Much of today’s society involves measurement, data collection and ultimately comparison amongst others to see how we stack up. We are either too heavy, too thin, our blood pressure too high, GPA too low, or we have too few followers on our social pages. In short, we have driven ourselves toward a constantly growing need to collect, evaluate and judge what success looks like based on measurable data points and how those outcomes compare to others. The trend toward data collection penetrates all facets of our daily lives, and in food safety, we are increasingly adopting measurement and data analysis to make further advancements in reducing pathogen risks for consumers. The convergence of economical computation, developments in artificial intelligence (AI) and progressively more sensitive and affordable diagnostic testing have created a promising new frontier in pathogen risk detection and management, and excitedly, one that offers the ability to be less reactive to one that is far more predictive and preventative. Data collection and analysis offers the ability to more efficiently realize the full value of our testing and food safety efforts; aggregating mass quantities of data from an entity, or an entire industry, provides new insights into the ecosystem where we manufacture food, and allows us to make even more informed decisions. In short, food safety data analysis and data sharing welcome the advent of a far more preventative food safety paradigm.

The intent of food safety systems is to prevent exposure and negative health consequences from any agent in a food that may cause consumer risk (e.g., bacteria, viruses, heavy metals, chemical contaminants). A simple concept, yet a complicated effort since hazards are generally unable to be seen by the naked eye, and usually present at very low prevalence rates. Prior to the development of molecular pathogen detection methods, such as polymerase chain reaction (PCR), if microbiological testing were to be used, food safety professionals relied upon laboratories’ abilities to select for and grow bacteria from foods or food processing environments. These cultural methods had multiple steps, could be costly due to the many specialized materials and skilled labor required, and generally took days or weeks to complete. For highly perishable food products, such as fresh produce and meat, cultural methods were simply not an option as results were obtained after shipping, and potentially after consumption. Over the past couple of decades, rapid pathogen methods, such as PCR, have served as revolutionary tools in food safety risk assessment since they offered fast, highly sensitive and economical techniques that allowed for timely results and increased detections by using genomic test targets (i.e. DNA/RNA gene detections). With more robust tools, food safety professionals have increasingly incorporated pathogen testing for bacteria and other microorganisms into their programs to better understand products’ and processing environments’ risks. Each year, the food industry is reporting incorporating more testing into its overall food safety plan, highlighting the growing demand for information to assess current practices and improve food safety outcomes.

As we approach food pathogen testing, or any quality testing, the most valuable test taken is the one where we “fail,” the test that identifies the pathogen or defect. However, designing a system to find failures doesn’t feel very intuitive, and subconsciously testing programs that report a high number of negatives can feel far more comfortable since it affirms that systems are functioning as expected and our efforts are successful. Herein lies a challenge for food safety professionals. Our biggest food safety successes come from designing systems that identify failures (i.e., pathogen detections), and failures by their nature, can create uncomfortable management situations. The drive for acceptable test results has been ingrained in us since childhood. At an early age, we learn the need to perform well on testing since grades not only dictate an individual’s potential by determining the schools one can gain admission to, but, more concerningly, grades are often interpreted to be reflections of intellect and worth. The pressures of performing well on testing create systems designed to advance those who score well but do not necessarily learn well. It’s summed up well in the common saying, “You hit the target and missed the point.” Given the pervasive social pressures to score well, it is not unexpected that it can be difficult to observe and process testing failures in any system, and this behavior should be taken into consideration as more efforts are invested in testing and analysis systems for food safety. The successful incorporation of food pathogen testing and the subsequent use of detection data sets to better understand and predict risk first requires building social networks that normalize and encourage finding failure.

Negative test results in food safety testing generally mean one of two things: (1) there is no prevalence of the target in the system or food, or (2) the sampling design and/or testing are not able to detect the target. For the first scenario, testing for a target that is not a risk for that food or environment doesn’t make any food safety or financial sense. If a target has been identified as a potential risk and thereby is being tested for, it is most likely because the target has been previously associated with illness or negative outcome (i.e. regulatory requirement). If a hazard has been known to occur, infrequently detecting the target with testing may occur from sampling design insufficiencies, limitations in the testing methodology or a combination of both. The second scenario is generally much harder to diagnose and interpret since it can be influenced by numerous factors that require investigation. Irrespective of the technical reason why few positives are detected, a related issue when analyzing testing programs is that negatives on samples are rarely questioned since they support the preferred outcome and expectation. In general, negative samples lead one to interpret that the process is under control and that all food safety efforts “pass the test.” Positive feedback in this scenario is the receipt of a negative test result. Quite simply, in food safety, a negative dataset is very comfortable, but ultimately not very valuable in reducing risk to consumers since it is only when detections are found that we modify behavior (e.g. destroy product, conduct additional sanitation, etc.). The change in behavior, and not the test result, is what would reduce risk in the system.

Evolving to a preventative, risk-based food safety paradigm is an exciting concept and one that food safety professionals have anxiously been waiting for. Receiving a call from regulators or public health officials that your product has been linked to an outbreak is one that no person or company wants to receive; it is the worst fear and ultimate signal of failure. One illness has always been one too many. While the improvement of testing technology and computational power creates the technical infrastructure for the shift toward risk-based management and preventative food safety, it is critical that the industry not overlook the need to establish a food safety culture where receiving failing results has been normalized and encouraged. Shifting goals to manage risks and not absolutes ultimately requires mentally accepting that there is some level of residual risk that we may have, especially in systems without processing interventions, and that testing programs with zero to few detections should not be the goal. Testing and analysis only contribute to reducing risk when information is learned from the81result. Detections teach us and allow us to focus behaviors to drive residual risks continually lower. A perfect score from pathogen testing is generally not the signal of a healthy system, but a testing program that may require attention and optimization to detect the targets. Regardless of how large the testing program is, negative test results provide no motivation for behavior change over time, and thereby do nothing to reduce risk to consumers. This is counterintuitive since many testing efforts require enormous financial and labor resources to execute; they surely are not easy. Alongside advancements in testing and analysis tools, data-sharing platforms are also being developed to help support broader and faster learning by providing an opportunity to see not just how one product, plant or industry performs, but to create enhanced visibility about how they perform relative to other operations. The increased visibility via data-sharing may further exacerbate inherent sensitivities toward observing detections, and the food industry and regulatory agencies must focus on building a social and regulatory framework that incentivizes finding failing test results. The advent of preventative, risk-based food safety management is an exciting and revolutionary time for the food industry. As the world connects through data analysis, new insights into the interrelatedness of our food and production ecosystems will offer new understanding and strategies for producing the safest, most sustainable food system possible.