The Patient Engagement Revolution: Why we led Wellth’s $20M Series B

Healthcare costs Americans a staggering $4.1 trillion dollars each year. But what actually drives all that cost? One glaring, seemingly avoidable factor is patient behavior. Six in ten adults in the U.S. have a chronic disease— the conditions that lead to 86% of our healthcare costs. Individual behaviors—attending doctor’s appointments, taking medications as prescribed, and “knowing your numbers” are crucial in managing these chronic conditions. In fact, $300–500 billion each year is wasted just as a result of prescription medication non-adherence and mismanagement, according to the National Institutes of Health.

That’s why we’re leading a $20M Series B round into Wellth, the leader in leveraging behavioral economics to drive adherence to prescribed care and treatment plans. You can read more about the news from Wellth and Fierce Healthcare.

As mentioned above, six in ten Americans suffer from a chronic health issue—including both authors of this piece. That’s more than 200 million of us dealing with ongoing treatment programs. To reduce the waste from non-adherence, Wellth is pioneering better ways to incentivize this group to remain steadfast in their treatment plans, focusing on a massive needle mover: motivation. Specifically, engaging and motivating the population that is most at risk of going astray from healthy decision-making—the 25% of Medicare beneficiaries that incur 96% of the costs(!).

Behavior: The true unlock

Forty percent of health outcomes can be linked directly to personal decisions—not genetics, not quality of care, and not social determinants like income, according to the Kaiser Family Foundation. We all have friends or family who know they need to get to the gym to be healthier but can never quite stick to it. There’s a big difference between knowing what we need to do to take care of our health and actually doing it. Especially for senior citizens and lower-income Americans—people who may not have the time, energy or motivation to keep up with daily health choices—leading patients to disengage from their care plans.

Disengaged behavior takes many forms: skipping appointments, forgetting medication, not checking key health signs frequently, etc. These may sound like minor slip-ups, but they can often cause extremely harmful (and expensive) effects down the line. Consider:

Heart attack: Patients who don’t take their heart medications after a heart attack are 2x more likely to have a second heart attack in the next three months.
Kidney disease: Patients with end-stage renal disease (ESRD) who miss one dialysis appointment are 3x as likely to end up in the hospital (and have 50% higher mortality).
Diabetes: Patients with diabetes who don’t check their glucometers or miss oral insulin prescriptions can be unaware of their blood sugar being either far too high or far too low. There were more than two million diabetes-related emergency room visits annually in the U.S. in 2021.

How Wellth helps

Wellth uses behavioral economics to drive better outcomes, primarily through a monthly rewards system. Patients use the Wellth app to document daily healthy behaviors (like taking their medication as directed)—and depending on the activity, a fraction is deducted from their reward if it’s not completed.

Wellth’s programs are delivered by leveraging key behavioral psychology principles such as:

Loss aversion: A $10 loss feels worse to us than how good a $10 gain feels
The intent-behavior gap: What we plan to do doesn’t translate to what we will do
The endowment effect: We value things more when we own them

Wellth partners with some of the nation’s largest health insurance companies (e.g., United Healthcare, Centene) to motivate their most vulnerable members. Wellth reaches out to these people, gets them to download the app, and motivates daily action with their treatment plan.

The rewards members receive are subsidized by those health insurers, and depending on the program, they are triggered by different user inputs—it may be snapping a picture of pills in your hand, showing a glucometer reading, or confirming presence at appointments. In each case, it’s as easy as texting a photo. That’s why Wellth has engagement rates that are completely unheard of in healthcare. 91% of Wellth’s users use the app every day. That’s 3x the engagement rate of TikTok’s user base.

In short, Wellth uses modern technology to get people to actually make the decisions that they know they ought to make, benefitting both their physical health, and the health of the U.S. healthcare system as a whole. Wellth’s co-founders, CEO Matt Loper and CTO Alec Zopf, have kept their mission of a healthier America front-and-center as they built this company together, leveraging expertise from prior careers in healthcare advisory and data engineering.

The proof that Wellth works

Great, so people log in and use the app. But that sounds like we’re spending more money, doesn’t it? Where’s the proof that people engaging with the app actually saves money downstream, by avoiding ER visits and the like?

