How AI and analytics could solve healthcare’s big data problems

What’s bigger than one trillion? The number of data points the healthcare industry is producing globally. The industry currently generates 30% of the world’s data volume, and the International Data Corporation (IDC) predicts that there are over 2,314,000,000 terabytes of data today, which has grown a staggering 110X since 2013.

By 2025, healthcare will have become the fastest-growing source of data worldwide. The problem is that while bad data in enterprise means a lost sale, bad data in healthcare could mean a lost life. But that’s also why we at SignalFire see huge opportunities for startups to build better data infrastructure for the healthcare industry.

Why the sudden data explosion within U.S. healthcare? We’re seeing increased adoption of electronic health records (EHRs), regulatory enforcement, and a growing popularity of wearables and other health tracking devices. These are producing a breadth of data types, including patient information, clinical notes, test results, imaging data, and claims data.

However, managing and analyzing this massive amount of data presents significant challenges. To get anything done with the data, you have to solve for the interoperability problem of systems communicating effectively with each other, data normalization, privacy, and security issues—before you even get to the sophisticated applications of data science.

‎Now’s the time, though. The world just got equipped with fresh AI tooling that can make sense of the seas of data flowing out of healthcare. And with everyone thinking about AI, many slower-moving incumbents will feel a sense of urgency to modernize their data stack.

In this post we’ll lay out some of the biggest technology and regulatory shifts affecting the healthcare data space, and a dozen specific opportunities where SignalFire is looking to invest. We’re going deep on healthcare data and the AI space given our bread and butter—we spent the past decade building our own proprietary AI data platform, Beacon, which tracks more than half a trillion data points, giving our portfolio companies unique insights into market intelligence. Our in-house expertise on data and machine learning gives us a unique lens into the power of data in healthcare and specific areas where we’re excited to back founders.

Now let’s dive into the trends and opportunities around AI and analytics for hospitals, payors, pharma, and patients.

Part one: The data infrastructure layer

With AI, it’s garbage in, garbage out, so the industry first needs infrastructure to improve data quality. Before a model or analytics can be built on top of a data set, we need to address the following questions:

  1. Where do we get access to raw data?
  2. How do we cleanse and structure the data?
  3. How do we accurately join different datasets to create a full data record on a single patient?
  4. How do we store this data in a way that protects the patient’s privacy?

Until recently, raw healthcare data was becoming increasingly commoditized. But beginning in 2016 major new regulations emerged, starting with the 21st Century Cures Act.

The Cures Act mandated the bi-directional exchange of patient clinical data through the Trusted Exchange Framework and Common Agreement (TEFCA), with a growing number of approved use cases for data sharing. Essentially, TEFCA required every healthcare organization to make their data more accessible across states, hospitals, and provider networks so patients’ care teams always had the information they needed.

Now, in order to be eligible for access to this shared data, entities must receive the Qualified Health Information Network (QHIN) designation. After QHIN networks are fully established, only QHIN designees will be permitted to access the broader network of U.S. healthcare data, thereby raising the bar for other companies trying to solve the challenges at the data infrastructure layer.

To be awarded the QHIN license, businesses need to build a highly compliant platform that can scale to enormous volumes of data. Among the first six entities to get a QHIN license alongside incumbents like Epic and Commonwell was SignalFire portfolio company Health Gorilla.

With a data lake that has access to the full longitudinal medical records of more than 90% of the U.S. patient population, Health Gorilla is opening up an extremely powerful data source for healthcare software developers and modeling how newer companies can work in tandem with regulators. They’ve solved a lot of the raw data access, cleanliness, integration, and privacy-safe storage issues to build a technical foundation for the next generation of solutions.

Part two: Building analytics and AI models on top of data

With improved infrastructure, companies can build unique analytics and AI models in highly verticalized categories within healthcare. These use cases often require specific data sets, allowing startups in this space to build data moats as a core part of their defensibility. Given this is a highly regulated space involving sensitive patient data, solutions here can distinguish themselves with top-grade privacy and security practices.

