machine learning AI

The evolution of machine learning is still in its early stages, but at the HLTH 2022 conference this week, companies shared how they are working to fine tune their approaches to AI. Those efforts included everything from improving the quality of patient data that underpin the algorithms, which has been criticized for not reflecting a diverse enough patient population, to making it easier for healthcare organizations to validate their effectiveness. Health tech companies also highlighted different approaches they are taking to how they work with providers to pilot machine learning algorithms and market them.

Dr. John Halamka, president of Mayo Clinic Platform, used his talk at HLTH to highlight an initiative to assess and reduce bias in patient data to improve the effectiveness of machine learning algorithms. Launched three years ago, Mayo Clinic Platform built an ecosystem to coordinate collaborations with health tech companies to enable innovation in healthcare.

Halamka’s talk on the “algorithmically underserved” noted that currently, when healthcare organizations use an algorithm, they often have no idea whether it performs well or not. The goal of its AI validation platform, Mayo Clinic Platform _ Validate, is to provide clinical validation for machine learning algorithms.

Mayo Clinic Platform is also partnering with other healthcare organizations to set standards and reporting for models as part of The Coalition for Health AI. In addition to Mayo, other founding members include University of California Berkeley, Duke Health, Johns Hopkins University, MITRE, Stanford Medicine, and University of California San Francisco. Industry members include Change Healthcare, Google, Microsoft, and SAS.

Halamka announced that a webinar is planned for December 7, which will offer a preview of plans for a public-private partnership to create a national register to assess the usefulness of a variety of healthcare algorithms.

“We feel we need a national set of assurance standards for algorithms,” Halamka said. The registry will host the metadata for algorithms produced in healthcare.

AI marketplace

Health tech companies are also developing marketplaces to improve the way collaboration partners, such as providers, payers and research groups, select algorithms.

“Data may be the new oil but the data needs to be refined,” said Wavemaker Three-Sixty Health General Partner Jay Goss. One of its portfolio companies, Gradient Health, partners with medical data providers around the globe (generally hospitals and imaging centers) to curate annotated medical images for AI research labs and corporations, so that they don’t have to do one-off deals with hospitals to obtain the data. Companies can search through segmented and labeled studies, or request a custom dataset, spending less time tracking down data and more time developing new tools.

AI hubs

Ferrum Health developed a program to enable health systems to assess machine learning algorithms without exposing their de-identified patient data to a cloud or otherwise forcing them to centralize that data. The company, which is part of the United Healthcare Accelerator 2022 cohort, exhibited on the accelerator’s pavilion.  Ferrum’s approach enables these tests to be done on-premises, behind a firewall, an approach David Miller, Ferrum’s vice president of sales – West, said is designed to de-risk their business for hospitals and health systems. The algorithms in its marketplace are FDA approved.

“We run a test of the algorithms using the hospital’s de-identified patient data to show how they perform for them,” said West. “We let our clients try it before they buy it.”

The company’s four AI Hubs include: oncology, orthopedics, cardiovascular, and breast care.

Reducing physician burnout

DeepScribe exhibited as part of the Plug and Play accelerator’s footprint at the conference. Its automated physician natural language processing software automatically summarizes a physician’s conversation with their patient and auto-populates those notes into EMR fields. Among the EMR companies it works with are athenahealth, dr chrono, AdvancedMD and Claimpower.

Earlier this year, DeepScribe closed a $30 million Series A round to support the company’s growth. The business is designed to negate the need for an in-person medical scribe, saving clinicians money.

The validation approach to algorithms seems like a natural progression in machine learning, similar to the rise of digital health apps followed by the need to validate them to ensure adoption by healthcare organizations skeptical of overhyped tech. It’s a natural progression balancing the interest in machine learning with the recognition that healthcare algorithms are not created equal.

Photo: Hemera Technologies, Getty Images

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When IBM first launched its Watson Health unit in 2015, it had to live up to a grandiose vision. The company’s AI creation, popularized by its win on Jeopardy!, was pitched to oncologists as a tool that could comb through medical literature and cancer patients’ health records, detecting patterns that they could not.

Reports later found that it fell short of those claims, and in some cases, offered ‘unsafe and incorrect’ suggestions. Now, IBM might be considering selling its Watson Health unit, as it focuses more on its cloud computing business, according to the Wall Street Journal. Citing anonymous sources, the Journal reported the health unit brought in $1 billion in revenue and isn’t currently profitable.

