Regulatory Navigation: A Digital Tool to Understand Medical Device Classification Pathways

Bergmann, Jeroen; Hendricusdottir, Rita; Lee, R

doi:10.1016/b978-0-444-64046-8.00287-1

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Regulatory science itself is evolving as well, and new tools are being created to help innovators to navigate the regulators [21]. These developments will assist with the creation of a more inclusive support structure for all universities, as these tools can be easily made accessible online to a wider community.…”

Section: Discussionmentioning

confidence: 99%

Lack of Support in Medical Device Regulation within Academia

et al. 2021

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New medical technologies can transform healthcare, and automation of processes is becoming increasingly ubiquitous within the patient care sector. Many innovative ideas arise from academia, but regulations need to be taken into account if they want to reach the market and create a real impact. This is particularly relevant for applied fields, such as prosthetics, which continuously generates cutting-edge solutions. However, it remains unclear how well the regulatory pathway is supported within universities. This study applied a data-driven assessment of available online information regarding support of medical device regulations within universities. A total of 109,200 URLs were screened for regulatory information associated with universities in the UK and the USA. The results show that based on available online data, 55% of the selected universities in the UK and 35% in the USA did not provide any support for medical device regulations. There is a big discrepancy between universities in terms of the available support, as well as the kind of information that is made accessible by the academic institutes. It is suggested that increasing support for regulatory strategies during the early phases of research and development will likely yield a better translation of technologies into clinical care. Universities can play a more active role in this.

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Section: Discussionmentioning

confidence: 99%

Lack of Support in Medical Device Regulation within Academia

et al. 2021

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“…This article builds on the concept of using decision trees to better understand medical device regulation proposed by Bergmann et al [17]. It will focus on the use of decision trees for representation of legislation, as they are suited to deal with issues arising from the differences between legal and technical semantics.…”

Section: Digitizing Legislationmentioning

confidence: 99%

“…Bergmann et al [17] proposed a data-driven methodology using the Iterative Dichotomiser 3 (ID3) decision tree learning methodology, introduced by Quinlan (1985) [18], to classify medical devices according to the rules set out in the Medical Device Directive (MDD). The alternative method to building a decision tree using a data-driven approach is the use of a rule-based approach.…”

Section: Decision Trees For Digitizing Legislationmentioning

confidence: 99%

Using Rule-Based Decision Trees to Digitize Legislation

et al. 2022

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This article introduces a novel approach to digitize legislation using rule based-decision trees (RBDTs). As regulation is one of the major barriers to innovation, novel methods for helping stakeholders better understand, and conform to, legislation are becoming increasingly important. Newly introduced medical device regulation has resulted in an increased complexity of regulatory strategy for manufacturers, and the pressure on notified body resources to support this process is making this an increasing concern in industry. This paper explores a real-world classification problem that arises for medical device manufacturers when they want to be certified according to the In Vitro Diagnostic Regulation (IVDR). A modification to an existing RBDT algorithm is introduced (RBDT-1C) and a case study demonstrates how this method can be applied. The RBDT-1C algorithm is used to design a decision tree to classify IVD devices according to their risk-based classes: Class A, Class B, Class C and Class D. The applied RBDT-1C algorithm demonstrated accurate classification in-line with published ground-truth data. This approach should enable users to better understand the legislation, has informed policy makers about potential areas for future guidance, and allowed for the identification of errors in the regulations that have already been recognized and amended by the European Commission.

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“…However, if a question is long because the author has taken care to explain its intent fully, then comprehension will actually improve [49]. The medical device regulations can be posed as a set of questions [50], which allows for further exploration of this metric. One can then even incorporate a suggested minimum of 3 s that is considered needed to perform cognitive tasks and formulate responses to questions [51].…”

Section: Complexity and Response Timementioning

confidence: 99%

The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques

2021

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Medical device regulations are dynamic, as they need to cover an ever changing landscape. In Europe this has led to a new set of regulations (both for Medical Devices and In Vitro Diagnostics), which replaced the old rules. This study is interested in how the complexity of these medical regulations changed over time and if additional time-based metrics can be associated with any of the complexity metrics. Complexity is defined in terms of readability of the text and it is computed using established linguistic measures, as well as Halstead complexity scores. It was shown that the regulatory complexity of new EU medical device regulations was higher than their predecessors, especially when Halstead complexity measures were considered. The complexity metrics obtained for the new regulations were subsequently associated with the time it took to consider these regulations. Only very weak Pearson’s correlation coefficients were found between the complexity scores and the obtained response times for the new regulations. This could indicate that there are issues with how complexity is perceived by those that need to apply these regulations. Taking the complexity of regulations into account can greatly help with the development of more user friendly regulations. The results from the data-driven methods that are applied in this research indicate that governments could benefit from focusing on making regulations more accessible and utilitarian. This would improve the stakeholder adherence and facilitate effective implementation. This work also highlighted the need to develop more suitable methods to analyse regulatory text to further inform the wider research community.

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Regulatory Navigation: A Digital Tool to Understand Medical Device Classification Pathways

Cited by 4 publications

References 4 publications

Lack of Support in Medical Device Regulation within Academia

Lack of Support in Medical Device Regulation within Academia

Using Rule-Based Decision Trees to Digitize Legislation

The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques

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