Having served more than 30,000 patients, the outcomes are nothing short of a revelation:

Missed appointments: Decreased 15% (keeping the system moving efficiently)
Emergency room visits: Decreased 29% (ER visits are incredibly costly)
Days spent in the hospital: Decreased 42% (hospitalizations are even more costly)
Annual savings per patient: Over $2,500
Total savings to the U.S. healthcare system: $50+ million to date

Across the estimated 30 million polychronic and disengaged individuals in the U.S., Wellth could eventually reduce the cost of the U.S. health system by over $75 billion.

Why Wellth chose SignalFire

Before investing in Wellth, SignalFire spoke to more than 50 companies in the patient engagement space. We determined that patient behavior change around medication non-adherence was the most important problem to solve. When we saw the engagement rate, the increased adherence, and the savings to health plans Wellth was generating, we knew we’d found a winner.

Every investment is a two-way street. So once we decided to work with Wellth, they had to decide to work with us. When asked why they did, Wellth’s CEO Matt Loper told us:

“Every venture fund I’ve ever met has promised to add value beyond capital—but SignalFire is the first I’ve known to exceed all expectations. They uniquely understand the challenges founders face and have purpose-built their team to help them overcome those challenges. Every team member is an expert in their field that goes above and beyond to help their portfolio companies. Chris Farmer and Chris Scoggins have seen firsthand how companies define industries and scale. Tawni and Heather help us think through how to best grow our team. YY and Tony immediately became a key extension of our executive team. And of course, there’s SignalFire’s XIR Frank Williams, who is a force of nature and would be a first-ballot inductee into the Healthcare Hall of Fame. Working with Frank and the whole SignalFire team has already proved to be a dream come true.” —Matt Loper, CEO of Wellth

Matt is referring to the unique Executive-in-Residence (XIR) program that SignalFire designed for situations just like this one: pairing A) startups serving a clear market need and with the potential to be generation-defining companies, with B) deeply experienced industry leaders in their field who have founded and scaled massively influential companies.

In this case, to help find a company working on a solution to the patient motivation problem, SignalFire partnered with XIR Frank Williams, former CEO of the Advisory Board Company (acquired by Optum for $2.5B) and founder/CEO of Evolent Health (NYSE:EVH, $3B+ market cap).

Williams, who is joining as the chairman of Wellth’s board of directors, shared, “I haven’t seen a venture firm that has made such a comprehensive investment in supporting entrepreneurs in scaling their businesses successfully.” He added:

What’s next?

Health outcomes are driven by behaviors, and Wellth is fundamentally evolving the way we think about healthcare behavior change. We are incredibly excited to be a part of the journey alongside Matt, Alec, and the entire L.A.-based Wellth team, as well as their new chairman Frank Williams. There are so many systemic issues in today’s healthcare system that it can feel impossible to change, but Wellth is showing us that with technology and an innovative approach to patient motivation, we can make a real difference.

And they’re hiring! If you want to help make a change in the way we take care of ourselves in this country, reach out!

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*SignalFire may engage Affiliate Advisors, Retained Advisors, and other consultants as listed above to provide their expertise on a formal or ad hoc basis. They are not employed by SignalFire and do not provide investment advisory services to clients on behalf of SignalFire. For more information on their specific roles, please contact us. Portfolio Company Endorsements: Certain portfolio company founders or Affiliate Advisors listed above may or may not be current investors in a SF fund in which they receive a fee reduction. Such fee reductions were not provided in exchange for or an incentive for their feedback, nor contingent upon the individual’s approval for SignalFire’s continued use. Please refer to our website for additional disclosures.

Why Signalfire funded CodaMetrix to fix medical billing

Healthcare is under threat of an enormous labor shortage, caused in large part by an aging workforce. Across the industry—physicians, nurses, administrators—many workers are expected to retire in the next decade, with not enough young people entering the profession to replace them. According to the American Journal of Nursing, 4 million nurses are expected to retire by 2030. Add to that an increasing rate of burnout due to COVID.

One particularly interesting, relatively unknown healthcare profession that’s also been affected is the medical coder. It’s a niche role—there are approximately 140,000 people in the U.S. and an additional 150,000 globally, in the trade. A coder’s job is to take doctors’ notes and come up with a combination of one or more Current Procedural Terminology (CPT) codes, and one or more ICD-10 codes (International Classification of Diseases, 10th Revision) that are used to express the type of care that was delivered to meet payers’ reimbursement guidelines and requirements. There are literally billions of combinations.

