Mark Cuban & Top Pulmonary Doctors Discuss Healthcare Technology Opportunity Issues

Webinar discussion took place at the American Thoracic Society's Annual Conference. "Stop siloing data," said Cuban. "My perception of it [referring to doctors using electronic medical records] is that you try to fit whatever you can into the square and round holes that are alreay there, and that's not necessarily optimized for process."

Mark Cuban, Dr. Gerard Silvestri, Dr. Peter Mazzone, and Dr. George A. Eapen for a live webinar hosted May 20, 2019, at the American Thoracic Society conference in Dallas, TX. The panel met to discuss healthcare technology issues that have led to patients, health care providers and hospitals facing an ever-widening gap between what technology can do and what is currently implemented.

Eon, a complex patient management technology company out of Denver, Colorado, is dedicated to creating sea-change in the healthcare industry and is determined to use advanced technology to improve complex patient management of every kind. Technology has improved processes and is now less expensive in every other industry – why not healthcare?

The webinar addressed issues in healthcare technology, such as the cost, the failures of EMRs, the siloing of data and misaligned incentives, as well as solutions to mitigate these issues. Eon launched the discussion with a question on how providers, as well as hospitals, can educate themselves on what technology should cost and why it has been okay for the industry to overcharge for technology that is now fractional to develop.

While some healthcare technology companies repackage and repurpose technology from the 1990s and sell it to hospitals for hundreds of thousands of dollars, it’s not surprising technology now contributes 40-50% of the annual cost increase in healthcare. As Dr. Eapen points out, it’s not necessarily the cost of the software that is the issue, but what the value is. He says, “It’s really not about driving down health care costs. Something costs what it costs, but if it provides value, it is either worth it, or it’s not.”

The physicians on the panel encountering these challenges were able to pose questions about patient data and the utility of EMRs from a user standpoint. Cuban, as an entrepreneur, Eon investor, and Artificial Intelligence (AI) aficionado, brought a unique perspective to the conversation and allowed it to naturally move in the direction of siloed data by both hospitals and insurance companies.

“Stop siloing data,” said Cuban. “My perception of it [referring to doctors using electronic medical records] is that you try to fit whatever you can into the square and round holes that are alreay there, and that’s not necessarily optimized for process.”

Mark Cuban
“You need Eon because you need the ability to know when to contact each patient next and what the patient needs to get healthy again.

 

 

As the technology gap continues to grow, so does the understanding of what AI in healthcare actually means for clinical utility. Google recently published a study in the journal Nature Medicine titled “End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.” The researchers trained a Convolutional Neural Network (CNN) on a publicly available dataset from the National Lung Screening Trial (NLST). The researchers compared their model to six thoracic radiologists and concluded that their model outperformed the radiologist. The study was subsequently picked up by the New York Times in an article that proclaimed “AI Took a Test to Predict Lung Cancer. It Got an A.”

The lack of understanding in AI becomes apparent when you see proclamations like this, and it’s important to understand who is looking at the study and analyzing the results. With data from 578 patients, and only 70% of it being used to train the model, the potential for overfit and incorrect diagnosis grows as you apply the model across larger populations. A meaningful AI model for true computer vision in lung cancer will require millions of labeled data sets. And while the hospitals procure labeled data sets, the value of de-identified data and the data silos themselves have made it nearly impossible for researchers to create models that result in clinically significant results.

To build on this point, Cuban added, “One of the biggest jobs in healthcare is going to be labeling data. You want to have a data scientist on staff that you are going to be able to go to and say, I’ve got this data, what can we do with it?”  Hospital systems have limited teams dedicated to understanding and developing deep learning models from their data sets and should partner with industry to open the repositories and create meaningful AI.

“What’s happening now is that [healthcare companies’] marketing departments are selling AI, right?” poses Dr. Alzubaidi. “What they’re really selling to these guys is a software that can do what is called segmentation, CAD [computer aided detection], where you take a CT scan and then reconstruct a pulmonary nodule. They’re calling it AI, and it was built in 1990.”

Doctors essentially have to fight for new technology in budget meetings each year. Unfortunately, technology companies make big promises to solve clinical problems, but more often than not, they do not deliver pragmatic clinical solutions to providers. “Peter and I write a lot of guidelines for the “how to’s” that say we’re going to solve it. But my god, we know that 50% of the time doctors don’t follow the guidelines because it’s just not easy enough,” stated Dr. Silvestri. He continued by posing the question, “Most physicians aren’t writing their own software, they’re trying to take care of patients. How do we bridge that gap?”

he importance of translating verbal and interactional patient data from experiential to sequential is the answer. What doctors will be able to take from properly developed natural language systems is the ability to translate interactions with their patients in their entirety. This will allow for data that may not have had previous labeling to be identified and tracked for potential use later. More information will be collected, aggregated into EMRs, and used for enhanced diagnoses and tracking.

Data is the main driver of current technological change. Current healthcare systems are logic based, “if this, then that,” and do not necessarily allow for the predictive needs of healthcare. Now, companies like Eon use machine learning to approach and anticipate needs. Cuban said, “Platforms like Eon allow you to aggregate data, learn, have the data teach you, have the data become more predictive. So that you can be smarter.”

Irresponsible tactics used to develop and sell outdated healthcare technology has warped the value of new software development and created frustration for all healthcare stakeholders. As the industry leader for complex patient management software, Eon is changing the way the healthcare industry views technological advancements and is improving patient care, all while lowering costs for users across the country. Patient management and outcomes can be improved, saving time, money, and most importantly lives. There is no time for expensive, out-dated technology. Together we can defy disease.

Dr. Mazzone adds “I couldn’t build a software program, I couldn’t do data science. But I know what’s necessary for clinical practice and so I want to be able to partner with those folks doing that good work to ultimately help the patients.”

Full transcript can be found here.

 

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