IBM declined to comment on the report, and it’s not clear who would buy the company. But it’s another example of a high-flying healthcare effort that might have tried to do too much all at once, and a case where marketing overtook the science.

Other tech behemoths have also stumbled in their much-vaunted plans to disrupt healthcare. Haven, a joint venture between Amazon, JPMorgan Chase and Berkshire Hathaway to combat rising healthcare costs, dissolved as the three companies pursued their own efforts.

Part of the challenge is that it’s difficult for these large companies to move quickly, while in the same span, dozens of startups are bringing their own solutions to market.

“There’s a contextual dynamic that large companies will by definition not move as quickly as early-stage innovative companies,” said Michael Greeley, co-founder and general partner with Flare Capital Partners. “A product roadmap that a big tech company might set for the end of the year, by the time committees meet and budget, the year has gone by.”

Two years ago, IBM started winding down sales of Watson for Drug Discovery to pharmaceutical companies, because it wasn’t yielding big enough financial returns. Before that, the general manager of the division also stepped down for a different role at the company.

Facing declining revenues, in an investor call last month, the company’s new CEO, Arvind Krishna, said he was looking to redefine IBM’s future as a cloud platform and AI company.

“This is where we are focusing the bulk of our efforts, time and investments,” he said.

Over-hyped and under-delivered

With the way IBM had marketed Watson for Oncology, “There was clearly always a mismatch in the reality and the promise of what they were going to bring to market,” Greeley said.

More time would have been needed to get closer to that goal. Building AI tools for healthcare requires a huge amount of high-quality data that can be hard to get, and complicated to analyze.

“To date, there’s been far more heat than light,” wrote David Shaywitz, founder of health-tech advisory firm Astounding HealthTech. “There’s a lot of complexity to health data that requires domain expertise to understand, and just sticking a lot of values in a data lake or data swamp and then setting algorithms loose on it hasn’t proved especially productive to date.”

Despite that, Shaywitz still remains optimistic that AI will have a role in medicine in drug development in the future. He pointed to Flatiron Health as an example of one startup that has done well – it was acquired by Roche in 2018 for $1.9 billion.

He said that success relies on the ability for health and tech experts to collaborate as equal partners,  something that’s “vanishingly rare” at big tech, biopharma and healthcare companies.

 Whatever happens with Watson, Greeley still doesn’t see tech companies’ interest in healthcare waning anytime soon, as Amazon wades into the prescription drug market and Google tries to woo more health systems with cloud partnerships.

“I think we’re seeing renewed intrigue by consumer tech, the Googles and Facebooks of the world,” he said. “I think because healthcare is such an important part of the economy, they will continue to be active with acquisitions.”

Photo credit: Getty Images, wigglestick

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Humana and IBM Watson Health are collaborating to provide the insurer’s Employer Group members with access to a conversational AI solution.

The solution, called the IBM Watson Assistant for Health Benefits, is an AI-enabled, cloud-based virtual assistant. The AI assistant gives users information about member benefits, coverage, claims, referrals and healthcare cost estimates, said an IBM Watson Health spokeswomen, who declined to be named, in an email.

The solution will be made available to all members of the Louisville, Kentucky-based payer’s Employer Group, which includes 1.3 million medical and 1.8 million dental members.

IBM is not the only tech giant that is using AI chatbot technology to make inroads in healthcare. Microsoft, for example, has been popular among insurers and providers alike, launching triage chatbots and other AI technology. For IBM, it also affords the chance to prove its value in offering AI services dedicated to healthcare — it stumbled in 2017 in its loftier vision to use the technology to one day revolutionize cancer care and more recently in 2019 when it abandoned the AI product meant to speed up drug discovery.

But Humana believes that IBM Watson Health’s AI assistant will provide several benefits to health plan members, said a spokesman for the insurer, who declined to be named, in an email. Specifically, it will provide personalized answers to questions from members.

“Customers want us to make it easy, meet on their terms, and save them time,” he said. “The Watson [Assistant for Health Benefits] offers immediate answers to the majority of customer questions without [them] having to call in for help.”

In addition, the solution can aid in the move toward price transparency, which is now a part of federal regulations for insurers. Beginning Jan. 1, 2023, insurers must disclose negotiated rates and provide estimates of patient out-of-pocket costs for 500 services and items per a federal rule finalized in October. Payers must make that information publicly available for all items and services starting Jan. 1, 2024.

