Virtual-first care, or V1C, could increase the quality of healthcare and make it more patient-centric by letting patients combine in-person visits with virtual options such as video for seeing their care providers.
That’s the idea behind virtual-first care (V1C), a new care model centered on convenient, customized, high-quality care that integrates a full suite of services tailored directly to patients’ clinical needs and preferences. This package includes asynchronous communication such as texting; video and other live virtual modes; and in-person options.
V1C goes beyond what you might think of as standard “telehealth” by using evidence-based protocols and tools that include traditional and digital therapeutics and testing, personalized care plans, dynamic patient monitoring, and team-based approaches to care. This could include spit kits mailed for laboratory tests and complementing clinical care with health coaching. V1C also replaces some in-person exams with ongoing monitoring, using sensors for more ‘whole person’ care.
Amidst all this momentum, we have the opportunity to rethink the goals of healthcare innovation, but that means bringing together key stakeholders to demonstrate that sustainable V1C can redefine healthcare.
Established V1C healthcare providers such as Omada, Headspace, and Heartbeat Health, as well as emerging market entrants like Oshi, Visana, and Wellinks, work with a variety of patients who have complicated long-term conditions such as diabetes, heart failure, gastrointestinal illness, endometriosis, and COPD. V1C is comprehensive in ways that are lacking in digital health and its other predecessors: it has the potential to integrate multiple data streams, incorporate more frequent touches and check-ins over time, and manage a much wider range of chronic health conditions, improving lives and reducing disease burden now and in the future.
Recognizing the pandemic-driven interest in virtual care, significant energy and resources are already flowing fast toward V1C. Some of the world’s largest innovators jumped into V1C early on: Verily, Alphabet’s Life Sciences Company, launched Onduo in 2016 to disrupt the diabetes healthcare market, and is now well positioned to scale its solutions. Major insurers like Aetna and United now offer virtual-first plans to members, responding as organizations expand virtual options for employees. Amidst all this momentum, we have the opportunity to rethink the goals of healthcare innovation, but that means bringing together key stakeholders to demonstrate that sustainable V1C can redefine healthcare.
That was the immediate impetus for IMPACT, a consortium of V1C companies, investors, payers and patients founded last year to ensure access to high-quality, evidence-based V1C. Developed by our team at the Digital Medicine Society (DiMe) in collaboration with the American Telemedicine Association (ATA), IMPACT has begun to explore key issues that include giving patients more integrated experiences when accessing both virtual and brick-and-mortar care.
Digital Medicine Society
V1C is not, nor should it be, virtual-only care. In this new era of hybrid healthcare, success will be defined by how well providers help patients navigate the transitions. How do we smoothly hand a patient off from an onsite primary care physician to, say, a virtual cardiologist? How do we get information from a brick-and-mortar to a digital portal? How do you manage dataflow while still staying HIPAA compliant? There are many complex regulatory implications for these new models, as well as an evolving landscape in terms of privacy, security and interoperability. It will be no small task for groups like IMPACT to determine the best path forward.
None of these factors matter unless the industry can recruit and retain clinicians. Our field is facing an unprecedented workforce crisis. Traditional healthcare is making clinicians miserable, and COVID has only accelerated the trend of overworked, disenchanted healthcare workers leaving in droves. Clinicians want more interactions with patients, and fewer with computer screens – call it “More face time, less FaceTime.” No new model will succeed unless the industry can more efficiently deploy its talent – arguably its most scarce and precious resource. V1C can help with alleviating the increasing burden and frustration borne by individual physicians in today’s status quo.
In healthcare, new technological approaches inevitably provoke no shortage of skepticism. Past lessons from Silicon Valley-driven fixes have led to understandable cynicism. But V1C is a different breed of animal. By building healthcare around the patient, not the clinic, V1C can make healthcare work better for patients, payers and providers. We’re at a fork in the road: we can revert back to a broken sick-care system, or dig in and do the hard work of figuring out how this future-forward healthcare system gets financed, organized and executed. As a field, we must find the courage and summon the energy to embrace this moment, and make it a moment of change.
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
