Abstract

In the last decade, a number of high profile medical device recalls have drawn attention to the regulatory approval process, particularly the streamlined process for devices considered “lower risk” known as the 510(k). Approval of medical devices through the 510(k) Process is not based on clinical data, but rather on “substantial equivalence” to predicate devices approved pre-1976 or legally marketed thereafter. A predicate device is one that shares the same intended use as the new device and technological characteristics which are either the same or different without introducing new safety hazards. Many scholars believe that the premise of approving medical devices based on similarity to existing devices is inherently flawed. In particular, there is worry that presence of technology creep between predicate devices can lead to the approval of medical devices which ultimately do not resemble the original device for which clinical evidence exists, even as that evidence is used to validate device safety.

Given these concerns about the safety of the established regulatory process, this thesis explored the impact of predicate creep within the 510(k) Process through a case study of a Robotic Assisted Surgery (RAS) devices, with particular focus on the Intuitive Surgical Da Vinci Surgical System. Through the development of new methodologies using publicly available data to measure predicate creep, this research traces the predicate ancestry of several RAS devices to assess the current impact and implications of predicate creep on the current regulatory process. The study concludes that there is significant evidence of predicate creep within the approval process and recommend new guidelines for classifying device risk and subsequent evidentiary requirements within the 510(k) Process, to reduce the number of devices with high levels of potential risk to public safety released onto the market.

Library of Congress Subject Headings

Medical instruments and apparatus--Government policy--United States; Surgical robots--Government policy--United States

Publication Date

5-2018

Document Type

Thesis

Student Type

Graduate

Degree Name

Science, Technology and Public Policy (MS)

Department, Program, or Center

Public Policy (CLA)

Advisor

Sandra Rothenberg

Advisor/Committee Member

Cristian A. Linte

Advisor/Committee Member

Qing Miao

Campus

RIT – Main Campus

Plan Codes

STPP-MS

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