Depending on the type of care provided, this could quickly become a very complex task. For example, if you go to your primary care doctor for a routine appointment, it’s usually a highly structured visit where several standard checks are performed, maybe a blood test is ordered, and you’re good to go. But let’s think about something like an inpatient visit: you’re staying in a hospital for a week because you just had surgery; you have multiple, varied interactions with your surgeon, the anesthesiologist, other specialists, and multiple nurses; all over the course of those seven days. The work to record this activity accurately is enormous. The “doctors’ notes” from your weeklong stay can become the length of a term paper. Coders then have to extract the key information to generate dozens and dozens of billing codes, or worse yet in some cases the physicians have to do that themselves, which takes them away from patient care.

Across the U.S., health systems spend a collective total of $9 billion annually on medical coding. And despite the large spend, the current state of coding is challenging—it’s highly manual, and so errors happen— in fact the recent Becker’s Hospital CFO Report shows 42% of denials are due to coding inaccuracies. Currently, miscoding can lead to costly back-and-forth communications between Providers and Payers, and undercoding can lead to Providers being underpaid for necessary services they delivered to patients.

Medical coding should be largely automated—enter “Applied AI”

Given SignalFire’s strength in data and analytics, AI, and specifically machine learning (ML) and natural language processing (NLP), we were particularly excited about how automation can play an outsized role in the medical coding space for four key reasons:

Coders are quickly aging out of the workforce. The average medical coder is approximately 50 years old, and fewer young people have been entering the field. Providers must find a way to scale their coding capacity, while making their existing employees more productive.
Coders are humans and humans make mistakes. We’ve all heard hospitals complain about Payers denying some portion of their claims. On the other hand, Payers complain that hospitals try to squeeze more reimbursement from them by billing for more services than were performed—a concept called “upcoding.” In reality, both sides are right, but very rarely do claims get denied because of fraud (intentional upcoding). Instead, the problem is usually human error. In the weeklong inpatient visit example above, it wouldn’t be unusual for a coder to make mistakes and input incorrect codes. Also, the list of codes changes and new codes are released annually or quarterly. Coders need training to keep up with the latest and they have to keep up with their CEU (Continuing Education Units) to maintain their certifications.
ML and NLP technology advancements have now made coding automation feasible with high accuracy. Natural language processing technology was not mature enough until recently to tackle challenges for automated coding. Many of these NLP systems required hand-annotated training data, which had poorer quality results. It wasn’t until 2013 that there was a big breakthrough in deep-learning–based methods (starting with a language model called word2vec), which created a step function improvement in accuracy across all NLP tasks. For example, Google Translate was first publicly released in 2006 but was able to improve accuracy by 60% when they switched to a deep learning model in 2016. Furthermore, NLP applied to the medical field saw exponential improvement in 2020 as researchers released deep learning language models focused specifically on medical notes (called BioBERT).
More compliant adoption of Electronic Health Record systems (“EHRs”) and highly scalable and affordable cloud computing. US healthcare Providers and Payers alike have made significant investments in electronification of both clinical and billing data. Now, this large corpus of data can be used by fairly reliable, secure, and affordable cloud and AI infrastructure to both clean the data to develop the ground truth data needed to train Machine Learning (ML) models that not only take advantage of more advanced NLP, but also add the pattern recognition and statistical analysis dimension to produce even better results.

Why now?

Between 2014 and 2018 many startups began working to apply the more mature NLP systems to medical coding. Fast forward to 2023 and we now finally see a small cohort of autonomous medical coding companies that have achieved product-market fit but are still in the early stages of crossing the chasm to achieve adoption widely. It’s not a surprise that companies have taken several years to get to this point. It requires an enormous amount of time to obtain a large training data set, train and backtest the models until they work, convince the typical hyper-conservative-unwilling-to-try-any-new-technology healthcare organization to use their solution, and finally integrate with all of their existing workflow solutions—all before any revenue is generated. But we believe the market is finally ready for these solutions, because ML and NLP technology advancements have enabled these solutions to actually deliver the results they claim.

Why CodaMetrix?

When we built a market map of all the medical coding automation companies that have product-market fit (defined as having at least a handful of customers and a minimum of several million dollars of revenue) there were only a few that made the list. At SignalFire, we believe the companies that will win over time are those with products built on a true machine learning approach and with teams that have experience working inside US-based health systems. CodaMetrix was the only company with both.

