A Critical Analysis of FDA Guidance for User Percentile Device Design Criteria Versus Currently Available Human Factors Engineering Data Sources and Industry Best Practices

15 Pages Posted: 25 Jun 2019 Last revised: 2 Jul 2019

See all articles by Charles Mauro

Charles Mauro

Mauro Usability Science

Peter Pirolli

Institute for Human and Machine Cognition

Chris Morley

Mauro Usability Science

Date Written: June 27, 2019

Abstract

A staggering report was recently released by the International Consortium of Investigative Journalists (ICIJ) indicating that 83,000 deaths and 1.7 million injuries were linked to medical device adverse events reported over the last decade (Díaz-Struck, 2018). Furthermore, a 2013 report by McKinsey & Company suggested that such adverse events and associated quality issues cost the medical device industry between $2.5 billion and $5 billion per year on average (Fuhr, George, & Pai, 2013). There is also indication that some aspects of both medical device human factors engineering (HFE) design and Food and Drug Administration (FDA) HFE validation criteria may not provide proper methodologies for selecting and testing patient populations. This may lead medical devices that pass FDA HFE validation to still exhibit significant use errors when utilized by actual patients in the real world. This paper explores the possible sources of HFE performance problems with medical devices and links potential problems to HFE validation methodologies within the FDA HFE guidance framework. The paper also examines device design teams’ failure to utilize HFE best practices in the design and testing of medical devices.

Critical conclusions drawn from this analysis suggest that, currently, medical devices may not be designed and validated according to the full percentile range recommended by FDA guidance and HFE best practice. The analysis also indicates that both the FDA and medical device design teams have failed to understand that designing devices to meet a specific range of user needs includes not only physical attributes, but also a wide range of percentile-based cognitive and physiological HFE performance variables. This problem is potentially causing patients who reside at the extremes of percentile distributions (i.e., those with extreme, but acceptable degrees of functional and/or cognitive limitations) to not be able to use devices successfully, which may significantly impact clinical outcomes. The analysis additionally indicates that such failure to design for proper percentile extremes is related to respondent recruiting methods and, more generally, a lack of awareness regarding HFE theory and best practice. Furthermore, for certain categories of modern medical devices, including drug delivery devices, little to no validated HFE percentile-based device design data exists to facilitate medical device development. These issues are not limited to the design of medical devices, but also apply to the design of secondary components of the device system, including device packaging, labeling and instructions for use. To resolve such issues, the authors propose generation of an HFE medical device design criteria dataset for use by development teams during design and testing. The FDA may also reference the proposed dataset during HFE review and validation.

Keywords: medical device design, drug delivery device design, human factors engineering, anthropometry, design criteria, user percentile design, product design, HFE design criteria, FDA HFE testing, FDA human factors testing guidance, AAMI/ANSI HE75

Suggested Citation

Mauro, Charles and Pirolli, Peter and Morley, Chris, A Critical Analysis of FDA Guidance for User Percentile Device Design Criteria Versus Currently Available Human Factors Engineering Data Sources and Industry Best Practices (June 27, 2019). Available at SSRN: https://ssrn.com/abstract=3408117 or http://dx.doi.org/10.2139/ssrn.3408117

Charles Mauro (Contact Author)

Mauro Usability Science ( email )

23 East 73rd Street
Suite 5F
New York, NY 10021
United States

Peter Pirolli

Institute for Human and Machine Cognition ( email )

40 S Alcaniz St.
Pensacola, FL 32502
United States

Chris Morley

Mauro Usability Science ( email )

23 East 73rd Street
Suite 5F
New York, NY 10021
United States

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