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Up-and-Comers 2018: Collective Medical Technologies and Healthfinch

June 5, 2018
by David Raths
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Another two of this year’s six up-and-coming health IT vendors, as selected by Healthcare Informatics

Each year, to accompany our Healthcare Informatics 100 list of the largest companies in U.S. health information technology, we profile fast-growing companies that could very well make the list in the future. Below are write-ups of the third and fourth companies that made this year’s Up-and-Comers rendition. The remaining two write-ups will be published later this week.

 

Collective Medical Technologies’ Collaborative Care Management Tools Meet Needs During Opioid Crisis

The history of Collective Medical Technologies has had several twists and turns, but the Salt Lake City-based company that provides collaborative care management tools has doubled in size in the last six months to over 100 employees. Its platform is used in 15 states, and that number is expected to reach 25 by the end of 2018. The company recently secured $47.5 million in Series A funding to fuel the expansion.

Collective seeks to close provider communication gaps that undermine patient care. It uses data feeds, risk analytics, notifications, and shared care guidelines to reduce emergency department (ED) utilization, inpatient readmissions and downstream care transitions, including to post-acute operators. After collecting data from all EDs visited by a patient, its solution packages that data into actionable insights, and delivers them to clinicians via real-time notifications. Collective is currently partnered with more than a dozen state hospital associations, and recently added the Florida Hospital Association to its network of partners.

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The company has an intriguing startup story. Fifteen years ago, one of the founders’ mother, Patti Green, was an emergency department social worker in Boise, Idaho, and suspected that some patients were opioid seekers. She set up a rudimentary collaborative care plan for providers to use to identify and help these patients. “It is easy for us now to talk about the opioid epidemic. Nobody was really talking about it 15 years ago, but she was seeing it on the ground,” says Chris Klomp, Collective’s CEO, “and she did something about it.”

She asked her son Adam and his friend Wylie van den Akker, both computer science students at Brigham Young University, to create software to improve on the basic spreadsheet she had created, and they did so. (Klomp was also a childhood friend from Boise who studied business at BYU at the same time.)

Chris Klomp

In 2006, they tried to launch the solution as a company, but there were no customers. “No one was willing to take a chance,” Klomp recalls. “Total crickets.” So all three founders went on to other jobs. Klomp went to work for Bain & Co. But the website for the startup was still up, and in 2009 they were contacted by a hospital in Spokane, Wash., that was trying to do work in the high-utilizer space and couldn’t find any other solutions. So the three old friends from Boise resurrected the company.

“It turned out that little hospital was part of what would become Providence St. Joseph Health, the second-largest nonprofit health system in the country,” Klomp says. Eventually use of their Emergency Department Information Exchange (EDIE) solution started to spread across the state of Washington. The Washington State Health Care Authority reported that use of EDIE by hospital EDs had helped save the state $34 million in Medicaid spending and there was a 9.9 percent reduction in total Medicaid ED visits across the state. “That was big,” Klomp says. “There were compelling results around opioid utilization, in terms of visits resulting in opioid prescriptions and related deaths.”

Next, Providence drove usage across its five-state system, and Oregon adopted it. “All of a sudden, the world caught up as healthcare started paying for quality instead of just volume,” Klomp says. Growth was slow and methodical as the co-founders sought to understand clinical workflows. “We worked to get real demonstrable outcomes from a clinical and economic perspective,” he adds. “We are pretty conservative. This is a different story than raise a whole bunch of money and try to grow the business fast.”

Besides EDIE, Collective now has other software it licenses to payers and accountable care organizations, but it does not charge post-acute operators, ambulatory providers and others who don't have risk. “Our model is that we license our software to those who could see economic benefit through improving coordination of their members, which makes sense,” he says. “Others may not benefit economically, so we don’t charge them.”

Klomp says that Collective helps healthcare providers answer four basic questions:

Where are your patients right now and why?

Of all your patients, which are at risk of something bad happening?

Who should be on the care team to mitigate that risk?

How do we let those people collaborate with one another to mitigate the risk we have identified?

