Clario is a Seattle-based company that has produced a smart Radiology worklist management, in use daily by more than 1400 Radiologists around the country. angelMD sat down with Clario CEO, Chris Wood, to discuss his company’s successes and challenges.
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What is the typical workflow for the average radiologist who is not using an enterprise worklist solution?
This is a highly inefficient and distracting process. Many radiologists log in and out of several systems (called PACS) all day, checking for exams. Each PACS has several worklists that a radiologist may be responsible for. Within each list, there may be dozens of exams that need to be read. The radiologist will look through the lists and manually find the exams they should be reading next, oftentimes having to pick through subspecialty exams outside their area of expertise.
What makes your worklist “smart” and how does that improve efficiency and quality of radiology interpretations?
Our system knows about all the exams in the enterprise, so we completely eliminate the need to look through multiple systems. Then, we automatically create a prioritized stack of exams for the radiologist to read. To determine the priority, we incorporate each radiologist’s subspecialty, their shift, how quickly they read certain exams, and the practice Service Level Agreements.
When radiologists are not distracted by bouncing between systems and modalities (CT, MR, Ultrasound, etc.) they are more efficient, and the quality of their work product improves. We also optimize subspecialty reading percentages. Subspecialty exams read by a radiologist fellowship trained in the area are not only read more quickly but at a higher level of quality.
How do you plan on incorporating AI and machine learning into the product?
Our system currently uses a manually configured rule set to determine what is in each radiologist’s queue and the order of the exams to be read. Over the years, our rule sets have become extremely complex because we have continued to uncover things that can optimize efficiency.
For example, we have found that some radiologists will read an MRI up to 20% faster if the exam they just finished reading was also an MRI. With AI, we will be able to optimize each radiologist’s queue individually, and can consider many more variables. The system will “learn” how to tailor the queue to optimize their efficiency. Because we have peer review data (a surrogate for quality) we can also drive up the quality of reports each radiologist creates.
Through partnerships with other companies we will also use AI to escalate exam priority based upon the computer’s “first look” at the actual images. If AI uncovers a potential critical result, we will escalate this exam to the top of the list. The radiologist will not know that anything has changed, but they will be producing more work at a higher quality. They will feel less stress, and will make more money by reading more exams while providing a higher level of patient care.
Can you elaborate on your subscription-based revenue model, and retention numbers?
Calculating the amount of revenue generated by customers in 2016 and comparing it to the revenue generated by those same customers in 2015 our revenue retention is 116%. This is because our customers are growing and purchasing more licenses each year.
What is your market share and who are your major competitors?
We process about 1.3 million exams per month through our system. That gives us about 3% of all the exams in the USA. Our biggest competitor was Medicalis which was just purchased by Siemens. Primordial is also a competitor, but they tend to focus on custom solutions.
To grow, we will continue to sell directly to private practices (50% of the total market), and anticipate hiring sales representatives toward the end of 2017. Lexmark and Mach7 are our distributors to health systems. They can couple our products with theirs and present a complete solution to a health system (which is how they like to buy).
Tell us about yourself and what led you to start Clario.
I am a medical physicist who did imaging research in a cancer center in grad school in the late 1980’s. We were funded by NASA and I applied satellite image processing technology (including AI) to MRI images. This got me into software and medical imaging at a time when radiology was still almost entirely film based.
After graduating, I worked in large radiology companies (Picker and Siemens) writing 3D visualization software and managing software teams. I then co-founded a Computer Aided Detection company (Confirma) which received the first FDA clearance for Breast MR cancer detection. This company was sold to Merge Healthcare.
In 2008 I started Clario because I realized how inefficient workflow became when we switched from film to PACS. In the days of film, the workflow was optimized by people who handed out films. With radiologists reading from lists, and trying to figure out what to read, they were wasting a great deal of time. I ran the idea by some radiologist friends and their response was universal. Now was the time to fix this!
What makes your team unique?
All of the senior managers (as well as many of the employees) worked with me at Confirma, each having over 10 years experience in radiology software solutions.. I also had 75 engineers reporting to me at Siemens in Seattle so I have a fairly deep bench of talent I can draw from. It also does not hurt to be in Seattle and across the street from Amazon. There is a lot of technical talent in Seattle, and we have more than enough domain expertise.
Are there any specific pitfalls that concern you?
We fight health care inertia every day. Radiology departments are currently getting the job done, but they are inefficient, so they do not have much time to look at new technology. It is our job to educate them about how we can make them function better.
We have learned that the best way to combat this inertia is showing them what other practices have done. This is of great interest to a radiology group. Luckily for us, our customers love to tell the story of how they made their business more efficient with Clario. All of our customers are reference sites for us, and several will host visits by potential Clario customers.
Where do you see Clario in 12-18 months?
We have over $2M in recurring revenue currently, and will add at least $1.5M in recurring revenue in the next 18 months. Our investment in AI will help us generate excitement around our product and build our sales pipline to over $12M in recurring revenue.
We will sell the company when we have several million in free cash flow. At that time, we will talk to all the strategics (such as GE, Nuance and our distributors) but we do not want to rely on them. Company valuations in Health IT are quite high, so we will have the option of selling to private equity to return capital to our investors.
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