Document Type
Article
Publication Date
2024
Publication Citation
103 North Carolina Law Review 187
Abstract
In response to America’s escalating drug poisoning crisis, the federal government has funded, incentivized, and mandated that states adopt and implement prescription drug monitoring programs (“PDMPs”) to electronically surveil controlled substances and other “drugs of concern.” State PDMPs utilize proprietary, predictive software platforms that deploy algorithms to determine whether a patient is at risk for drug misuse, drug diversion, doctor shopping, or substance use disorder. PDMPs have never been validated by a federal agency or peer review, yet states have mandated their use throughout the health care delivery system.
Research demonstrates that clinical overreliance on the risk scores generated by PDMP algorithms motivates clinicians to refuse to treat—or to inappropriately treat—marginalized and stigmatized patient populations, including individuals with actual or perceived substance use disorder, chronic pain conditions, or other disabilities. The misuse of information generated by PDMP algorithms by healthcare providers is anticipated to impact over one billion patient encounters each year. This Article provides a framework for challenging such PDMP algorithmic discrimination as disability discrimination. It contends that Section 504 of the Rehabilitation Act, the Americans with Disabilities Act, and Section 1557 of the Affordable Care Act can be engaged to protect vulnerable patients from PDMP-related algorithmic discrimination. It then provides recommendations to develop and strengthen the 2024 Section 1557 final rule concerned with clinical-decision algorithmic discrimination, harmonize new and existing antidiscrimination protections, and improve implementation and enforcement efforts in this context.
Recommended Citation
Elizabeth Pendo & Jennifer D. Oliva,
Disability Discrimination by Clinical Algorithm,
103 North Carolina Law Review 187
(2024).
Available at:
https://www.repository.law.indiana.edu/facpub/3133
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