Looking back, Klomp sees a huge element of luck in their success story. “We work hard and try to be smart, but entrepreneurs chronically underestimate how much they get lucky or kind breaks from others,” he says. “I look back at the people willing to take a chance on a couple of unsophisticated kids from BYU who didn’t know a lot about healthcare but were trying to solve one of our mom’s problems. They gave us a chance and indulged us when we made mistakes. You look at Washington and it was just a stroke of luck.”

 

Each year, to accompany our Healthcare Informatics 100 list of the largest companies in U.S. health information technology, we profile fast-growing companies that could very well make the list in the future. Below, a write-up of the fourth company that made this year’s Up-and-Comers rendition. The remaining two write-ups will be published throughout this week.

 

Healthfinch Grows by Automating EHR Tasks

Healthfinch, a Madison, Wis.-based startup, has been laser-focused for several years on making clinicians’ lives easier with its software that automates or re-routes routine tasks in their EHR work flow. For instance, its solution helps delegate prescription refill requests to staffers so the physician has more time to spend with patients.

Healthfinch has gradually grown to more than 4,000 providers on its Charlie Practice Automation Platform, and the company now has 40 employees. It recently secured $6 million in financing led by Adams Street Partners, which brings the company’s total raised to over $17 million since it was founded in 2011.

CEO Jonathan Baran identifies two forces that have jump-started the company. Number one is that all sorts of routine tasks are piling up on physicians and staff, leading to high levels of burnout and negative consequences. “Health systems have really seen this problem and understand there has to be a better way to do this,” he says. Number two is a change in approach by the EHR vendors themselves. “When we started, it was a foreign concept to have an app store for the EHRs. None of them had one yet. But now we have seen widespread adoption of this model across all the major EHRs,” he says. “They now think about themselves as platforms and open marketplaces where people like us can build technology on top of APIs that allow us to integrate our technology into the workflow. That is a big piece. Without those two major forces—market awareness and enabling innovation by building on top of EHRs—this wouldn't be possible.”

Jonathan Baran

The company got its start in 2010. Baran, a Ph.D. student in engineering at the University of Wisconsin at the time, was thinking about how to build apps to make life easier for physicians. He went to a Mayo Clinic Innovation Conference and saw Lyle Berkowitz, M.D., of Northwestern Medicine speaking. “Lyle happened to be speaking there on that very topic, coming at it from the physician perspective,” Baran recalls. “I realized this is exactly the person I need to work with. A few weeks later I drove to Chicago, met with him, and the rest is history. We started this company and have been going ever since.”

Berkowitz, interviewed in this Up-and-Comer section for his role as an executive with MDLive, is also healthfinch’s chairman. He says the idea grew out of his own experience as a physician. “If there is something routine and repeatable, I don’t want to have to do it over and over again. Computers are better at doing those activities.”

Besides refill management, healthfinch also helps physician practices with other tasks such as ordering pre-visit labs and other procedures ahead of patient visits.

Baran says that although automation of repetitive tasks is the ultimate goal, the first step in automation is delegation. “That means shifting the work from physician to staff and using technology to make that process as easy as possible. For the physicians it looks like automation because you are taking this work off their plates, and we use technology to make the process as easy as possible for the staff.”

Madison has developed a burgeoning ecosystem of health IT startups, helped by the presence of the mammoth Epic Systems and its alumni, several of whom healthfinch has hired.

The company also is starting to garner more industry recognition. It won the 2018 athenahealth Innovation Award for work in driving meaningful change in healthcare delivery and organizational outcomes. It was included in the 2017 KLAS Emerging Technologies Report and was in the first wave of vendors in the Epic App Orchard.

Baran believes the future looks bright for companies improving the utility of EHRs. “The Epics and athenas of the world have built these EHRs to capture all this data,” he says. “The next wave of healthcare IT is going to be built on top of the EHRs making life easier for the physician and the staff.”

 


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Definitive Healthcare Acquires HIMSS Analytics’ Data Services

January 16, 2019
by Rajiv Leventhal, Managing Editor
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Definitive Healthcare, a data analytics and business intelligence company, has acquired the data services business and assets of HIMSS Analytics, the organizations announced today.