ML is the right platform approach to this technology problem. We think it’s best positioned to extend across specialities over time. The alternative to using an ML model is a simpler rules-based NLP system—the concept that if a doctor wrote X, Y and Z keywords and phrases, then output would be specific billing (CPT and ICD) codes. However, the pure rules-based NLP systems do not scale well because changes in documentation style and every subsequent speciality product developed will require a new set of rules to be programmed. When a system relies on a large set of rules, that’s when it becomes “brittle” and will require lots of caring and feeding to function properly. Currently, companies in this space begin with one or two specialities (for instance, CodaMetrix started with radiology and pathology, and has now expanded to offer surgery, inpatient coding, etc.). But as the autonomous coding platforms need to handle a larger number of specialties and subspecialties, the ML approach, vs. NLP-centric, promises to have the chops to handle the complexities.

However, ML platforms are useless without an abundance of accurate training data. Because CodaMetrix was incubated and launched inside Mass General Brigham (MGB)—one of the largest and most innovative health systems in the country—it has had unparalleled access to large volumes of training data on any specialty. The company’s cutting edge deep learning models can quickly “learn” the terminology and coding guidelines (or “patterns”) of a new speciality, enabling a rapid product development cycle. This allows CodaMetrix to get its new, tested products into the hands of customers faster than the competition.

One important, yet subtle, feature of its platform is “code transparency.” Because the company started from day one as a product built for a health system (MGB), the tech team also creatively designed the architecture to solve for a key challenge in the industry—showing the reasoning behind its AI decisions. Most machine learning models are black boxes, but CodaMetrix has a built-in audit trail feature in their product so one can see what information the system specifically used to reach its conclusion. This is incredibly rare, yet valuable, in machine learning solutions and a must-have in healthcare, where Providers are frequently audited by Payers to determine if they are coding appropriately.

Example of CodaMetrix’s audit assistant

CodaMetrix’s ability to build products tailored for health systems is driven by an experienced team who deeply understand the ecosystem. The executive team, which has decades of experience working inside and with health systems, is led by CEO Hamid Tabatabaie who was previously an Entrepreneur-in-Residence at MGB, and formerly the CEO and Founder of LifeIMAGE.

The early results are impressive. Today, CodaMetrix is a multi-specialty platform that classifies codes across radiology, pathology, surgery, gastroenterology, and inpatient professional coding with customers across 10 health systems and major academic universities, representing 111 hospitals in 40 states, including Mass General Brigham, University of Colorado Medicine, Yale Medicine, and Henry Ford Health Systems. Customers using CodaMetrix have seen significant increase in cost savings and cash acceleration—from 70% reduction in manual labor, 59% reduction in denials, and improved cash collection by as many as 47 days.

Why CodaMetrix and MGB chose SignalFire

SignalFire is honored to be leading CodaMetrix’s $55M Series A alongside Frist Cressey Ventures, Martin Ventures, Yale Medicine, CU Healthcare Innovation Fund, and Mass General Brigham physician organizations. SignalFire’s proprietary AI data platform Beacon—which tracks 495 million employees and 80 million companies—stood out to CodaMetrix’s CEO Hamid Tabatabaie as a demonstration of our depth in AI and data as one of the key reason why he chose us to lead the Series A, amongst many others.

“SignalFire’s mantra of adding value beyond the check was evident from the onset and further pronounced with every step along the process. The impeccable combination of Yuanling Yuan’’s in-depth industry research, Tom Peterson’s continuous involvement, SignalFire’s in-house AI and data expertise, Chris Scoggins’ infectious drive for scale, Chris Farmer’s refreshing healthcare thesis, and the brilliant team of experts in talent, operations, and go-to-market strategists are clear reasons for us to have selected them as the lead investor.’ – Hamid Tabatabaie, CodaMetrix CEO

We are also thrilled to have the support of Mass General Brigham in selecting SignalFire as a long-term partner for CodaMetrix.

