Page 296 - NEWNORMAL_ANIMATED
P. 296

NEW NORMAL


        are already in use in society and the business  customers because restaurants, schools, and
        sector.                                  other sites temporarily closed.
          Some of this innovation is already creating   In addition, enhanced traceability, coupled
        a revolution in food production, supply, and  with advanced analytical tools, could help us
        delivery.                                spot potential problems in advance and help
          These developments offer great opportu-  us prevent or lessen their impact.
        nity, but also pose many challenges, some
        of which are complicated by an increasingly  SMARTER TOOLS AND APPROACHES
        complex global supply chain.             FOR PREVENTION AND OUTBREAK
          I want to note that while the New Era has  RESPONSE
        a strong emphasis in the application of new   A second core element of the blueprint in-
        technology, it’s not just about technology.  volves our ability to draw on the power of new
        It’s about using that technology to build and  data streams.
        put in place more effective approaches and    One of our most important resources we
        processes.                               have today lies in our ability to unleash the
                                                 power of data.  We intend to do everything
        ENHANCED TRACEABILITY                    we can to attain better quality data, conduct
          I’d like to spend a few minutes going over  a more meaningful analysis of it, and to trans-
        the core elements of the blueprint.      form streams of data into more meaningful,
          The first is tech-enabled traceability.  This is  strategic, and prevention-oriented actions.
        one of those areas that we’ve learned during   The plans embraced by the blueprint inclu-
        the pandemic has utility beyond our response  de strengthening our procedures and proto-
        to outbreaks of foodborne illness.       cols for conducting the root cause analyses
          One of the challenges we’ve faced over the  that can identify how a food became contami-
        years is recurring outbreaks of illnesses asso-  nated and inform our understanding of how
        ciated with the consumption of certain foods.  to help prevent that from happening again.

        What this daunting problem underscores is   The need for greater traceability and pre-
        the critical importance of the FDA working  dictive analytics can be seen in our most re-
        with industry so that we can rapidly trace a  cent efforts to improve the safety of romaine
        contaminated food to its source.  And when I  lettuce and other leafy greens, which have too
        say rapidly, I mean minutes, not days, weeks,  often been implicated in outbreaks of Shiga-
        or even longer.                          -toxin producing E. coli (STEC) infections.
          We want to explore ways to encourage com-  The repeat nature of these outbreaks illustra-
        panies to adopt tracing technologies and also  tes the importance of achieving end-to-end
        to harmonize efforts to follow food from farm  traceability and of maximizing the effective-
        to table.  We should strive to speak the same  ness of root cause analyses.
        language, by espousing similar data standards   Another example of the kinds of new tools
        across government and industry for tracking  we’re developing for prevention can be seen
        and tracing a food product.              in a pilot program we’re conducting that will
          During the pandemic we realized that wi-  leverage artificial intelligence (AI) and machi-
        despread traceability provides greater sup-  ne learning to strengthen the agency’s review
        ply chain visibility.  This, in turn, can help the  of imported foods at ports of entry to help en-
        FDA and the food industry anticipate the kind  sure that they meet U.S. food safety standards.
        of imbalances in the marketplace that led to   A proof of concept application of AI and ma-
        temporary shortages of certain commodities  chine learning models to historical shipment
        and created food waste when producers lost  data indicates that we can expect very promi-
       296
   291   292   293   294   295   296   297   298   299   300   301