The purchase includes the Logic, Predict, Analyze and custom research products from HIMSS Analytics, which is commonly known as the data and research arm of the Healthcare Information and Management Systems Society.

According to Definitive officials, the acquisition builds on the company’s “articulated growth strategy to deliver the most reliable and consistent view of healthcare data and analytics available in the market.”

Definitive Healthcare will immediately begin integrating the datasets and platform functionality into a single source of truth, their executives attest. The new offering will aim to include improved coverage of IT purchasing intelligence with access to years of proposals and executed contracts, enabling transparency and efficiency in the development of commercial strategies.

Broadly, Definitive Healthcare is a provider of data and intelligence on hospitals, physicians, and other healthcare providers. Its product suite its product suite provides comprehensive data on 8,800 hospitals, 150,000 physician groups, 1 million physicians, 10,000 ambulatory surgery centers, 14,000 imaging centers, 86,000 long-term care facilities, and 1,400 ACOs and HIEs, according to officials.

Together, Definitive Healthcare and HIMSS Analytics have more than 20 years of experience in data collection through exclusive methodologies.

“HIMSS Analytics has developed an extraordinarily powerful dataset including technology install data and purchasing contracts among other leading intelligence that, when combined with Definitive Healthcare’s proprietary healthcare provider data, will create a truly best-in-class solution for our client base,” Jason Krantz, founder and CEO of Definitive Healthcare, said in a statement.

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Machine Learning Survey: Many Organizations Several Years Away from Adoption, Citing Cost

January 10, 2019
by Heather Landi, Associate Editor
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Radiologists and imaging leaders see an important role for machine learning in radiology going forward, however, most organizations are still two to three years away from adopting the technology, and a sizeable minority have no plans to adopt machine learning, according to a recent survey.

A recent study* by Reaction Data sought to examine the hype around artificial intelligence and machine learning, specifically in the area of radiology and imaging, to uncover where AI might be more useful and applicable and in what areas medical imaging professionals are looking to utilize machine learning.

Reaction Data, a market research firm, got feedback from imaging professionals, including directors of radiology, radiologists, chiefs of radiology, imaging techs, PACS administrators and managers of radiology, from 152 healthcare organizations to gauge the industry on machine learning. About 60 percent of respondents were from academic medical centers or community hospitals, while 15 percent were from integrated delivery networks and 12 percent were from imaging centers. The remaining respondents worked at critical access hospitals, specialty clinics, cancer hospitals or children’s hospitals.

Among the survey respondents, there was significant variation in the number of annual radiology studies performed—17 percent performed 100-250 thousand studies each year; 16 percent performed 1 to 2 million studies; 15 percent performed 5 to 25 thousand studies; 13 percent performed 250 to 500 thousand; 10 percent performed more than 2 million studies a year.

More than three quarters of imaging and radiology leaders (77 percent) view machine learning as being important in medical imaging, up from 65 percent in a 2017 survey. Only 11 percent view the technology as not important. However, only 59 percent say they understand machine learning, although that percentage is up from 52 percent in 2017. Twenty percent say they don’t understand the technology, and 20 percent have a partial understanding.

Looking at adoption, only 22 percent of respondents say they are currently using machine learning—either just adopted it or have been using it for some time. Eleven percent say they plan to adopt the technology in the next year.

Half of respondents (51 percent) say their organizations are one to two years away (28 percent) or even more than three years away (23 percent) from adoption. Sixteen percent say their organizations will most likely never utilize machine learning.

Reaction Data collected commentary from survey respondents as part of the survey and some respondents indicated that funding was an issue with regard to the lack of plans to adopt the technology. When asked why they don’t ever plan to utilize machine learning, one respondent, a chief of cardiology, said, “Our institution is a late adopter.” Another respondent, an imaging tech, responded: “No talk of machine learning in my facility. To be honest, I had to Google the definition a moment ago.”

Survey responses also indicated that imaging leaders want machine learning tools to be integrated into PACS (picture archiving and communication systems) software, and that cost is an issue.

“We'd like it to be integrated into PACS software so it's free, but we understand there is a cost for everything. We wouldn't want to pay more than $1 per study,” one PACS Administrator responded, according to the survey.

