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
This article explores the complexities and controversies surrounding Prescription Drug Monitoring Programs (PDMPs), particularly their reliance on machine learning and predictive modeling to identify potential drug abuse and diversion.
A key theme is the inherent bias in these systems, as they are primarily designed to detect behaviors associated with harm, fraud, waste, and abuse, while often failing to account for appropriate opioid use, patient benefits, or positive outcomes.
The source highlights concerns that these algorithms, and tools like NarxCare, may be misbranded medical devices due to a lack of validation for their predictive scores and inadequate transparency regarding their methodologies, potentially leading to harmful outcomes for patients and healthcare providers.
Additionally, the youtube video presentation “I’m Only Human touches upon the historical context of PDMP development, the influence of groups advocating for stricter opioid policies, and the potential for PDMP data sharing with third-party payers to reduce costs but also raise privacy and data misuse concerns.
THE CORPORate overlords willing to sell out the truth
Attorney Ron Chapman recently articulated
” As the only Marine in my unit with a law degree and a deep appreciation for civil liberties, Palantir’s capacity for enabling civil rights abuses stood out to me immediately. We outsourced critical decisions to a proprietary algorithm…When it worked, it unquestionably saved lives.
But you become addicted to that sense of “knowing the future,” even when it’s just sophisticated pattern recognition. We believed we were chasing the Taliban, at least.
Certainly a far cry from prescribing medications. Yet years later, when Palantir’s sales teams stormed government contracting offices bragging that their system could replicate its counterinsurgency successes in Medicare fraud detection, I was deeply alarmed.
Conclusion:
PDMPs represent a complex and evolving tool in addressing prescription drug abuse and improving patient safety. While evidence indicates their potential effectiveness in specific areas like reducing doctor shopping and informing prescribing, significant concerns remain regarding their use as surveillance tools, the potential for algorithmic bias, and the lack of standardization in key metrics.
“SCORE TOO HIGH-NOT CANCER PAIN”
Future efforts should focus on enhancing data quality, ensuring transparency in algorithmic processes, establishing best practices for data sharing with third-party payers with appropriate safeguards, and continuing to evaluate the impact of PDMPs on both public health and individual patient care.
The nuances in calculating MME per day and the potential for misclassification highlight the need for careful consideration in policy and enforcement related to opioid prescribing.
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