The IBM AI assistant’s cost transparency tool, which uses historical claims and provider data to calculate cost estimates for members, will help the insurer comply with the federal rule.

This is not the first time Humana and Armonk, New York-based IBM Watson Health have partnered on AI technology. The companies developed the Provider Services Conversational Voice Agent with Watson, which was made available to healthcare providers in 2019.

“Given the success, both parties see considerable value in investing in the co-creation of a new, cloud-native, healthcare-specific product,” said the Humana spokesman. “IBM has the technical experience to optimize the AI platform and Humana has the business expertise to bring forward the desired customer experiences.”

The collaboration between the payer and technology company is coming as the use of AI chatbots is soaring.

Though interest in AI-powered digital assistants was growing prior to 2020, the Covid-19 pandemic accelerated its use. Companies, like chatbot and voice bot company Syllable, found themselves overwhelmed by demand, and health systems like Cincinnati Children’s Hospital Medical Center and Springfield, Illinois-based Memorial Health System quickly developed and deployed the technology.

AI-powered chatbot use is expected to grow and continue to shape healthcare in 2021.

Photo: Gerasimov174, Getty Images

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Paige, A computational pathology startup spun out of Memorial Sloan Kettering, raised $100 million in a series C funding round. The New York-based company is developing clinical decision support tools for pathologists, and plans to use the funds to further advance its technology.

Paige was started in 2018 by Dr. Thomas Fuchs, who spun out the company from research at Memorial Sloan Kettering Cancer Center. The startup had a research agreement to receive de-identified images of digitized pathology slides, which it is using to make AI tools across multiple cancer subtypes, and the Memorial Sloan Kettering holds an equity stake in the company.

Earlier this year, Paige received 510(k) clearance from the Food and Drug Administration for a digital pathology image viewer. It also received a Breakthrough Device designation from the FDA for an AI tool for cancer diagnosis.

None of Paige’s clinical decision support tools are yet cleared to be used for diagnostics in the U.S. But in Europe, it has two CE-marked solutions to detect areas of suspected prostate cancer or breast cancer.

With the new funds, Paige plans to double its headcount, with roughly 70 new employees across its engineering and commercial teams.

“This investment reaffirms the vast potential of the Paige platform for clinical and biopharmaceutical drug development applications,” Paige CEO Leo Grady said in a news release. “These funds will enable us to build additional AI-based products within and outside of oncology, deliver these products to laboratories and clinicians globally, and invest in our talent across engineering and commercial functions.”

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aging, senior, old

The pandemic has cast a spotlight on healthcare’s technological shortcomings, accelerating the industry’s historically slow march toward digitization. Confronted with lockdowns and social-distancing mandates, providers have turned to digital communication and data management, e-visits, and telehealth to continue serving patients. In response, enterprising firms of all shapes and sizes—from tech giants to health-app startups—are scrambling to meet this need with secure, efficient, and reliable technology aimed at streamlining remote care.

For those working on the health IT side of things, this demand surge for digital healthcare feels like the dawning of a new era. It’s incredibly exciting to see providers, payers, and patients beginning to embrace digital innovation and experience the positive impact these technologies have on care delivery. Amid all this excitement, however, I find myself feeling leery of how quickly the industry is shifting toward digitization.

I think back to a recent experience my son and I had at a doctor’s office. When we entered the waiting room, we exchanged quick, nervous glances with the other patients in the space before checking in online. Our only human interaction was a brief conversation with the desk attendant—through a plexiglass divider.

While I know that touchless experiences are all the rage (and for good reason), I wonder: if we’re not careful about our migration toward digital care, will human touch and face-to-face interactions become a thing of the past? And will healthcare lose empathy if it swings too far digitally?

There is power in human touch.
Touch is fundamental to the human experience. It forges personal connections, decodes human emotion, and—from a healthcare perspective—promotes trust and healing.

In his book titled, “In the Hands of Doctors: Touch and Trust in Medical Care,” historian Paul Stepansky explores how American medicine has changed since the 19th century, focusing on the role of touch in building trust between doctors and patients. In it he writes, “Medicine then was all about touching, and patients welcomed their touch. It was integral to doctoring, and partly because physicians were part of the community, medicine was about laying hands.”