A radiologist who responded to the survey said, “The market has not matured yet since we are in the research phase of development and cost is unknown. I expect the initial cost to be on the high side.”

According to the survey, when asked how much they would be willing to pay for machine learning, one imaging director responded: “As little as possible...but I'm on the hospital administration side. Most radiologists are contracted and want us to buy all the toys. They take about 60 percent of the patient revenue and invest nothing into the hospital/ambulatory systems side.”

And, one director of radiology responded: “Included in PACS contract would be best... very hard to get money for this.”

The survey also indicates that, among organizations that are using machine learning in imaging, there is a shift in how organizations are applying machine learning in imaging. In the 2017 survey, the most common application for machine learning was breast imaging, cited by 36 percent of respondents, and only 12 percent cited lung imaging.

In the 2018 survey, only 22 percent of respondents said they were using machine learning for breast imaging, while there was an increase in other applications. The next most-used application cited by respondents who have adopted and use machine learning was lung imaging (22 percent), cardiovascular imaging (13 percent), chest X-rays (11 percent), bone imaging (7 percent), liver imaging (7 percent), neural imaging (5 percent) and pulmonary imaging (4 percent).

When asked what kind of scans they plan to apply machine learning to once the technology is adopted, one radiologist cited quality control for radiography, CT (computed tomography) and MR (magnetic resonance) imaging.

The survey also examines the vendors being used, among respondents who have adopted machine learning, and the survey findings indicate some differences compared to the 2017 survey results. No one vendor dominates this space, as 19 percent use GE Healthcare and about 16 percent use Hologic, which is down compared to 25 percent of respondents who cited Hologic as their vendor in last year’s survey.

Looking at other vendors being used, 14 percent use Philips, 7 percent use Arterys, 3 percent use Nvidia and Zebra Medical Vision and iCAD were both cited by 5 percent of medical imaging professionals. The percentage of imaging leaders citing Google as their machine learning vendor dropped from 13 percent in 2017 to 3 percent in this latest survey. Interestingly, the number of respondents reporting the use of homegrown machine learning solutions increased to 14 percent from 9 percent in 2017.

 

*Findings were compiled from Reaction Data’s Research Cloud. For additional information, please contact Erik Westerlind at ewesterlind@reactiondata.com.

 

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Drexel University Moves Forward on Leveraging NLP to Improve Clinical and Research Processes

January 8, 2019
by Mark Hagland, Editor-in-Chief
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At Drexel University, Walter Niemczura is helping to lead an ongoing initiative to improve research processes and clinical outcomes through the leveraging of NLP technology

Increasingly, the leaders of patient care organizations are using natural language processing (NLP) technologies to leverage unstructured data, in order to improve patient outcomes and reduce costs. Healthcare IT and clinician leaders are still relatively early in the long journey towards full and robust success in this area; but they are moving forward in healthcare organizations nationwide.

One area in which learnings are accelerating is in medical research—both basic and applied. Numerous medical colleges are moving forward in this area, with strong results. Drexel University in Philadelphia is among that group. There, Walter Niemczura, director of application development, has been helping to lead an initiative that is supporting research and patient care efforts, at the Drexel University College of Medicine, one of the nation’s oldest medical colleges (it was founded in 1848), and across the university. Niemczura and his colleagues have been partnering with the Cambridge, England-based Linguamatics, in order to engage in text mining that can support improved research and patient care delivery.

Recently, Niemczura spoke with Healthcare Informatics Editor-in-Chief Mark Hagland, regarding his team’s current efforts and activities in that area. Below are excerpts from that interview.

Is your initiative moving forward primarily on the clinical side or the research side, at your organization?

We’re making advances that are being utilized across the organization. The College of Medicine used to be a wholly owned subsidiary of Drexel University. About four years ago, we merged with the university, and two years ago we lost our CIO to the College of Medicine. And now the IT group reports to the CIO of the whole university. I had started here 12 years ago, in the College of Medicine.

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And some of the applications of this technology are clinical and some are non-clinical, correct?