1. Analytics and AI applications for providers and hospitals

Providers are one of the major contributors to healthcare data—every time someone in this country completes a doctor’s visit, a medical record is generated. This data set, called clinical data, is one of the most valuable data sets because it captures the essence of what we need in order to practice healthcare—what are the patient’s symptoms, blood test results, medication history, etc. Here are a couple of areas where SignalFire is particularly excited:

  • Personalized patient engagement: Knowing everything we ought to about a patient’s medical history, demographic information, and their consumer preferences, how do we proactively engage with them in a way that encourages them to come in for preventive visits, obtain further education on conditions they may be at higher risk for, provide education on offerings available to them, and ultimately help them achieve better outcomes? This would help providers proactively engage with their patients over the long term, increasing the hospital’s brand loyalty while reducing costs vs. reactively seeing patients as they need care.
  • Clinical intake intelligence: How many times have you sat at a doctor’s office with a clipboard and pen in hand, already five minutes late to your appointment but still needing to fill out a basic questionnaire? There’s been an effort to digitize this experience, but Health Note takes it to the next level by sending patients a digitally powered (i.e., via SMS) dynamic questionnaire (the next question changes based on your responses to the previous question) before their visit, mirroring what a doctor would ask in the first five minutes of the actual visit. The solution not only saves time for a front desk administrator but also a doctor whose clinical note is already halfway auto-generated at the time of the visit.
  • Clinical decision support: Having access to the entire patient medical record plus an AI tool enables more precise diagnoses and real-time intervention at a higher accuracy than what humans can accomplish alone. The overall adoption of these models is relatively limited today and typically needs continuous data from outside the four walls through an integrated continuous management system like all.Health. Where we see great potential is in tools that can assist clinicians, not replace them (e.g., providing a second set of eyes), speeding up diagnosis time by providing an assessment that a clinician can review. For example, Recora Health’s virtual cardiac platform is able to surface to providers who is more likely to have another heart attack after only several virtual visits.
  • Coding automation: Empowered providers get paid faster and bill more accurately using AI models to autogenerate a billing code based on an unstructured doctor’s note. SignalFire led the Series A in CodaMetrix, which has a unique competitive advantage in this space, having spun out of Mass General Brigham—making it a data moat around high-quality training data (read more about our investment here). ‎

Tomorrow’s machine learning startup opportunities

‎2. Analytics and AI applications for payors

Payors’ business models—effectively an insurance business—inherently create incentive alignment with solutions that are using AI and analytics to drive down the cost of care while improving outcomes. Below are several examples of problem statements solved by companies using data and AI:

  • Medication adherence and management: The entire payor ecosystem pays an estimated $300 billion annually for: medications that don’t get consumed; more expensive medications vs. generic equivalents; and medications that patients no longer need. Better data can help create a fuller picture of a patient’s existing conditions and engage with them in a highly personalized way, using behavioral economics principles to nudge them to take the right medicine at the right time. It’s why we invested in Wellth.
  • Population health management: Every payor typically manages hundreds of thousands to millions of lives. Because they’re ultimately responsible for paying the bill, it’s important they understand how healthy their population is and which segments would benefit from proactive management of their health. A data-driven solution like Color would review the entire patient population data across all attributes and help patients navigate to the appropriate care they need.
  • Payment integrity: Annually, $200–300 billion is spent on claims waste, fraud, and abuse. Ninety percent of the time, the reason payors overspend on claims comes down to human error—the person on the provider side has made a mistake and asked for more money than they should collect for a visit. The autonomous coding solution from CodaMetrix not only directly addresses this problem, but—with increased adoption—could establish the common language that would allow payors and providers to transact in an equal and fair manner.