Using touch as a powerful healing tool is a practice that spans back even further than what Stepansky documented in his book. According to research published in the International Journal of Complementary & Alternative Medicine, the traditional shamans of the North East Australian rainforest have used touch and talk to heal mental and physical disorders for thousands of years. To the aboriginals, touch and human interaction were key to learning secret information about the body, reliably guiding them to the root of the problem.

Modern-day research corroborates these time-tested beliefs. Researchers have published countless studies over the past decade championing the power of touch and empathy in medicine by showing that:

Touch is something we crave on a primal level, and it has proven to be immensely powerful when comforting, diagnosing, and treating patients. But in our rush to digitize every aspect of the healthcare journey, are we leaving this elemental practice behind?

Technology will never replace human interaction
Effective, modern medicine cannot survive without technology. As someone who works for a rehab therapy software company, I fully understand the impact EMRs, mobile apps, telehealth, and general treatment technologies have on improving patient care and outcomes. Regardless of how intuitive the software—or how advanced the technology—patients will always highly value and seek out human touch because:

  1. They remain wary of AI and other nuanced technologies. According to a recent Harvard Business Review report, “patients believe that their medical needs are unique and cannot be adequately addressed by algorithms.” Patient experiences aren’t meant to be 100% digital. And despite the accuracy of computers, humans prefer to seek care from other human beings.
  2. They have emotional needs. And as such, life-altering diagnoses and unforeseen outcomes are best delivered by a living, breathing, feeling individual who can fully understand and address these needs.
  3. Physical examinations are reassuring and restorative. Abraham Verghese, a physician, author, and Professor for the Theory and Practice of Medicine at Stanford University has spoken extensively about the importance of this rudimentary practice, stating that “when [physicians] shortcut the physical exam, when [they] lean towards ordering tests instead of talking to and examining the patient, [they] not only overlook simple diagnoses…[they’re] losing a ritual that I believe is transformative, transcendent, and is at the heart of the patient-physician relationship.”

We must ask ourselves how we can preserve touch in health care

Unfortunately, I don’t think there’s a clear-cut solution to this question yet. At best, technology helps providers reach more patients, reduces administrative burden, and expands access to treatment. At worst, it creates a physical barrier between provider and patient, extinguishing empathy and damaging patient rapport. All things considered, technology’s sole constant is that it will only be as good as the people who created and are using it.

So, from a health technologist’s perspective, I’ll offer up the following considerations for developing digital health tools in the years to come:

  • Focus on the problems rather than the potential solutions
     It’s easy to get distracted by the sheer number of possible solutions your technology can provide. Instead, prioritize your focus by tackling the problems that will deliver the biggest impact once solved. Then, commit your energy to understanding the nuances of those problems. This mindset keeps patient needs front and center, steering you away from feature-rich products that deliver little benefit.
  • Be mindful of unintended consequences. Digital patient intakes and touchless experiences were created for all the right reasons. Yet, however well-intentioned they may be, digital tools always run the risk of producing unintended consequences. As such, physicians and health technologists must work together to understand what problems might occur (e.g., misdiagnoses, overlooked symptoms, missed chances to develop a rapport with patients) if technology is left unchecked.

Medicine will never progress without technology—there’s no denying that. But for the foreseeable future, human interaction remains an instrumental part of the healthcare experience. So, moving forward, healthcare professionals must find a way to blend the sophistication of technology with the power of touch in order to continue improving patient experiences, care, and outcomes.

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Text-based primary care company Curai Health raised $27.5 million in funding.  The Palo Alto-based startup was co-founded in 2017 by Neal Khosla, who previously worked in machine intelligence at Google, and Xavier Amatriain, who built Netflix’s recommendation engine. Curai also recruited MDLive’s former chief medical officer, Dr. Sylvan Waller.

Khosla said he had the idea for the company as he began thinking more about healthcare access. Why does it take most patients two to three weeks to see a physician, and why do so many people go to Google with their healthcare questions instead of a healthcare professional?

“We have this amazing supply of knowledgeable doctors in our country and we need to figure out how to scale them,” he said.

Up to this point, Curai has been consumer-facing, with a relatively low cost of about $8 per visit. Going forward, the company hopes to build out partnerships with employers, payers, and public sector organizations, to make its text-based primary care service available to more employees.

“We see it as not just being a primary care service, but a safety net for healthcare access,” Khosla said. “We can provide you with a primary care physician to help you navigate over time and build a suite of services around that.”

