Quality Data Inputs Essential For Machine Learning
Multiple times over the last decade, this column has covered the issue of the importance of data quality in decision making, both by executives as well as machines. Back in 2014, when the “big data” craze was mesmerizing the C-Suite, the warning was issued in Big Data and the Madness of Crowds. More recently in How Bad Data Is Undermining Big Data Analytics from December 2020.
Since then, more and more news has emerged regarding the failures of AI and Machine Learning initiatives with the blame given to faulty data as the reason. The recent demise of IBM Watson Health is the latest example.
To weigh in on these changes, I recently interviewed Sandeep Konam, Co-founder and chief technology officer at Abridge.
Gary Drenik: Sandeep, before we dive into the industry issues, please tell us about your expertise and experience that led to the formation of Abridge.
Sandeep Konam: I’ve always believed that artificial intelligence has the potential to radically alter the field of healthcare and fundamentally change the way we look at health and wellbeing. Over the course of the past decade, I built various healthcare AI tools ranging from a cancer biomarker detection app to a low-vision navigation aid. If I were to pick one project that taught me the most, it would be EXAID, an NLP-based clinical trial matching tool for cancer patients and oncologists, that I built during my time at Carnegie Mellon University. It was inspired by personal experiences accompanying my grandmother to the hospital visits for her breast cancer treatment and wanting to improve care delivery experiences and outcomes for patients.
Working on EXAID exposed to me various fault lines in the US healthcare system, most notably, lack of patient centricity on the one hand and how physicians are overburdened with documentation on the other hand. Shaped by those observations and with shared conviction around how medical conversations exchanged between patients and physicians can unlock value for both the end-users, I co-founded Abridge with Shiv Rao. At Abridge, we are building conversation understanding AI to summarize healthcare conversations: to help patients better understand and follow through on their care and to help providers get a head start on their documentation.
It was also while building the clinical trial tool, that I started closely following and taking notes on IBM Watson’s healthcare work, some of which I recently summarized in a Quartz Op-ed.
Drenik: So where did IBM go wrong with Watson Health?
Konam: In response to my Quartz Op-ed, an ex-Watson employee commented that ‘Watson Health was a hammer immediately searching for about a thousand nails.’ That, I think, was their biggest problem – IBM looked to throw AI at everything from medical imaging to clinical trial recruitment.