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

3. Analytics and AI applications for pharma

Pharma spends, on average, over $1 billion and 10 years for a successful drug to come to market. Any data-driven and AI solutions that can expedite the drug development timeline or reduce costs are highly attractive to pharma:

  • Drug discovery: AI algorithms can analyze vast amounts of biological data, such as genomics, proteomics, and metabolomics, to identify potential therapeutic targets. By integrating diverse data sources and applying machine learning techniques, AI can predict target-drug interactions and prioritize targets with the highest probability of success. These models can also analyze molecular structures, predict their interactions with target proteins, and propose modifications to enhance drug efficacy, safety, and pharmacokinetics (the branch of pharmacology concerned with the movement of drugs within the body). A strong, valuable dataset like that of can help more quickly identify which approaches to pursue—a key benefit when considering it can take years to get a new drug to the market.
  • Synthetic control arm for clinical trials: One innovative clinical design approach made increasingly feasible with burgeoning digital data and enhanced analytic tools is the use of synthetic control arms. Instead of collecting data from patients recruited for a trial who have been assigned to the control group in a traditional randomized control trial, synthetic control arms model comparators using data. Pharmaceutical companies can save substantial money, shorten trial timelines, and inform development decisions. Synthetic control arms can also bring benefits to patients who may be leery of landing in an arm requiring use of a placebo or ineffective standard-of-care. Synthetic control arms ensure that all trial participants will receive active treatment, obviating an important patient concern which could result in increased patient recruitment and retention.
  • Post-approval targeting: After a new drug has been approved—allowing it to be marketed—there is an opportunity for a pharmaceutical company to harness predictive analytics and machine learning to enable precise physician targeting. The approach might allow a company to identify physicians caring for patients with the highest need for a given therapy, and whose prescribing patterns indicate potential openness to a novel mechanistic approach.

4. Analytics and AI applications for patients

At the end of the day, all these solutions above that work with providers, payors, and pharma will always benefit the patient downstream in one way or another, as the patient is the center of our healthcare ecosystem. However, here are several other ways in which data insights and availability can help us directly:

  • Individual medical record access: Patients with chronic and rare diseases are currently tasked with manually assembling their information to get the best treatment possible. Currently under TEFCA, only certain use cases of data sharing are approved—a provider can pull information if they’re treating a patient, but a patient cannot directly pull information on themselves. We think an individual use case is going to be unlocked in the next year, helping everyone from the overburdened patient with clinical illness to the person who’s simply trying to keep track of their immunization records.
  • Patient payments: Better data can help patients afford their healthcare. Payzen uses large amounts of patient data—spanning medical history, demographics, frequency of visits, and more—to provide patients with a personalized medical bill payment plan that has a 0% interest rate.

‎Building for healthcare? We want to hear from you

If you’re working on a startup in this space, we’d like to chat. Cold emails are welcome at [email protected] to connect with Yuanling Yuan (she goes by YY) from our healthcare investment team. You can also subscribe to our email updates for more on healthcare startup trends and opportunities

Yuanling (YY) Yuan, from SignalFire’s healthcare investment team

‎At SignalFire, we like to say, “Think of us as an extension of your team that scales with you.” Beyond our in-house Beacon AI for help with recruiting, we built our full-time Portfolio Experience team with world-class operators across a variety of functions, including the former chief people officer at Netflix for developing an engineer hiring strategy, the chief marketing officer at Stripe to optimize your sales process, and the former editor-at-large at TechCrunch to help you convert the value you deliver into a persuasive story. Our XIR program, meanwhile, pairs top industry leaders with high-potential companies as they scale and includes healthcare luminaries like Evolent Health ($EVH) founders Frank Williams and Tom Peterson.

We love helping healthcare companies solve their internal problems so they can heal the world. That approach of providing value far beyond our capital is why we have a net promoter score of 85 among founders, with 85% saying we are the most valuable investor on their cap table.

If you’re working on a company in the healthcare data, analytics, and AI space, come talk with us. We’ll share our full research and connections, and hope to earn the chance to hear about your next fundraise. By unlocking the secrets trapped within our medical data, we can build a healthier future for everyone. We can’t wait to see what you’re building.‎

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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.

Tomorrow’s machine learning startup opportunities

Fads fade. We believe machine learning (ML) is a classic piece of building software. Classics have an undeniable air of timelessness. They are hard to ignore and impossible to miss. Classics are forever. We believe ML is forever for software. In fact, at SignalFire, ML is woven into the software we build. We develop our own ML models in-house for our Beacon AI data platform, which we use for sourcing investments and helping portfolio companies with recruiting. That’s given us a deep appreciation for how ML tooling can improve a business, so we’re eager to invest in and support founders building the future of ML infrastructure.

