The man behind no-survey survey
I used to work in a cognitive science laboratory. Our team collaborated with researchers from the University of Washington and Université de Neuchâtel to look at the cross cultural differences in attitudes towards mathematics and group learning. The projects went well and funding was sufficient. For years, we were pretty much independent and happy.
Until one day, the Dean of college stormed in. Our junior staff almost barred him from entering because we never had visitors. All the more, the Dean looked at least a decade dissimilar from the profile picture we were shown on the first day of work. The laboratory was told it had been given the worst feedback ever across the whole campus for three years consecutively. So the Dean was eager to find out why. We were appalled by the comment as we were never dissatisfied, let alone hearing anyone express this view out loud. After a series of clarifications, it turned out that something had gone wrong with the survey responsible for capturing our feedback.
Cory Linton, Chief Executive Officer of Edify.ai, was not at all surprised when I recalled my story during our interview. In fact, Linton said most healthcare systems will have at least one annual survey per year. He agreed that whilst these surveys tend to yield good data, they are not effective to reflect what happen in the actual context.
At a glance, a person may seem to be doing well in his/her job at a particular time. However, if one managed to track his/her performance over a period, the story may tell otherwise. What Edify.ai does is to leverage on the massive amount of real-time data exist in the system to help employees to be more engaged and effective, creating what Linton called, a “no-survey survey”.
“Think about emails in an organization,” Linton explained, “the average employee sends about 50 messages a day. A large healthcare system we are working with, sends 300 million emails per year, so that’s a lot of data. With that, we analyze the tone of communications; whether it’s positive or negative and when does it begin to change”. Linton believes unlike survey, it’s harder for these data sets to lie. He added when people decide which email they respond to immediately and which they do not, they are making a judgement. Structured questions, on the other hand, may not be sensitive enough to pick up the information.
Previously, Linton and his eminent technical team had successfully generated algorithms based on enterprise data to help companies like ShipEx to better engage truck drivers, and JT Thorpe, US’ largest and oldest refractory construction company, to improve safety and awareness. Now, Edify.ai is setting their eyes on healthcare. They are employing artificial intelligence (AI) to predict when healthcare professionals are at risk of burnout.
According to a report put forward by the Massachusetts Medical Society, Massachusetts Health and Hospital Association, Harvard T. H. Chan School of Public Health and Harvard Global Health Institute, nearly half of all physicians in the US experience burnout in some form. As high as 78% of physicians expressed they are feeling the pinch. By 2025, an estimated of 90,000 healthcare professionals will be lost due to general shortage and continuous work pressure. On average, the cost of recruiting and replacing a physician can range between $500,000 to $1 million.
“Sadly, what’s going on right now is that we will never be able to find out if a doctor has the desire to quit until he/she is ready to dos,” Linton remarked. “Our system typically learns whether a healthcare professional is showing signs of burnout in more or less than a week’s time. He/she may not leave immediately but go on for job searching in the next few months”. So, the sooner a burnout is being detected, the healthcare professional can be moved into a less stressful position.
Apart from emails and messages, Edify.ai is also making use of other actionable data like electronic health records (EHRs) to gain an insight into healthcare professionals’ workflow. Ultimately, the company hopes that the eventual application, will not only reflect burnout rates, but also whether healthcare professionals are spedning too much time interacting with the EHRs. Whether diversity and inclusion were considered in the workplace and the overall dynamics within the team.
“Instead of actual percentages, users will see shades of green, yellow and red on the Edify.ai interface. Green represents the statistical norm, yellow means outside of it, and red means statistically different,” Linton said. As real-time data keeps streaming in and results change every day, even if it says 10% abnormal, it may not be statistically significant, so color coders will prevent people from reacting when they do not need to.
In the coming year, Edify.ai is working with several companies in the US to strengthen its neural network. They are also debating on the level of transparency the end-product shares. Linton foresees their pioneering clientage to be cutting edge. At the same time, he also wishes to have more like-minded on board.
This article was published in AIMed Magazine Vol 2, #2 — The Cardiology & Radiology issue, pages 48 to 49, debut May 2019.
