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17 - Compulsory Medical Device Registries

Legal and Regulatory Issues

from Part V - Medical and Legal Oversight of Medical Devices

Published online by Cambridge University Press:  31 March 2022

I. Glenn Cohen
Affiliation:
Harvard Law School, Massachusetts
Timo Minssen
Affiliation:
University of Copenhagen
W. Nicholson Price II
Affiliation:
University of Michigan, Ann Arbor
Christopher Robertson
Affiliation:
Boston University
Carmel Shachar
Affiliation:
Harvard Law School, Massachusetts

Summary

Physicians are entrusted as the sole authorities to assess the competence of fellow physicians, and peer review is the primary pillar in assuring medical quality. A new era of intelligent tools may serve as the death knell for this insular physician-led, self-regulatory process. Increasingly, medical decision-making relies on advanced decision support with AI, and algorithms are asked to triage and prioritize patient care semi-autonomously. These technologies often lie beyond physician expertise, limiting the ability of any review process to determine where fault lies. Since machine learning may identify data patterns that guide treatments where there is current clinical equipoise, the best course of treatment may be swayed by information a physician may not have considered prima facie relevant, or by information that lies beyond published literature. Peer review committees determining if an error occurred will have less information than the machine that directed treatment. Our chapter discusses these challenges; explores implications that intelligent tools have on the organization of medicine and its structures of authority; considers the need for maintaining physician self-regulation; alternative legal approaches to maintaining quality assurance; and proposes new approaches to peer review and quality assurance for an era of medicine that utilizes these smart machines.

Information

Figure 0

Table 17.1 Hypotheses

Figure 1

Table 17.2 Specifications and study goals

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