Hot takes on machine learning and LLMs

  1. For the time being, large companies will own model training for large language models (LLMs) and foundation models (FMs). Training requires a combination of proprietary/web-scale datasets and costly infrastructure, making them too big a challenge to be adequately addressed by smaller companies.
  2. Companies can open-source models and methodology without fear of giving away their secret sauce since data is often proprietary and infrastructure is tough to build. The secret sauce is not primarily the recipe, but the rare ingredients and huge kitchen required. Facebook’s pytorch may be open-source, but you can’t build Facebook models on your own.
  3. Foundation models unlock bigger TAMs for enterprises via additional services. Incumbents can charge higher annual contract value for premium AI features, and there is a high interest in companies like Fixie helping enterprises build with LLMs.
  4. Foundation models are industry agnostic, making specialization a growth opportunity. To scale in the commercial medium, one has to fine-tune on industry-specific data, as well as have more control and visibility—for example, the ability to filter for brand safety and content moderation standards.
  5. MLOps will be a critical component in the industry for the foreseeable future. For several years to come MLOps will be important for tasks where LLMs don’t perform reliably. And once large models become more efficient, MLOps will be able to fine-tune the models on a task, get data, manage artifacts, monitor, update a model to address failures, and more.

ML is a core component of the software we build and our portfolio

SignalFire truly understands ML because we’ve spent 10 years building our own ML models for many uses across venture: sourcing companies, completing due diligence, winning competitive deals, and supporting our portfolio with recruiting data and customer lead lists. We do not build a “robot general partner,” but rather utilize data to empower human decision-makers. For example, we use natural language processing (NLP) models to classify companies and surface them to the appropriate investors, classify potential recruiting leads for our portfolio companies to match their hiring needs, and use graph ML algorithms on the open-source community to find compelling technical projects, many of which are ML projects themselves!

SignalFire AI Portfolio

MLOps or MLOops?

Machine learning infrastructure went from Matlab produced by Mathworks in 1984 to an explosion of tooling to operationalize models, referred to in the industry as MLOps. Up to 2021, $3.8B was invested in MLOps. The category has existed for a decade, and while there are a few companies valued at $1B+—such as, Weights & Biases, and DataRobot—there is no IPO yet. Which begs the question: MLOps or MLOops? Here’s how we think the timeline will play out.

ML infrastructure timeline

A timeline of ML infrastructure companies

We believe that MLOps aren’t disappearing anytime soon. LLMs have raised the awareness of AI in our collective mindshare. While they can be useful, LLMs are not (yet) suitable for all tasks, such as time series forecasting, or where end users can’t afford to be wrong in even one percent of cases, such as self-driving cars. While the latest GPT-4 LLM has expanded to accept audio and image inputs—and other foundation models tackle image (DALL-E 2) and audio (AudioLM) generation—the high cost of inference and model fine-tuning are a barrier for high-volume, low-margin business applications.

For several years to come, MLOps will be needed for tasks where LLMs don’t perform or are cost prohibitive. And even once large models become more efficient, there will still be a need for MLOps to fine-tune the models on your task, get data, manage artifacts, monitor data, update the model when you see failure cases, and update the simpler models that manage spam filtering and toxicity detection, among other things. 

In the category of MLOps, we invested in annotation (Explosion), testing (Kolena), and compute (SaturnCloud). While there is no common tooling yet, there is a common workflow to build models. In the image below, you can see an incomplete sketch of companies operating in each of the categories, illustrating in one snapshot the common workflow for building ML models. It captures notable companies for each step, the different teams involved, and some of their publicly announced total funding. 

Industry Map of ML Tooling

This graphic shows how operationalizing an ML model requires a wide variety of different tools and teams with deep expertise. Enterprise companies with the requisite data may still find it hard to staff all these teams with top AI talent—but they have the funds to purchase MLOps.