Curai currently offers two different text-based services. The first is somewhat familiar: users can chat with a provider anytime about an urgent health concern. But the startup has also built out a version of text-based care where patients can chat with the same physician across multiple visits, such as if they had questions about a medication or health condition.

Patients’ care teams include a licensed physician in the U.S. and clinical associates, which are trained physicians overseas.

Behind all of this, Curai is building out an AI decision support tool to work with clinicians. For example, it can help with charting, prompting questions for patients before they start a visit, and pulling important information into a patient’s medical record.

The AI system isn’t trained to work with all conditions, but is building up its capabilities over time, Amatriain said.  The company’s algorithms are trained on de-identified healthcare data, and Amatriain added that the company encrypts all patient data to ensure it is secure.

“The AI is humble in a way because it’s learned when it’s being helpful, whether the doctor has accepted (its suggestion) or not,” he said. “The AI learns to be more helpful and suggest things that are more appropriate given the context of the situation.”

Morningside Ventures led Curai’s series B round. Previous investors General Catalyst and Khosla Ventures — the firm founded by Khosla’s father, Vinod Khosla — also participated in the funding round. Morningside’s Stephen Bruso will join the company’s board of directors as part of the deal.

The startup plans to use the funding to expand its platform to additional states. Curai is currently only available in the state of California, but plans to expand to half of the U.S. by next summer.

Photo credit: Venimo, Getty Images

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Two examples of protein targets in the free modelling category show AlphaFold’s prediction compared to the shape of proteins determined by experimental results. AlphaFold’s predictions are in blue and the experimental results are in green. Screenshot from DeepMind.

DeepMind, the Google subsidiary that has been beating chess and Go players with artificial intelligence, has set its sights on cracking a decades-old problem: predicting the structures of proteins.

At a biennial challenge where participants must blindly predict the structure of 100 proteins based on their amino acid sequences, a system developed by DeepMind captured researchers’ attention when it predicted their shape with a high level of accuracy.

Called AlphaFold, the system determined the shape of around two-thirds of the proteins with an accuracy comparable to time-consuming laboratory experiments. Its accuracy with most of the other proteins was also high, according to results shared by CASP (the Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction) on Monday. The results were compared to the shape of proteins discovered in the lab and were assessed by independent scientists.

This is an important breakthrough because the shape of proteins is closely linked with their function, but it is difficult to predict a protein’s structure based on its amino acid sequence. Proteins can theoretically fold into a multitude of shapes before setting into their final structure. It can take years of research, and expensive equipment, to work out their shape.

“Proteins are extremely complicated molecules, and their precise three-dimensional structure is key to the many roles they perform, for example the insulin that regulates sugar levels in our blood and the antibodies that help us fight infections. Even tiny rearrangements of these vital molecules can have catastrophic effects on our health, so one of the most efficient ways to understand disease and find new treatments is to study the proteins involved,” John Moult, a computational biologist at the University of Maryland in College Park who co-founded CASP, said in a news release.

London-based DeepMind has been working on AlphaFold for four years. It also beat the other teams in the last CASP challenge in 2018, but did so by a much larger margin in the most recent year.

The model’s accuracy is measured using the Global Distance Test, which approximately measures the percentage of amino acid residues within a certain distance from the correct position. In a scale of 1 to 100, DeepMind’s latest AlphaFold system scored a median of 92.4 across all targets.

For the latest iteration of AlphaFold, DeepMind designed a neural network that interprets a protein’s structure as a “spatial graph.” It trained the system on 170,000 protein structures from the protein data bank as well as databases with proteins whose structure was unknown.

This allowed the system to determine structures in a matter of days, the team who developed it wrote in a blog post. An internal confidence measure also indicated which parts of each predicted protein structure are reliable.

What does this all mean? It could have broad implications for drug discovery and better understanding specific diseases. Andrei Lupas, director of the Max Planck Institute for Developmental Biology and a CASP assessor, stated that the system helped his team solve a protein structure that they were stuck on for close to a decade.

Andriy Kryshtafovych, a researcher at UC Davis and one of the judges, described the result as a “triumph for team science,” crediting the collaborative work of researchers over the years to reaching this achievement.

