable medical equipment fraud, transportation fraud, and nursing home fraud. [0005]

The system utilizes an “Extract, Transform, and Load” (ETL) process to normalize data from various sources into a common model. [0035]

The graph can represent various entities as nodes, including members, doctors, criminal events, and organizations. [0119]

Edges in the graph can represent different types of relationships and can be color-coded to convey information. [0121, 0122]

The system can also generate timelines to visualize events over time, which can be correlated with the graphical representation. [0009, 0131]

The system can filter the graph based on various criteria to focus the analysis. [0039, 0140]

The system can generate workflow tickets for analysts to investigate identified leads. [0022, 0065]

The techniques described can be implemented on standard computer systems. [0150]

Potential Applications:

The system is primarily designed for detecting health care fraud, which can involve various entities such as:

Providers: Doctors, hospitals, clinics, pharmacies.

Patients: Individuals receiving healthcare services.

Insurers: Organizations paying for healthcare services.

Organizations: Medical practices, labs, etc.

Limitations (based on the provided excerpts):

While the document provides a detailed overview, specific algorithms and technical implementations for metrics and trigger definitions are not fully described in the excerpts. Further investigation would be required to understand the precise methods used for identifying unusual values and generating leads.

This briefing document provides a high-level overview of the key aspects of the GB 2514239 A patent application for a system designed to detect health care fraud.

Timeline of Main Events:

Prior to March 14, 2014: Palantir Technologies Inc. developed a system for detecting health care fraud. This system was described in US Provisional Application 61/801,470, filed March 15, 2013.

March 14, 2014: Palantir Technologies Inc. filed UK patent application GB 2514239 A titled “System for detecting health care fraud.”

November 19, 2014: The UK patent application GB 2514239 A was published.

2000-2010 (Example Timeline illustrated): An example timeline shows “Suspect Scripts” from Doctor B occurring, followed by “Doctor B Arrested” around 2005. Later, “Suspect Scripts” from Doctor C occur, followed by “Doctor C Arrested” around 2010. Other “Significant Events” are also indicated.

September 28, 2011: US patent application US 13/247,987 was filed.

November 5, 2012: US patent application US 13/669,274 was filed. (Both US applications describe examples of interactive graph-based interfaces and are incorporated by reference in the UK patent document).

Cast of Characters:

Palantir Technologies Inc.: The applicant of the UK patent application, a technology company based in Palo Alto, California, USA.

Lekan Wang: An inventor listed on the UK patent application.

Ken Shirriff: An inventor listed on the UK patent application.

Michael Wintio: An inventor listed on the UK patent application.

Christopher Ryan Luck: An inventor listed on the UK patent application.

Kitsune IP: The service address listed for the applicant in the UK patent application, located in London, UK.

Members X, Y, and Z: Example individuals who are part of a health plan and have received prescriptions. Member Z may have received suspect prescriptions from Doctors B and C.

Doctors A-G: Example doctors, some of whom (B and C) are associated with criminal events (arrests) and suspect prescriptions. Doctor G wrote a regular prescription for Member Z. Doctor F may have written a suspect prescription for Members X and Y and may be questioned. Doctor D wrote a prescription for Members B and C. Doctor E wrote a prescription for Member Y. Doctor A wrote a prescription for Members X, Y, and Z.

Organization K and G: Example organizations. Doctor F is associated with Organization K and wrote a suspect prescription. Doctor G is associated with Organization G and wrote a regular prescription.

Organization J: An example organization that Doctors C, D, and E belong to.

Owner of Organization K: An individual identified as the owner of Organization K, potentially relevant to investigating fraud associated with Organization K.

Owner 861: Example owner of Retail Pharmacy 851 and possibly related to family. Pharmacy 836 is also immediately related to patient node 810, and identified as related to owner 861.

Owners 862-864: Example owners of various pharmacies (831-835). Relationships are linked by phone records, employer/employee relationships, and/or address relationships.

Pharmacist 865 and 866: Example pharmacists linked to Pharmacy nodes 831-835 and their owners 862-864.

Pharmacist 867-869: Example pharmacists linked to Pharmacy nodes 831-835 and their owners 862-864, possibly representing possible familial relationships or possible identity relationships.

Michael…Pharmacist 871: Example individual who is a pharmacist and was arrested involving forging prescriptions.

Paul F…882: Example individual who was arrested involving pharmacy fraud and possessing controlled substances.

Patient node 810: An example patient object in a graph illustrating health care fraud investigation techniques.

Pharmacy nodes 831-835: Example pharmacy objects connected to patient node 810, representing various relationships associated with “two pharmacy claims” and “5 pharmacy claims.”

It’s important to note that the “Characters” section is based on the examples provided in the patent document to illustrate the system’s capabilities in identifying and visualizing relationships between different entities involved in healthcare fraud. Most are example entities used for demonstration purposes.

