Modeling Instrument Design Risks in Medical Device Reprocessing

Medical device reprocessing is an essential practice in healthcare delivery. While FDA regulations require device manufacturers to develop and validate reprocessing procedures, the increasing complexity of medical device designs and their use environments can increase the risks of inadequate reprocessing and associated device malfunctions and potential patient injury. While some design features present known difficulties for reprocessing, critical information needed to define relative risks or feature-specific parameters that increase or decrease risks of inadequate reprocessing is lacking. There is an urgent need for tools to quantify and predict reprocessing risks associated with design features used in surgical instruments. The broad objective of this dissertation is to assess reprocessing risk in surgical instrument design. This work will inform a predictive risk model that, if broadly implemented, would enable a more objective, quantitative approach to risk assessment at the design feature level. The experimental work in this dissertation was completed in three specific aims. Aim 1 developed and fabricated standard test pieces (STP) by characterizing design features on a set of 42 common surgical instruments. STPs were manufactured from stainless steel materials representing standard surgical instruments and were characterized by measuring geometry, dimensions, and surface roughness. Specifically, the STPs emulated crevice design features characterized from the surgical instruments. Aim 2 was accomplished by designing and executing bioburden retention studies while varying the STP crevice dimension. STPs were contaminated with known quantities of a standard test soil (coagulated blood) and residual amounts of the test soil after controlled STP immersion were measured based on standard analyte measurement methods. Two analytes relevant to the test soils were detected, including total organic carbon measured in an automated chemical analyzer and proteins measured using established microbiological assays. Aim 3 developed a predictive model based on risk analysis to identify and define high-risk features. Experimental results from Aim 2 were analyzed to identify relationships between design feature dimensions and amounts of residual bioburden. A framework for further computational modeling was developed with design parameters as independent variables. Availability of a tool to predict reprocessing risk will allow engineers to make design decisions that help to lower the risk of improper reprocessing and contribute to safer medical devices.