Dosing Discrimination: Regulating PDMP Risk Scores
69 Pages Posted: 19 Jan 2021 Last revised: 2 Mar 2022
Jennifer D. Oliva
Indiana University Maurer School of Law; Georgetown University Law Center; UCSF/UC Law Consortium on Law, Science & Health Policy
Date Written: January 18, 2021
Abstract
Prescription drug monitoring program (PDMP) predictive surveillance platforms were designed for—and funded by—law enforcement agencies. PDMPs use proprietary algorithms to determine a patient’s risk for prescription drug misuse, diversion, and overdose. The proxies that PDMPs utilize to calculate patient risk scores likely produce artificially inflated scores for marginalized patients, including women and racial minorities with complex, pain-related conditions; poor, uninsured, under-insured, and rural individuals; and patients with co-morbid disabilities or diseases, including substance use disorder and mental health conditions.
Law enforcement conducts dragnet sweeps of PDMP data to target providers that the platform characterizes as “overprescribers” and patients that it deems as high risk of drug diversion, misuse, and overdose. Research demonstrates that PDMP risk scoring coerces clinicians to force medication tapering, discontinue prescriptions, and even abandon patients without regard for the catastrophic collateral consequences that attend to those treatment decisions. PDMPs, therefore, have the potential to exacerbate discrimination against patients with complex and stigmatized medical conditions by generating flawed, short-cut assessment tools that incentivize providers to deny these patients indicated treatment.
The Federal Food and Drug Administration (FDA) is authorized to regulate PDMP predictive diagnostic software platforms as medical devices, and the agency recently issued guidance that provides a framework for such oversight. Thus far, however, the FDA has failed to regulate PDMP platforms. This Article contends that the FDA should exercise its regulatory authority over PDMP risk scoring software to ensure that such predictive diagnostic tools are safe and effective for patients.
Keywords: risk scoring, algorithms, opioids, opioid use disorder, prescription drugs, PDMPs, surveillance, pain, patients, FDA, DEA
Suggested Citation: Suggested Citation
Jennifer Oliva (Contact Author)
Indiana University Maurer School of Law ( email )
211 S. Indiana Avenue
Bloomington, IN 47405
United States
HOME PAGE: http://law.indiana.edu/about/people/details/oliva-jennifer-d.html
Georgetown University Law Center ( email )
600 New Jersey Avenue NW
Washington, DC 20001
United States
HOME PAGE: http://oneill.law.georgetown.edu/experts/jennifer-oliva/
UCSF/UC Law Consortium on Law, Science & Health Policy ( email )
200 McAllister Street
San Francisco, CA 94102
United States
HOME PAGE: http://www.uchastings.edu/people/jennifer-d-oliva/
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