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. C.T. VIVIAN, JELANI ZIMBABWE CLEMENT, BS., MBA., IN THE SPIRIT OF THE HON. PATRICE LUMUMBA, IN THE SPIRIT OF ERLIN CLEMENT SR., WALTER F. WRENN III., MD., JULIE KILLINGWORTH, LESLY POMPY MD., NANCY SEEFEDLT, WILLIE GUINYARD BS., JOSEPH WEBSTER MD., MBA, BEVERLY C. PRINCE MD., FACS., NEIL ARNAND, MD., RICHARD KAUL, MD., LEROY BAYLOR, JAY K. JOSHI MD., MBA, 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
“FIGHTING AGAINST HEALTHCARE INJUSTICE”
THANK YOU RICHARD LAWHERN PH.D. AND NANCY SEEFEDT, PAIN ADVOCATE
‘ IF YOU EVER THINK YOU ARE TOO SMALL TO MAKE A CHANGE THEN YOU’VE NEVER SLEPT WITH A MOSQUITO’
CATHLEEN LONDON, MD CIPP/US, Class of 2022
Equifax just completed the acquisition of Appriss Insights, who is rebranding as Bamboo Health. How much data sharing goes on between the entities? Just as Appriss’ NarxCare score is a black box, never subjected to peer review or outside scrutiny, this reorganization seems designed to hide data sharing. Monitoring of controlled substance prescribing is a recent phenomenon that owes its appearance to the opioid epidemic.
Doctors are now put in a position to be law enforcement, counterintuitive to their training. As medical students, physicians learn to conduct a history and physical. The patient’s story is a centerpiece of the history, and there is a truth bias. Physicians are being asked to doubt what patients tell them and approach each encounter as someone who is “drug-seeking.” This harms the physician-patient relationship directly.
“NarxCare scoring failS FDA safety and effectiveness criteria”
There is a historic conflict around what constitutes the practice of medicine, particularly in regard to physician prescribing of controlled substances. With the passage of the Harrison Narcotics Act in 1914, Congress sought to address the non-medical use of narcotics.
Drafted as tax law, the Harrison Act required anyone authorized to manufacture or distribute narcotics to register with the Treasury Department, pay a fee and keep records. For the first time, possession, use, and distribution of narcotics were criminalized. Physicians were easier than unlicensed distributors to target and bring to court.
A series of Supreme Court decisions transformed narcotics control from tax revenue to a cabining of physician prescribing. Prescribers could no longer treat patients with their drugs of choice to prevent withdrawal, as addiction was viewed as a vice. The first argument to allow alleviation of pain and suffering was Linder v. United States, when prescribing for withdrawal symptoms was permitted.
YOUR MEDICAL PRESCRIPTION DATA IS NOW USED TO VIOLATE YOUR CONSTITUTIONAL RIGHT IN EVERY PHARMACY IN AMERICA
“..PDMPs have not shown that they reduce overdose deaths nor improve patient outcomes“
BACKGROUND OF CONTROLLED SUBSTANCE ACT (CSA)
In 1968, Congress established the Bureau of Narcotics, housed in the Justice Department for the enforcement of federal drug laws. The Controlled Substance Act (CSA) was passed in 1970, beginning the accelerated ‘War on Drugs’. The CSA created five schedules of controlled substances based upon medical use and abuse potential.Prescribers were now required to register with the Attorney General, the law required that prescriptions “must be for a legitimate purpose acting in the usual course of professional practice.”[14
The CSA created a closed chain for controlled substance distribution which was designed to monitor legal products as they were transferred among DEA registrants to prevent diversion to the illicit market. The DEA manages diversion by maintaining strict control over the availability of substances through quotas, registration, record keeping, and security requirements from manufacturer to patient.
The DEA has a way to track suspicious ordering without the need to resort to protected health information (PHI), and has since the initiation of the CSA. The DEA is responsible for the production numbers of opioid quotas.
YOUR PHARMACY IS NOW A PART OF LAW ENFORCEMENT
Prescription Drug Monitoring Programs (PDMP) began on paper as a set of law enforcement tools. The first program, in New York in 1918, was rescinded after three years. California started one through the Bureau of Narcotic enforcement in 1939 followed by Hawaii in 1943.
When Illinois chose to begin one in 1961, it was housed in the Department of Health. As other states began their programs, all were used for Schedule II drugs and required duplicate or triplicate prescription forms that relied on tracking serial numbers.
In 1977, the Supreme Court ruled in Roe v. Whalen that these PDMPs were not unconstitutional. The Court felt that PDMPs did not violate confidentiality and were part of state police powers. This ruling was based on paper, static PDMPs with very limited information. In 1990, Oklahoma was the first state to mandate electronic transmission of PDMP data.From 2000-2017, twenty-seven electronic PDMPs were established.
In 2010, five states had mandatory prescriber query laws; by 2021, forty states had mandatory query laws. Forty-seven states allow interstate sharing of data. Unlike their paper predecessors, today’s PDMPs have a wealth of personal information. They track Schedule II-V drugs and some track unscheduled medications. Prescriptions reveal information from diagnosis to location.
Only Missouri does not currently have a PDMP. Twenty are housed in the Board of Pharmacy, nineteen in the Department of Health, six in professional licensing agencies, five in law enforcement, three in substance abuse agencies, and one in a consumer protection agency. In addition to scheduled drugs, they track “drugs of concern.”
Many have alternate data from child welfare cases, drug courts, drug arrests and convictions, medical marijuana dispensing, Narcan dispensing, disciplinary information of registrants, and lost or stolen drug reports. Insurance companies and marijuana dispensaries are being given access to PDMPs. Thirty-eight PDMPs give prescribers an unsolicited report card comparing them to other prescribers.
