| 总结: | <strong>Background/Aims<br></strong>
In response to the COVID-19 pandemic, medical devices were given exceptional use authorisations. Medical device regulators around the world modified their administrative approach to medical device approval to ensure that relevant devices and personal protection equipment would be available to healthcare professionals and patients. Using data from medical device regulators in the US and Australia, this study assessed the impact of COVID-19-related regulatory modifications implemented to allow quick market entry of relevant equipment, and how data science techniques can help to inform future policy.
<br><strong>
Methods<br></strong>
Data from the US Food and Drug Administration's Global Unique Device Identification Database and the Australian Therapeutic Goods Administration's Australian Register of Therapeutic Goods were used as case studies to explore the effects of policy changes. All COVID-19-related devices and personal protective equipment were identified in the data using the Global Medical Device Nomenclature system and device descriptions. A 10-day and 20-day moving average was applied to assess how many of these devices reached the market between 30 July 2019 and 24 June 2020.
<br><strong>
Results<br></strong>
In both the US and Australian datasets, the number of market entries increased after the pandemic began. However, the impact of the Australian policy appeared to be greater than that of the US, as there was a considerable increase in the number of personal protective equipment and COVID-19-related device registrations following the declaration of the pandemic in Australia, while the impact on the US data was relatively minimal.
<br><strong>
Conclusions<br></strong>
In general, market entry of relevant equipment increased during the COVID-19 pandemic, indicating the possible impact of policy changes. The kind of data captured in national databases greatly influences the ability to accurately track performance of regulatory policy changes. This work shows the potential of using data science in a regulatory context to assess if a policy change creates the desired effect.
|
|---|