Every day we hear about big data, the need to harness it, and the need to analyze it. In doing so, we are promised that big data will solve our most pressing challenges. By applying machine learning to imaging scans, a computer may be able to detect cancer cells as well as – or better than – medical experts.[1] By parsing thousands of chemical compounds, pharmaceutical companies will be able to discover new antibiotics.[2] At this point, it is undeniable that big data and artificial intelligence will play a bigger role in the practice of medicine.
For all the effort put into tackling these revolutionary challenges, however, our day-to-day questions are left unanswered. When we ask questions such as “How many patients do you treat each year?” or “What is causing delays in surgery?” we get different answers from different people. Due to the complexity of modern data storage systems, clinical staff, finance, and decision support staff may produce vastly different reports. How do we make decisions if we can’t even agree on the basic truths?
We see solutions to big and small questions manifested in the partnerships between healthcare organizations and technology companies like Amazon, Google, and Microsoft. Unlike EMR implementations, these partnerships are centered on organizing and analyzing data to answer specific questions. They focus on the fundamentals of data analytics, cutting across organizational silos and extracting only the data that illuminates the challenges you face. From supply chain optimization, to pharmaceutical pricing, to tracking patient flows across hospital systems, the key to success can be found in these analytics-focused platforms.
As we tackle the day-to-day challenges of providing care, we must find ways to answer the simple questions we ask big data every day. Answering one question may lead to another, but together these simple answers to simple questions can provide the solution to big challenges. The trick is creating a platform that allows us to harness the mountains of data available to enable us to make better decisions faster.
[1] Pisano ED. “AI shows promise for breast cancer screening.” Nature. 2020-01-01. http://www.nature.com/articles/d41586-019-03822-8.
[2] Trafton A. “Artificial intelligence yields new antibiotic.” MIT News. 2020-02-20.