Patent 5

Dr. Timothy Earl King’s patent application (US 20200143925A1) describes a forensic system and method designed to detect fraud, abuse, and diversion in the prescriptive use of controlled substances. Here’s a breakdown of the methodologies outlined in the document:

1. Forensic Chronology Development

Data Integration: The method starts with integrating data from patients’ medical charts with their prescription drug data from the Prescription Drug Monitoring Program (PDMP).

Manual Data Entry: Data is manually summarized and entered into an Excel spreadsheet. This includes chronological entries for:

Prescribed drugs

Clinical rationale

Diagnostic foundation

Past medical history

Clinical response

Color Coding: To enhance readability and analysis, each type of entry (like office visits, hospitalizations, drug tests, etc.) is color-coded. For example:

Blue for prescriptions by the target doctor

Pink for prescriptions by other doctors

Green for urine drug screens (UDS)

Purple for imaging

White for nursing notes

Orange for hospital or ER visits

Chronological Sorting: All data is sorted by date to create a timeline of medical care and prescribing practices, helping to identify patterns of abuse or irregular practices.

2. Forensic Summary

Data Compilation: This step involves creating a summary of the data from the Forensic Chronologies of multiple patients.

Standards of Care: The summary is organized around six standards of care:

Medical diagnosis

Support of diagnosis

Risk assessment

Treatment plan

Procedures

Compliance/Enforcement/Outcome

Opiate Parameters and Queries: Each standard is further broken down into specific opiate parameters and targeted queries (total of 15) to assess the legitimacy of controlled substance use. This involves evaluating aspects like:

Objective diagnosis documentation

Clinical history

Risk factors including mental health and medical co-morbidities

Treatment plan adequacy

Compliance with drug monitoring practices

3. Standard of Care Summary

Evaluation of Compliance: This is the final product where each standard of care is evaluated for compliance.

Notation System:

✓ indicates the standard is met

0 indicates documentation is lacking

X indicates a standard violation

XX indicates an egregious violation

Summary Outcome: This one-page document provides a clear, concise summary to determine if federal or state laws regarding controlled substance prescriptions have been violated.

4. Analytical and Legal Application

Objective Analysis: The methodologies aim to reduce subjectivity in medical and legal reviews by translating complex medical data into objective, quantifiable criteria.

Legal Support: These documents can be used in legal contexts to provide evidence in cases where a practitioner might be suspected of prescribing controlled substances outside the usual course of medical practice or for non-legitimate medical purposes.

Additional Notes:

Training and Education: The method also includes potential for training physicians in proper standards of care regarding controlled substance prescriptions.

Expert Witness Role: Dr. King himself is noted as having experience as an expert witness, which underscores the practical application of this method in legal proceedings.

This system is intended to provide a structured, systematic approach to scrutinize the prescribing practices of medical professionals, ensuring they align with legal and ethical standards.

United States

(12) Patent Application Publication (10) Pub. No.: US 2020/0143925 Al

King (43) Pub. Date: May 7, 2020

(54) FORENSIC SYSTEM AND METHOD FOR

DETECTING FRAUD, ABUSE, AND

DIVERSION IN THE PRESCRIPTIVE USE OF

CONTROLLED SUBSTANCES

(71) Applicant: Timothy Earl King, Carmel, IN (US)

(72) Inventor: Timothy Earl King, Carmel, IN (US)

(21) Appl. No.: 16/666,971

(22) Filed: Oct. 29, 2019

Related U.S. Application Data

(60) Provisional application No. 62/755,605, filed on Nov.

5, 2018.

Publication Classification

(51) Int. Cl.

G16H 20/10 (2006.01)

G16H 10/60 (2006.01)

G16H 15/00 (2006.01)

(52) U.S. Cl.

CPC G16H 20/10 (2018.01); G16H 15/00

(2018.01); G16H 10/60 (2018.01)

(57) ABSTRACT

The present invention relates to a forensic system and

method, including a methodology, for analyzing medical and

pharmacy data to determine the legitimacy of controlled

substance prescription and use, and to detect fraud, abuse,

and/or diversion of controlled substances. In one aspect, said

method provides objective evidentiary data that shows with

high certainty whether a suspected medical practitioner has

been issuing controlled substance prescriptions “outside the

usual course of medical practice” and/or “for other than

legitimate medical purposes”. Said method rearranges, organizes,

and simplifies the complex data in patients’ medical

and PDMP charts, and provides a series of three sequential

work products in spreadsheet form, a Forensic Chronology

chart, a Forensic Summary chart, and a Standard of Care

Summary chart, via which medical data is effectively translated

into objective criteria from which a legal conclusion

can be determined by legal scholars, prosecutors, defense

attorneys, and juries.

Here is a comparative analysis of the five patents, focusing on their purposes, methodologies, and applications in addressing issues related to controlled substances, healthcare fraud, and medical documentation:

---

### **1. US 8,688,477 B1 (NarxCare)**

– **Purpose:** To generate a “narcotics use indicator” (a numerical score) assessing a patient’s risk of improper prescription drug use, particularly narcotics and controlled substances.