MLOps startup opportunities

We want to meet founders to solve common pain points for commercial customers who want to add AI skills. If you are building tooling that helps companies use ML to benefit the bottom line, please email [email protected]

Three of the areas we are especially interested to meet more MLOps startups are:

  1. Visibility across the stack: Customers tell us their ML stacks are heterogeneous and hard to review and audit end-to-end. A head of AI at a large retail chain told us, “We have to work with many companies to get to 75% of what Google has internally as their ML platform.” He would pay top dollar for tooling that can provide the enterprise level of standardization and visibility end-to-end, vs. opinionated ML tooling for a point solution.   
  2. Closing the skills gap: As the diagram makes clear, a multitude of different builders have to interact throughout the ML lifecycle. We are interested in startups that bridge the gap in skills between these different organizations as well as tooling that helps the engineers who build models to be more productive. 
  3. Data engineering: We have more data than ever, and we are interested in tooling that makes the collection, transformation, and usage of data more efficient.

Native LLMOps 

Large language models are bringing faster time-to-value to enterprises. SignalFire recently invested in in this space, a cloud-hosted Platform-as-a-Service that enables anyone to build and integrate smart agents that leverage the power of LLMs to solve problems based on natural language. We’re looking to meet more founders building native tooling to use LLMs for commercial use cases, particularly around these three areas:

  1. Inference: Inference (applying an ML model to new data) is expensive, and optimizing it would be beneficial to many companies. Businesses need help with inference and also need help understanding the cost. Algorithmia pioneered MLOps; we would love to speak with teams building the next Algorithmia and startups helping build industrial-strength inference.
  2. Business-specific context: Foundation models are industry agnostic; we are interested in tooling to verticalize a model, providing the vertical-specific context to the model, such as vector databases. 
  3. Commercial control: To scale in the commercial medium, one has to have more control and visibility—for example, a tool to filter for brand safety. ChatGPT is cool but cannot be adopted and used in production if it uses profanity or risks saying something against brand standards that could be screenshotted and shared with everyone.

A VC engineered to help you scale

At SignalFire, we like to say “think of us as an extension of your team that scales with you.” Beyond our in-house Beacon AI, we built our full-time Portfolio Experience team with world-class operators across a variety of functions including the former Chief People Officer at Netflix for developing an engineer hiring strategy, the Chief Marketing Officer at Stripe to optimize your sales process, and the former Editor-At-Large at TechCrunch to help you convert the value you deliver into a persuasive story. Our approach was built around providing value to founders, leading to our net promoter score of 85 among founders, with 85% saying we are the most valuable investor on their cap table.

We are investors, and we don’t have a magic crystal ball; we are open to being proven wrong and we keep an open mind as we’re meeting founders. If you are building in the LLM space, come talk with us—we will share our full research, and we’d like to learn more about what you’re building and earn the option to be at the table when you’re raising. We’re also co-hosting plenty of AI events at our San Francisco office if you have ideas. Cold emails welcomed at [email protected].

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Ro’s patient revolution: $500M for health, not insurance

“When we started, people called us a boner company. They said that to be pejorative, to diminish our efforts. I didn’t find that to be an insult. Addressing erectile dysfunction wasn’t the sole purpose of the company, but we see the moments it unlocks between people. It’s intimacy and love,” says Ro co-founder and CEO Z Reitano. “Take away all the bullsh*t, and what matters is people living a happy, healthy, fulfilled life.”

With today’s $500 million Series D at a $5 billion valuation, the jokes should be replaced with a different kind of name-calling that describes Ro as a unicorn (quintacorn?) charging towards an IPO. The telehealth primary care company now offers diagnosis, prescriptions, medication delivery, and ongoing care. But Reitano, who SignalFire has backed since Ro’s Series A, wasn’t in the mood to celebrate. One thing you’ll notice that’s missing from his quote about what matters in healthcare: Insurance. 