Yes, that’s correct. Our data repository is used for clinical and non-clinical research. Clinical: College of Medicine, College of Nursing, School of Public Health. And we’re working with the School of Biomedical Engineering. And college of Arts and Sciences, mostly with the Psychology Department. But we’re using Linguamatics only on the clinical side, with our ambulatory care practices.

Overall, what are you doing?

If you look at our EHR [electronic health record], there are discrete fields that might have diagnosis codes, procedure codes and the like. Let’s break apart from of that. Let’s say our HIV Clinic—they might put down HIV as a diagnosis, but in the notes, might mention hepatitis B, but they’re not putting that down as a co-diagnosis; it’s up to the provider how they document. So here’s a good example: HIV and hepatitis C have frequent comorbidity. So our organization asked a group of residents to go in and look at 5,700 patient charts, with patients with HIV and hepatitis C. Anybody in IT could say, we have 677 patients with both. But doctors know there’s more to the story. So it turns out another 443 had HIV in the code and hep C mentioned in the notes. Another 14 had hep C in the code, and HIV in the notes.

So using Linguamatics, it’s not 5,700 charts that you need to look at, but 1,150. By using Linguamatics, we narrowed it down to 1,150 patients—those who had both codes. But then we found roughly 460 who had the comorbidity mentioned partly in the notes. Before Linguamatics, all residents had to look at all 5,700 charts, in cases like this one.

So this was a huge time-saver?

Yes, it absolutely was a huge time-saver. When you’re looking at hundreds of thousands or millions of patient records, the value might be not the ones you have to look at, but the ones you don’t have to look at. And we’re looking at operationalizing this into day-to-day operations. While we’re billing, we can pull files from that day and say, here’s a common co-morbidity—HIV and hep C, with hep C mentioned in those notes—and is there a missed opportunity to get the discrete fields correct?

Essentially, then, you’re making things far more accurate in a far more efficient way?

Yes, this involves looking at patient trials on the research side, while on the clinical side, we can have better quality of care, and more updated billing, based on more accurate data management.

When did this initiative begin?

Well, we’ve been working with Linguamatics for six or seven years. Initially, our work was around discrete fields. The other type of note we look at has to do with text. We had our rheumatology department, and they wanted to find out which patients had had particular tests done—they’re looking for terms in notes… When a radiologist does a report on your x-ray, it’s not like a test for diabetes, where a blood sugar number comes out; x-rays are read and interpreted. The radiologists gave us key words to search for, sclerosis, erosions, bone edema. There are about 30 words. They’re looking for patients who have particular x-rays or MRIs done, so that instead of looking for everyone who had these x-rays done, roughly 400 had these terms. We reduced the number who were undergoing particular tests. The rheumatology department was looking for patients for patient recruitment who had x-rays done, and had these kinds of findings.

So the rheumatology people needed to identify certain types of patients, and you needed to help them do that?

Yes, that’s correct. Now, you might say, we could do word search in Microsoft Word; but the word “erosion” by itself might not help. You have to structure your query to be more accurate, and exclude certain appearances of words. And Linguamatics is very good at that. I use their ontology, and it helps us understand the appearance of words within structure. I used to be in telecommunications. When all the voice-over IP came along, there was confusion. You hear “buy this stock,” when the message was, “don’t buy this stock.”

So this makes identifying certain elements in text far more efficient, then, correct?

Yes—the big buzzword is unstructured data.

Have there been any particular challenges in doing this work?

One is that this involves an iterative process. For someone in IT, we’re used to writing queries and getting them right the first time. This is a different mindset. You start out with one query and want to get results back. You find ways to mature your query; at each pass, you get better and better at it; it’s an iterative process.

What have your biggest learnings been in all this, so far?

There’s so much promise—there’s a lot of data in the notes. And I use it now for all my preparatory research. And Drexel is part of a consortium here called Partnership In Educational Research—PIER.

What would you say to CIOs, CMIOs, CTOs, and other healthcare IT leaders, about this work?

My recommendation would be to dedicate resources to this effort. We use this not only for queries, but to interface with other systems. And we’re writing applications around this. You can get a data set out and start putting it into your work process. It shouldn’t be considered an ad hoc effort by some of your current people.

 

 


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