Transcarent CEO Glen Tullman

Digital Health

A Delicious Primer on Food-as-Medicine

Rock Health dished up a fantastic primer on the food-as-medicine market, serving as a helpful cheatsheet of which trends will stay fresh the longest, and which ones are already stale.

Fertile grounds for new FaM models have been created by shifting consumer behaviors around diet and wellness, with 20% of US adults saying the pandemic prompted healthier choices. [Graphic: Three eras of food-as-medicine]

• Payors are also grappling with the rising costs of treating the nearly 50% of Americans with diet-related illness, and FaM offers an avenue to reign these in without pricey medications like GLP-1s.

• Policy changes have also planted the seeds for growth, with new initiatives helping scale FaM programs like medically tailored meals. One wild stat is that FaM partnership volume in the last 18 months has surpassed that of the prior seven years combined.

As the FaM market begins to sprout, startups are facing an increasingly complicated menu of funding sources and potential partners

Value chain segment #1: Food access

• Food and supply chain – Providers of healthy groceries, prepared meals, or digital marketplaces for third-party products, including delivery partners and food “farmacies” that fill clinician’s produce prescriptions. Examples: Mom’s Meals, Uber Health

• Service navigation – Services that refer consumers to food access programs and support enrollment, often through community orgs like FQHCs. Examples: Findhelp, Unite Us

Value chain segment #2: Nutrition care

• Medical nutrition counseling – Virtual or in-person nutrition counseling with dietitians to provide tailored nutrition plans and resources. Examples: Foodsmart, Season Health

• Behavior change support – Tools for tracking diet and outcomes, educational content, and recommendations. Includes non-digital services like nutrition and cooking classes. Examples: Heali, SeekingSimple

Value chain segment #3: Program enablers

• Fintech – Targeted tools and vouchers (category-restricted to healthy products) that enable consumers to use food benefits provided by their health plan. Examples: Solutran, Soda Health

• Data and food benefits management – Data on consumer behavior or food products to help payors optimize benefit design, measure program impact, and inform engagement strategies. Examples: DietID, NourishedRx

The Takeaway

The food-as-medicine market is turning into a “cornucopia of innovation,” but founders looking to take advantage of new funding mechanisms now have to separate the wheat from the chaff when it comes to business models and potential partners.

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The Wire

• Epic Up, Competition Down: KLAS released its headlining-grabbing report on the US acute care EHR market, which showed that Epic was the only vendor to gain share in 2023. Epic added 153 hospitals last year without losing a single one, which naturally came at the expense of its competition. All other vendors showed a net loss of hospitals and beds (Oracle Health had a particularly tough go of it after losing 71 hospitals representing 15k beds) and Epic now holds a massive 39% share of US hospitals – and for the first time over half of all US beds (52%).

• AI Validation Comes to Epic: Instead of taking its foot off the gas after gobbling up the EHR market, Epic launched its open source AI validation software suite that enables healthcare organizations to evaluate their models at the local level and monitor them over time. The new software automates data collection and mapping to provide nearly instant analysis on model performance. This is Epic’s first-ever open-source tool, a decision that was made to allow organizations to bring in AI best practices alongside the validation capabilities as they get developed.

• Transcarent Debuts Wayfinding: Transcarent rolled out a new AI chatbot to help employees navigate their healthcare issues, stirring up a lot of excitement with features that streamline clinical guidance, benefits access, and even care delivery. The WayFinding platform helps patients answer questions about benefits and costs, recommends an appropriate course of clinical action, and then connects them to the right providers or digital solutions to address their needs. Not a bad way to follow-up their nine-figure Series D earlier this month.

• GLP-1s Don’t Work For Everyone: Gallup survey data shows that injectable weight-loss drugs have been effective for about two-thirds of Americans who have taken them, but only 48% of seniors. Although research has shown that GLP-1s can help seniors, slower metabolisms and an increased likelihood of side effects seems to make them less effective. On a related note, a genetic test developed by Mayo Clinic can apparently predict who’s likely to lose weight from GLP-1s with 95% accuracy.

• Two-Year Telehealth Extension: A second House subcommittee voted to advance the proposal to extend some Medicare telehealth flexibilities for another two-years. The House Energy & Commerce health panel previously weighed legislation that would permanently extend those flexibilities, but recently amended it to be a two-year extension. Although nothing’s for certain until the legislation passes, the final product seems like it’s starting to take shape.

• Notable + Beacon: Notable’s recent momentum is snowballing with the announcement of a new partnership with Beacon Health System, arriving less than a few days after a similar move at Sturdy Health. Beacon will deploy Notable to streamline the process of self-scheduling procedures and tests by using AI to scan its EHR for any pending orders, automatically reaching out to those patients via personalized messages, then enabling easy scheduling for any needed appointments.

• International AI Safety Report: An International AI Safety Report written by specialists from 30 countries found that many of AI’s riskiest use cases are in healthcare. The report calls out a few especially high-risk threats to patients, including biased diagnostic tools, the security of health information in training data, and a high market concentration that could be a vulnerability if systems go down. The findings supposedly played a “substantial role” in the decision to launch a new network of AI safety institutes that just took place at a global summit on the topic.

• Tempus AI Files for IPO: Tempus AI filed for its long-awaited IPO, and will be looking to raise $100M according to the SEC documents. Since 2015, Tempus has reportedly built the world’s largest library of clinical and molecular data, as well as an operating system that allows providers and life sciences orgs to surface insights from the data. The company amassed $1.3B in funding before heading to the public markets, and will now be looking to grow past its roots in oncology to bring AI-enabled diagnostics and personalized medicine to areas like cardiology and radiology.

• Digital Self-Management for Heart Patients: The largest-ever study on the use of digital tools to control risk factors for cardiovascular disease was just published in the Journal of the American Heart Association, linking Hello Heart’s app-based self-management program to significant outcome improvements. After examining over 100k Hello Heart users with hypertension between 2018 and 2022, those with baseline systolic BP above 140 mm Hg reduced SBP by 19 mm Hg at two years, while those with baseline LDL cholesterol above 160 mg/dL reduced it by 67 mg/dL at 13 months. 

• Lumeca Messages Launch: Canadian up-and-comer Lumeca Health launched a new encrypted messaging solution tailored for providers, allowing them to securely share files, coordinate care, and exchange advice. Lumeca Messages includes a lengthy list of features designed to make HIPAA-compliant messaging as frictionless as possible, such as EHR integration for content shared on the platform and an integrated provider directory.

• Disparities in AI Analysis: There’s growing evidence that many healthcare disparities are carrying over into the AI era,including a new paper in Radiology describing how an AI algorithm for breast screening performed differently based on patient race. Researchers used iCAD’s ProFound algorithm to analyze 4.9k true-negative mammograms, finding the AI was 50% more likely to generate false-positives for Black patients than White patients (OR = 1.5) and 30% less likely in Asian patients. False positives were also nearly twice as common in older patients ages 71-80.

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