Silobusting & Informatics

In November, I had the honor of being one of ten undergraduate students selected from across the country for a scholarship to the American Medical Informatics Symposium in San Francisco, California. There, I learned more about what informatics is – the art and science at the intersection of computer/data science and health care. It was one of the most discipline-diverse conferences I’d ever been to, and it reminded me of the book I had been reading – Gillian Tett’s “The Silo Effect,” a management-oriented book about how staying in a professional or social silo can cause havoc.

In The Silo Effect, Tett argues that everyone staying in their respective verticals and not communicating well horizontally – across disciplines – in turn caused the 2008 financial crisis. She also discusses examples of how busting silos and combining disciplines can solve problems, such as how integrating a predictive analytic tool into the Chicago Police Department cut down the rate of violent interactions in Chicago.

I was reminded of silo busting when we split up into workshops about patient generated health data(PGHD) and patient portals. At my table were three practicing physicians, two nurses, a pharmacist, a software salesman, hospital leadership and two computer scientists. When we discussed the needs and lacking qualities of the current tools, we were able to hear from all different sides of the spectrum on what was needed in these type of tools. The discussion was moderated by two leaders in the fields of PGHD and patient portals, who took the feedback the diverse group had generated and made notes to give to their respective teams. By communicating horizontally, across disciplines, we were able to see the problem more fully, brainstorm holistic answers based on all the stakeholder needs, and relay that information to someone that would actually use it.

The opening keynote was given by Dr. Jessica Mega, Chief Medical Officer of Verily, Google X’s healthcare startup branch. She talked about how AI and Machine Learning have been and will continue to improve patient care. She joked about the skittishness of some clinicians to adopt these techniques, reminding the 2000-person audience that doctors used to think stethoscopes would take them away from their patients. She showed an example of how they’re using machine learning to identify images in an app to monitor glucose levels. The patient would take a picture of their meal, then the app would identify the food, and map that data to their current blood glucose level and their exercise history. This constant stream of data could be overwhelming to physicians, so the data scientists developed an algorithm to look for recurring trends tagged to certain events. This gives both physicians and patients actionable insights into their daily life, not just month-to-month checkups.

The AMIA conference is a glowing example of professional silo busting. I truly enjoyed learning about this topic and I look forward to learning more about the field. Thanks to Hofstra University for the scholarship to attend the conference and for three year membership in AMIA!