(Before & After Augmented Analytics - Blog 2 of 4)
This week, smart thermometer company Kinsa shared anonymized data that showed U.S. regions where people were registering a reduction in the number of fevers. The goal was to help health care professionals and local governments get a better picture of where the Coronavirus might be slowing, and if their efforts were paying off.
While useful, this report reflects the typical pitfalls of big data analysis today. For one, not everyone owns a Kinsa thermometer, so a large percentage of the population went unaccounted for in this report. Second, it shows where local restrictions might be slowing the spread of Coronavirus--but examines efforts after the fact. Third, it lacks critical important context: which of those fevers were due to Coronavirus, and which can we attribute to any other health issue? Finally, while Kinsa’s data gathering tool can detect anomalies, it can’t necessarily explain their root cause.
What’s missing here can be easily solved with Augmented Analytics--Artificial Intelligence-infused data analysis that provides richer context, like historical data, for a better understanding of what’s happening. In the healthcare industry, technology’s ability to provide actionable insights, and immediately identify root causes and anomalies are more crucial now than ever for effective future-looking solutions. Sure, smart tech can help radiology departments detect issues now, but the benefits go much further. According to an S&P trends report on healthcare, 2020 is seeing a shift from interest in AI-powered analytics to augmented AI analytics.
Here are three ways Augmented Analytics is making a difference in the medical arena:
It Makes Treatment Recalibration Swift and Accurate.
The World Economic Forum says that by 2030, AI will regularly access multiple sources of data to reveal patterns in disease and help with its treatment. Before Augmented Analytics, a chronically ill patient would see a doctor at regular intervals to review how they are responding to treatment. Now, healthcare informatics--a data science specific to the medical field -- has made it possible for care teams to gather much more real-time information from various sources (like Fitbits, pacemakers, and chatbot check-ins). With so much additional content, providers are continuously learning about a patient’s ever-changing status. Thanks to advanced analytics capabilities, modern medicine has made disease management much more reactive and adaptable on a larger scale--and can save administrators time by automatically updating data within a patient’s electronic health record. Augmented Analytics’ ability to provide hyper-personalized care has also shown to reduce costs, as well as revealed previously unknown connections between things like depression and heart disease (Deloitte).
It Gets Us Ahead of the Problem.
Before Augmented Analytics, a doctor’s ability to predict a patient’s risk for disease was reliant on knowledge of their family history and lifestyle. Now, predictive analytics can set solutions in place prior to a medical diagnosis --helping to lengthen patients’ lives and avoid costly, unpleasant procedures. Organizations like 23andMe have leveraged a massive amount of data to improve health technology and treatment system, as advanced analytics can aid health professionals in better assessing medical risks. AI-powered analytics can even make predictions on a cellular level.
It Improves Measurement for Efficacy and Quality.
One of the tenets of the Affordable Healthcare Act was the negative consequences associated with hospital readmissions. It turns out, Augmented Analytics can improve the business aspect of medicine as much as it can save lives. Before this technology, hospitals often relied on a wait-and-see approach and experienced a lot of lag time between the realization of a problem and implementing a solution. Now, hospitals and other treatment centers use predictive modeling to determine if a patient is likely to re-admitted. Augmented Analytics has the intelligence to provide actionable suggestions to reduce readmission rates--which saves money, helps with ACA compliance, and provides an overall better patient experience.
Augmented Analytics in healthcare is just getting started as we explore all the applications of artificial intelligence, machine learning, and other big data technologies.