That’s because Ro doesn’t accept it. “It’s clearly not working. Hasn’t been working for 70 years,” Reitano says of health insurance. “The idea of giving insurers more power and control is preposterous. It’s not that capitalism doesn’t work in healthcare. It’s that we’re incentivizing the wrong stakeholders. Insurers aren’t incentivized to reduce the cost because employers pay.” And sadly, we all pay in the form of lower-quality care at higher prices.

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$500M for patient-centric healthcare

That’s why Ro is building a new patient-centric healthcare system where it’s the people in need who control where the money goes. “We should have to fight for the right to take care of patients,” Reitano insists.

Here’s a quick history lesson about how things got so broken. America froze wages during World War 2 to prevent inflation, so employers started offering better health insurance to attract talent. Then the IRS made employer-provided health insurance tax-exempt, making it the cheapest way to get it, so most Americans did. Then the government fixed health insurance company margins so they had to spend 80 to 85% of the money from premiums on providing care. That sounded good, but it means that one of the only ways for health insurance companies to grow cumulative profits is to…raise costs. 

Rising insurance premiums, according to KFF

The result is runaway insurance premiums and worker contributions. Premiums are up 54% just from 2010 to 2020. Medical expenses cause 66.5% of individual bankruptcies in the US. “An MRI costs the same as in 1984 when it came out. That’s bananas! That thing should be running 24 hours a day. It costs $27 in Norway. There should be a Chipotle of MRIs!” Reitano says. We could clearly be doing better. He notes that Singapore pays for 50% of citizens’ healthcare but it only costs 4% of GDP, not 18% like in the US. Meanwhile, 66.5% of individual bankruptcies in the US are caused by medical expenses. 

Reitano’s own family was once in hundreds of thousands of dollars of medical debt, in part due to treatment for his heart condition, which had erectile dysfunction as a side effect of his medication. “I was actually one of the luckier ones,” he tells me, simply because none of his immediate family members had died. They lived through heart attacks, neurological disorders, and multiple bouts with cancer. Z says the only reason they survived was that his Dad was a doctor and could help them navigate the complexities of getting great care despite the dysfunctional insurance system.  What gave me agency was having a world-leading expert by my side. That shouldn’t be a requirement for what we see as a fundamental right.”

Ro co-founder Z Reitano and his dad Dr. Michael Reitano

Those experiences led Reitano to build Ro to destigmatize conditions like his and help other families beat the odds. That’s why Ro doesn’t accept insurance. “It exacerbates a system that reduces and limits the agency of patients over time, and I can’t abide by that. I think the insurance companies will drive themselves into oblivion because they’re endlessly greedy, increasing premiums and deductibles. When you remove the administrative burn of insurance and all the stakeholders who need to make money in the process, you can dramatically reduce the cost to patients,” Reitano explains. “People talk about how we’re limited by not accepting insurance, but we are unleashed and unlocked to offer 10X better service!”

That doesn’t make Ro expensive. It sells generic versions of the 500 most common medications for just $5 per month each from its own pharmacy. A virtual doctor’s visit is $15, which is cheaper than most co-pays. For $20 to $40 per month, it offers diagnosis, prescription, and delivery of brand name treatments for men’s and women’s health, hair loss, smoking cessation, dermatology, weight loss, and more. Plus, Ro’s Health Guide site is challenging WebMD with self-serve medical info that’s actually approved by doctors. “We start by building what patients need and then use technology to reduce the friction. It’s the only way we do more with less,” Reitano explains.

Now Ro has the funding to bring its care offline and into the home in-person. The $500 million Series D co-led by our friends at General Catalyst, First Mark, and TQ Ventures is joined by Altimeter, Dragoneer, Baupost, Glen Tullman of Livongo, Box Group, Torch, and us at SignalFire. It’s been a pleasure to equip Reitano and his team with our Beacon recruiting technology, competitive intelligence, sales lead intros, and in-house experts. 

“Simply put, they’re not like other VCs,” Reitano wrote to another startup considering SignalFire. “They’re engineers and operators — they’ve built it before. The other thing I love, and this is more personal, is that they are a new VC fund, which means they can’t sit behind a brand name…they have to wake up every single day and work their tail off for their port cos. I first met [SignalFire’s seed fund MD] Wayne Hu on a holiday when no other VC would carve out time. [Venture partner] Tony Huie has been on the phone with me at 2 am helping me work through a strategy question. Their data science team helped our Head of Data solve a complex attribution problem . . . Every VC says they are value-add, but most don’t have to prove it. I’m not kidding when I say SignalFire will always be there.”

