Healthcare and Agriculture: United in Using Data to Improve the Quality of Life

My husband works in media serving the agriculture industry, which has its own big data challenges. He recently invited HIMSS Analytics expert James Gaston (@JamesEGaston) to guest-speak at a conference on precision agriculture, which uses data models and high-tech machinery to improve crop yields and minimize use of pesticides, fertilizers and water.  I asked him to give me a run-down of the conference and provide parallels between analytics in agriculture and healthcare.  Here are some key take-aways:

Health and Safety. Healthcare and agriculture are “two of the most essential industries in the world”—and together accountable for most of the increases in life expectancies around the world.  At the same, each industry has distinct margins of error that can have widespread implications. Medical error now is the third leading cause of death in the U.S. behind only heart disease and cancer. Similarly, miscalculations in farming can lead to food shortages or, conversely, to misapplications of potentially hazardous materials, or to E. coli in the food chain. 

“Rights.” Healthcare’s axiom of “the right medication in the right dose to the right patient in the right time” was echoed elsewhere at the conference in just a slightly different form for agriculture: “the right application (of pesticides or fertilizers), at the right place, at the right time.” In healthcare, the use of smart IV pumps has led to a 32% reduction in the use of reported heparin administration errors. It turns out this technology has a direct corollary in agriculture’s use of sophisticated machinery and electronics to precisely apply “crop inputs”—which in their own way are like medicines for plants.

Syndromic Surveillance.  Through the increasing use of electronic  health records, the healthcare industry is in a position to track and anticipate pandemic outbreaks. For instance, James Gaston cited “What’s Going Around?,” a syndromic surveillance system used by NorthShore University HealthSystem in the Chicago area with  historical and current data to create data-visualization maps that help track and prepare for epidemics of influenza, strep throat and other maladies. 

“What if crops had (similar) real-time monitoring,” James asked, and indeed, in some progressive parts of agriculture they already do, using sensitive monitors to detect weather fluctuations and anticipate the emergence of, say, crop diseases. Many “precision” farmers now have become accustomed to poring over data maps of their fields that are very similar to NorthShore’s surveillance maps. 

Such “smart” use of data promises a world of opportunity for healthcare and agriculture as both industries progressively move up the analytics value curve from descriptive analytics (“what happened?”), to diagnostic analytics (“why did it happen?”), to predictive analytics (“what will happen?”), and on to the highly desired endgame: prescriptive analytics (“can we make things happen?”).

The path won’t be easy, as noted throughout the conference. Both industries are swimming in data, struggling still to process and make sense of it all, let alone to put it to proactive use so that seasoned experts—doctors and farmers alike—can combine the best of their professional intuition with data quantification. And data security still is very real. At the conference a cybersecurity expert noted that the recent data breach of  large discount,  department stores is nothing compared to the inherent risk of data breaches in healthcare, whose data records are worth approximately 50 times those of the retail sector.

But the trend line for data analytics is a positive one, and healthcare—and agriculture—are hardly alone. The next time you dig into a salad, or sink your teeth into a crunchy apple, you can be confident that many of the same analytics approaches we use in the healthcare sector are being applied to the production of our food.

•What are other clinical and business lessons learned from healthcare that can help other industries?
•Have you applied data and analytics models, techniques, or solutions from other industries in your practice?

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Big Data; Predictive AnalyticsMedical Error; Data Security; Syndromic Surveillance; Clinical and Business Intelligence