– **Methodology:**  

  – Integrates patient-specific prescription data (e.g., prescriber, drug type, quantity) with general population data.  

  – Calculates weighted indicators (usage, instruction, dispensing, auxiliary) and combines them into a final risk score.  

  – Employs statistical analysis (percentiles) and automated weighting (e.g., morphine equivalents weighted 4x higher).  

– **Applications:**  

  – Aids healthcare providers in quickly identifying high-risk patients.  

  – Addresses underutilization of Prescription Monitoring Programs (PMPs) due to cumbersome manual processes.  

– **Strengths:** Rapid (5-second processing), data-driven, and scalable.  

– **Limitations:** Relies heavily on prescription history; may not capture contextual factors like patient behavior.

—

### **2. JP 2019-513504 A (Opioid + Device Combination)**

– **Purpose:** Improve safety and efficacy of opioid administration through an integrated system combining a drug dispenser, app, and data analytics.  

– **Methodology:**  

  – Uses biometric authentication (e.g., fingerprint) to prevent diversion.  

  – Monitors patient compliance via real-time data (self-assessments, physiological metrics).  

  – Alerts providers for anomalies (e.g., missed doses, overdose risks).  

– **Applications:**  

  – Reduces opioid misuse by controlling dispensing and enabling remote monitoring.  

  – Supports personalized treatment plans.  

– **Strengths:** Holistic approach (hardware + software), emphasis on patient engagement.  

– **Limitations:** Requires patient cooperation; high implementation cost.

—

### **3. WO 2015/167852 A1 (Copy-Paste Detection in EHRs)**

– **Purpose:** Identify and analyze copied-pasted text in medical documents to prevent inaccuracies and fraud.  

– **Methodology:**  

  – Compares text strings across documents using NLP and risk thresholds (e.g., >90% similarity).  

  – Flags high-risk passages (e.g., conflicting diagnoses, outdated info) for review.  

– **Applications:**  

  – Ensures documentation accuracy in Electronic Health Records (EHRs).  

  – Mitigates billing fraud and legal risks.  

– **Strengths:** Integrates with existing EHR workflows; scalable.  

– **Limitations:** May generate false positives; relies on manual review for corrections.

—

### **4. GB 2514239 A (Healthcare Fraud Detection)**

– **Purpose:** Detect healthcare fraud (e.g., prescription forgery, billing fraud) using data analytics.  

– **Methodology:**  

  – Aggregates data from providers, insurers, and pharmacies into a graph-based ontology.  

  – Applies triggers (e.g., unusual prescription patterns) to flag suspicious activity.  

  – Visualizes relationships (e.g., patient-doctor-pharmacy networks).  

– **Applications:**  

  – Investigative tool for insurers and law enforcement.  

  – Prioritizes leads for fraud cases.  

– **Strengths:** Comprehensive data integration; interactive visualizations.  

– **Limitations:** Requires high-quality data; complex to implement.

—

### **5. US 2020/0143925 A1 (Forensic System for Controlled Substances)**

– **Purpose:** Forensic analysis of prescribing practices to detect fraud, abuse, or diversion of controlled substances.  

– **Methodology:**  

  – Creates chronological timelines from medical charts and PDMP data.  

  – Evaluates compliance with six standards of care (e.g., diagnosis documentation, risk assessment).  

  – Uses color-coding and scoring (✓/X) for objective legal conclusions.

 

– **Applications:**  

  – Legal evidence for prosecuting non-compliant practitioners.  

  – Training tool for physicians.  

– **Strengths:** Standardized, legally actionable output.  

– **Limitations:** Labor-intensive (manual data entry); subjective interpretation of standards.

### **Key Takeaways**

1. **Diverse Approaches:** The patents address overlapping issues (e.g., opioid misuse) but from different angles—clinical decision support (NarxCare), device-based control (Opioid Device), documentation integrity (Copy-Paste), fraud detection (Healthcare Fraud), and legal forensics (Forensic System).  

2. **Automation vs. Manual Analysis:** NarxCare and Healthcare Fraud leverage automation for scalability, while the Forensic System relies on manual curation for legal precision.  

3. **Interoperability Challenges:** Integration with existing systems (EHRs, PDMPs) is a common thread, highlighting the need for standardized data formats.  

These innovations collectively advance the fight against prescription drug abuse and healthcare fraud, though their effectiveness depends on implementation and adoption.

Comparative Summary

Patent	Focus	Key Innovation	Data Sources	Output
NarxCare	Risk scoring	Weighted narcotics use indicator	PMP data, population statistics	Numerical risk score
Opioid Device	Safe dispensing	Biometric-controlled dispenser + app	Patient inputs, physiological data	Real-time alerts
Copy-Paste Detection	EHR accuracy	NLP-based plagiarism detection	Medical documents	Flagged high-risk passages
Healthcare Fraud	Fraud detection	Graph-based relationship mapping	Claims, prescriptions, public data	Visualized fraud networks
Forensic System	Legal compliance	Chronological standards evaluation	Medical charts, PDMP data	Standardized compliance report