COVID vaccines delivered

Ro’s years of building a national telemedicine practice, pharmacy network, in-home care platform culminated this month in one of its most important launches to date: Ro’s COVID-19 Vaccine Drive. Ro is now Ubering healthcare professionals to the homes of the elderly and homebound to administer vaccines to those who’d have trouble traveling to a traditional vaccination site.

The launch comes in response to how difficult it’s been to book vaccine appointments through confusing government websites with unpredictable availability — especially for the less digitally literate. “Everyone felt the system was complicated. It felt rigged. They felt powerless. We’re not sure why our government can’t operate at the same efficiency as Amazon,” Reitano explains. Luckily, Ro acquired in-home healthcare API maker WorkPath in 2020 and had already facilitated 100,000 housecalls. “Because of our unique capability, I think we have a responsibility to help.”

So in December, Ro co-founder Saman Rahmanian got to work partnering with the NY Department Of Health and leading an “all on-hands on deck, 18 hour days for 4 weeks” sprint, Reitano explains. “People don’t talk enough about choosing certain co-founders — why you need shared vision, shared values, shared work ethic. One attribute I’d add is that you’re constantly amazed by what they can will into existence. Saman willed this into existence.”

Here’s how it works. An eligible patient or their guardian can go to and sign up for an appointment. Rather than scrambling for open slots, Ro just puts you in the queue for the next available date and time window. Just like calling an Uber, you’ll get an ETA and visualization of your vaccinated healthcare professional heading to your home. They walk you through the standard protocol, collect any necessary info, administer your dose, and wait 15 minutes to check for adverse reactions. Uber has donated 10,000 rides to the cause, and any leftover doses will go to local fire departments and frontline workers.

Reitano believes this convenience and quality of care would become standard if more of the healthcare system empowered patients to purchase their care directly. If employers or the government just gave patients the cash for routine service and saved insurance for catastrophes, “within 60 days, you’d see cucumber water in the waiting rooms. Hospitals would show prices and readmission rates. They’d compare quality of care versus the hospital down the street. In a world where you had $2 to $3 trillion every year at the discretion of patients, [care would] decrease in price, increase in quality, and increase in satisfaction.”

SignalFire’s “Convenient Health” thesis

We see patient-centric healthcare and finance as the fifth pillar of SignalFire’s “convenient health” investment thesis. We’re seeking startups offering:

  1. Democratized, destigmatized, more affordable access to care via telehealth
  2. Continuous, automatic, personalized data collection via wearables and smartphones
  3. Consumerization of the user experience and a reduction of reliance on pure will power
  4. Human doctors and experts in the loop, augmented with AI and automation to optimize quality of care
  5. Patient-centric control of billing to incentivize providers to improve their services

This thesis is why we’ve been long-time believers in Ro. It’s also led SignalFire to invest in telehealth startups like Form Health for weight loss, Bicycle Health for opioid dependency, Apostrophe for skincare, and OrthoFX for teeth alignment. We’ve also backed startups that make staying fit at home less work, like Down Dog for yoga and Tempo for weight lifting. We’re also supporting convenience in genetic testing with Color, surgeon training with Osso VR, and at-home clinical trials with Hawthorne Effect. If you’re building something to advance this vision for patient-centric healthcare, SignalFire would love to hear from you.

“It’s paternalistic saying patients can’t make the right decisions. Similar to the Winston Churchill quote, ‘Democracy is the worst form of government, except for all the others’, a patient might be the worst person to make their health care decisions except for everyone else.”

“We’ve been beat up and punched in the face for decades, and the only thing that the healthcare industry listens to is who controls the money,” Reitano concludes. “We’re eating the edges of primary care. Others will eat the edges of insurance. I want to devote my life to this patient revolution.”

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