“Mass General Brigham is delighted to have SignalFire lead the Series A and attract a strong syndicate. SignalFire brings to CodaMetrix deep AI/ML and go-to-market commercialization expertise that will ensure CodaMetrix continues to build on those capabilities developed at Mass General Brigham and lead the market in autonomous medical coding to help drive down healthcare administrative costs and reduce physician burnout. In the short time that SignalFire has been involved with CodaMetrix, they have already demonstrated how their focus on data and GTM expertise will add value.” — Gaye Bok, Partner of AI and Digital Innovation Fund, Mass General Brigham

The team at CodaMetrix was excited about SignalFire’s unique XIR program, which pairs deeply experienced tech industry leaders from the firm’s Advisor Network with high-potential portfolio companies to help accelerate their growth. Tom Peterson, SignalFire’s XIR who will be very actively involved at the CodaMetrix board level and advising on day-to-day operations, boasts 20+ years of deep healthcare experience as the co-founder and former COO of Evolent Health (NYSE:EVH) and former executive director of the Advisory Board Company (acquired by United Health and Vista Equity Partners for $2.6B).

“Given the strength of the leadership team and the connection to MGB, I was really impressed with the depth of CodaMetrix’s understanding of health systems and how that was combined with best-in-class technology to develop industry leading coding automation products. CodaMetrix is the only coding automation company out there that is built for the health system ecosystem by people who deeply understand it.” — Tom Peterson, SignalFire XIR and co-founder and former COO of Evolent Health (NYSE:EVH)

We are incredibly excited to see how the medical coding automation space evolves over the next decade. This solution is coming just in time, as hiring managers in the industry are likely to face a steep challenge replacing a soon-to-be retiring workforce. With investments like CodaMetrix, we see a future where healthcare Providers can save on labor costs, ensure proper billing for services rendered and achieve faster pay reimbursement thanks to a much lower rate of claim denials. All of these will help our overburdened health systems to remain financially solvent as they try to keep up with the rapidly increasing needs of U.S. patients.

*SignalFire may engage Affiliate Advisors, Retained Advisors, and other consultants as listed above to provide their expertise on a formal or ad hoc basis. They are not employed by SignalFire and do not provide investment advisory services to clients on behalf of SignalFire. For more information on their specific roles, please contact us. Portfolio Company Endorsements: Certain portfolio company founders listed above have not received any compensation for this feedback and did not invest in a SignalFire fund. Please refer to our website for additional disclosures.

Care-Now-Pay-Later: Why SignalFire is leading $15M for PayZen

79 million Americans have medical debt or bill problems. Sadly, the inability to pay or fear of debt has led 1 in 3 Americans to delay or decline essential medical care.

This has to change. And that’s why we’re leading a $15 million Series A for PayZen, the care-now-pay-later company.

PayZen helps any patient get on a zero-interest, zero-fee custom payment plan so they can receive the care they need immediately without going into debt. How? PayZen’s AI analyzes thousands of data points about their financial situation to ensure they’re on a payback schedule they can afford, instead of the industry-standard one-size-fits-all plan. That’s why 87% of patients offered PayZen enroll, and it increases their payment adherence by 40% within one month. You can learn more about how PayZen works here.

PayZen lets patients choose and track their healthcare payment plan

Meanwhile, PayZen helps hospitals increase collections by 52%. Typically, hospitals collect just 15 cents of every dollar they charge while sending people to collection services that destroy their credit score. By integrating PayZen, care providers give their patients a more understanding and affordable approach. They share a portion of the extra payments they receive with PayZen so patients aren’t charged extra fees.

Building a true win-win-win solution like this takes a combination of healthcare veterans, fintech experts, and skilled engineers. That’s the team PayZen assembled. CEO Itzik Cohen, COO Tobias Mezger, and CTO Ariel Rosenthal had previously worked together and seen how legacy approaches had failed providers and patients. They’ve joined forces with Kevin Roberts, the CFO of 3 million patient healthcare group Geisinger, and industry legend Lawrence Leisure who co-founded ADVI Health, Healthspottr, and healthcare investment fund Chicago Pacific Partners.

PayZen founders (from left): Ariel Rosenthal, Itzik Cohen, Tobias Mezger

Together, we see their potential to address the healthcare affordability crisis plaguing our country. Many of our LPs at SignalFire are large health systems, and their leaders told us how badly they need a solution like PayZen. We’re honored to get to support the company with our Beacon Talent recruiting technology, in-house AI experts, and network introductions team.

PayZen is the kind of startup that makes us proud to be investors. When technology is applied with empathy, it can solve some of society’s greatest challenges. We can’t think of a larger or more pressing one than ensuring everyone can afford the healthcare they need.

Image Credit: Mix and Match Studio. Cited Statistics