Forty-two of the fifty-two PDMPs have Appriss’ algorithm embedded within them, which uses the “NarxCare” score, a three-digit score, for narcotics, sedatives, and stimulants. It leverages a black-box algorithm that has never been subject to outside or peer evaluation. The ‘NarxCare’ patent was originally from a 2011 filing by the National Boards of Pharmacy.
KNOW YOUR NARXCARE SCORE
When the patent was renewed in 2015 it was transferred to Appriss. All of the validation of NarxCare was internal, retrospective, case-control studies of Ohio data from 2009-2015. Appriss claims to be a clinical support tool and on the website markets NarxCare as “Up Front, Every Patient, Every Time”, but only reveals some of the data used to generate the score:
- Number of prescribers;
- Number of pharmacies;
- Amount of medication;
- Presence/amount of potentiating medication
- Number of overlapping prescriptions
Appriss is seeking access to CLUE (an auto database), SIRIS (a banking database), and MIDEX (a real estate database) which the recent purchase by Equifax makes likely.Appriss asserts that their internal studies “validate the NarxCare scores.
Such self-serving assertions hardly quell the concerns identified, the initial innovator of a black-box software platform faces strong financial incentives not to disprove its own algorithm.” Their model fails transparency. It is a retrospective cohort which means selection bias and often errors of conclusion (correlation is not causation). In a retrospective study, there are too many confounding variables.
PDMP SYSTEM DANGEROUSLY FRAUGHT WITH MASSIVE ERRORS
The study population was selected for having the targeted health outcome which confounds the contextual information and is not accounted for in the study population.
In constructing the NarxScore, no alternative hypotheses were accounted for. They had a lack of independence and had an overarching assumption which puts great limits on the data integrity.
NarxCare is also based solely on data from Ohio which then creates questions about its generalization to expand beyond that geographic region. Appriss did not disclose any tests of reliability and validity. Algorithms need post-marketing surveillance audits.
The Odds Ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.
Risk is a probability, a proportion of those exposed with an outcome compared to the total population exposed. An OR of 10.1 means there is a 1010% increase in the odds of an outcome with a NarxCare (Overdose Risk) score of 200-290, and so forth.
Looking at Table 2 From the Appriss White Paper pictured below, we see that in Ohio the Overdose Risk 0-190 represents an OR of 1.0; Overdose Risk 200-290 OR = 2; Overdose Risk 300-390 OR = 4; Overdose Risk 400-490 OR = 8; Overdose Risk 500-590 OR = 14; Overdose Risk 600-690 OR = 24; Overdose Risk 700-790 OR = 38; Overdose Risk 800-890 OR = 72; and Overdose Risk 900-990 OR = 417.
Importantly, this is not the same as saying a multiplication of the likelihood of an outcome. Rather this is a measure of a chance that a projected likelihood will occur.
The confidence interval (CI) is the 95% probability that the true OR (chance) would be likely to lie between the upper and lower limits, assuming there is no bias or confounding in the data. Confidence intervals are a general guide to the amount of random error in the data.
The width of the CI indicates the amount of random error in an estimate. Pictured below, for Overdose Risk of 200-290 with an OR of 2, the true OR is 10.1 with a 95% CI of (7.8, 13); for Overdose Risk of 300-390 with OR of 4 the true OR is 10.0 with CI (7.7, 12.9); for Overdose
Risk of 400-490 with OR 8 the true OR is 16.3 with CI (12.7, 20.9); for Overdose Risk of 500-590 with OR 14 the true OR is 31.7 with CI (24.7, 40.6); for Overdose Risk 600-690 with OR 24 the true OR is 56.1 with CI (43.1, 73); for Overdose Risk 700-790 with OR 38 the true OR is 76 with CI (55.9, 103.3); for Overdose Risk 800-890 and OR 72 the true OR is 101.3 with CI (66.2, 155.2) and finally for Overdose Risk 900-990 with OR 417 the true OR is 168.1 with CI (48, 588). These are large errors.
The Appriss NarxCare model overpredicts overdose risk. Highlighted in the graph above are the errors at 56%, 60%, and 125%. A calculated 54 times means where 90 MME (morphine equivalents) it should really be reflected as 4500 MME. As a comparison, an estimated 200,000 dead from COVID-19 would be 10,000,000 dead.
The Appriss model is a smart database that purports to use artificial intelligence to predict an individual’s probability of developing opioid use disorder. The NarxCare predicted risk scores do not appear to correlate with the individual-specific treatment effect of receiving opioids.
Professor Kilby, an economics professor, constructed an algorithm similar to Appriss and used a more comprehensive database. There is inherently algorithmic unfairness in machine learning applications arising from the researcher’s choice of the objective function.
The algorithm identifies high risk for opioid use disorder based on a few key demographic characteristics thereby flagging complex chronic pain patients with comorbidities as high risk. Models trained with the typical risk-prediction objective function do not produce a valid proxy for the object of interest: patient-level heterogeneous treatment effects.
The algorithm falsely discriminates against rural patients, those who have suffered trauma, having multiple prescribers due to no fault of their own (especially now that most doctors are employees) or relocation due to jobs, and cash payments due to indiscriminate need of prior authorizations.
The algorithm falsely sees these variables as doctor shopping and indicators of drug diversion or substance abuse.
–TO BE CONTINUED–
CONGRESS MUST CLEAN UP THIS MESS
FOR NOW, YOU ARE WITHIN