“Being able to investigate the shape of proteins quickly and accurately has the potential to revolutionize life sciences,” he said in a news release. “Now that the problem has been largely solved for single proteins, the way is open for development of new methods for determining the shape of protein complexes – collections of proteins that work together to form much of the machinery of life, and for other applications.”

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Providence, a Renton, Washington-based health system, and artificial intelligence company Nuance Communications have entered into a collaboration with the aim of improving clinical documentation and reducing clinician burnout across Providence’s 51 hospitals.

Providence is one of the largest health systems in the country, with its facilities spanning seven states. The health system has an existing relationship with Nuance and already uses the company’s Dragon Medical One solution in its facilities. The solution is a cloud-based speech recognition platform that translates the clinician’s voice into clinical documentation that is entered into the EHR.

Per the new collaboration, Providence will deploy Nuance’s Dragon Ambient eXperience. The Dragon Ambient eXperience combines conversational AI technology with Microsoft Azure, a cloud computing service, to capture and contextualize the entire patient visit. The clinician and patient can continue their conversation while the technology automatically documents the visit.

“Our partnership with Nuance is helping Providence make it easier for our doctors and nurses to do the hard work of documenting the cutting-edge care they provide day in and day out,” said Dr. Amy Compton-Phillips, executive vice president and chief clinical officer at Providence, in a statement. “The tools we’re developing let our caregivers focus on their patients instead of their keyboards, and that will go a long way in bringing joy back to practicing medicine.”

Clinical documentation and EHR use have been significantly linked to clinician stress and burnout. A survey of 282 clinicians published last year showed 40% of physician burnout is attributable to EHR use, according to EHR Intelligence. It also showed that physicians spend two minutes at the computer for every one minute they spend with patients.

Research shows that reducing the number of tasks a physician is expected to perform has a tangible effect on reducing burnout. A study published earlier this month in The Joint Commission Journal on Quality and Patient Safety found that for every 10% decrease in physician task load the odds of experiencing burnout dropped by 33%.

Providence and Nuance’s expanded collaboration will also include the deployment of CDE One, a cloud-based workflow management and documentation guidance solution, and Nuance’s surgical computer-assisted physician documentation solution.

Further, the two entities will develop integrated clinical intelligence and enhanced revenue cycle solutions.

Providence is not new to using machine learning and AI tools. The health system is already using them to anticipate COVID-19 resurgence and manage personal protective equipment and other finite resources, B.J. Moore, executive vice president and CIO at Providence, said via email.

“As healthcare embraces wearables, internet of things, big data, AI will be imperative to sift through and make sense of the trillions of signals we will be receiving from our communities and patients,” he said.

But the technology is still new, and in its infancy, he added.

“We need to take small incremental steps to prove the value and use of AI. In addition, change management and adoption will be critical as we begin to weave AI into caregiver workflows,” Moore said.

Photo credit: Dmitrii_Guzhanin, Getty Images

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Ontrak, a company that works with insurers to identify employees with untreated behavioral health conditions, acquired health coaching startup LifeDojo. Ontrak CEO Terren Peizer said the deal would expand its footprint with health plans and employers.

The company acquired LifeDojo for about $10 million in cash and equity, Ontrak confirmed in an emailed statement.

“We will endeavor to make additional strategic purchases that expand our addressable market and maximize customer value,” Peizer said in a news release.

Formerly known as Catasys, Ontrak uses analytics to predict people whose chronic conditions will improve with behavior change, and connects them to care coaching and treatment. Some of its customers include insurers Aetna, Centene and Cigna.

LifeDojo would add to Ontrak’s list of coaching services with several programs for behavior change. In addition to modules for healthy eating and exercise, LifeDojo also has programs for managing stress and resilience. The company primarily sells its services to employers.

“We are thrilled to be combining forces with Ontrak,” LifeDojo CEO and Co-Founder Chris Cutter said in a news release. “We are proud to have built a trusted platform for some of the most demanding digital health clients and tech companies in the industry. Like Ontrak, we have engagement rates that are many times higher than the industry average and we look forward to delivering a comprehensive ecosystem of health solutions to high and low acuity Ontrak members.”

Cutter and LifeDojo’s CTO, Patricia Bedard, will join Ontrak’s leadership team. Ontrak said the deal would expand its addressable market by adding a lower-cost digital solution. The data from LifeDojo’s apps would also feed into Ontrak’s AI capabilities and be used to further personalize its coaching.

Photo credit: exdez, Getty Images

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