Contents

1. How Kinetic created and then won a category — the worked example before the framework.

2. The framework for effectively owning risk — five conditions, three stages, five failure modes, four diagnostic instruments.

3. Value-based care economics are often bad. Yours do not have to be — owned-loss economics vs. savings-share economics.

4. Three failures, three modes triggered — Bright, Cano, Babylon.

5. CareBridge — a narrow-population exit consistent with the framework.

Part 2 ships July 15: maternity through four different concepts of risk and venture design (Fetal Pillow, Maven, Pomelo, Ouma + MedArrive), a nine-question diligence memo for evaluating risk-bearing projects, and the venture-scale path through the framework.

TLDR: Take responsibility only for losses you can define, measure, materially influence, price with credible data, and survive when the model is wrong.

1. How Kinetic created and then won a category

My favorite startup example of a both wearables and owning risk is almost never discussed in the healthtech startup ecosystem.

Kinetic Insurance is an NYC company living the VC-funded dream. It created a new multi-billion dollar category that it now dominates. It did so by providing ground truth capable of dramatically beating the relevant actuarial tables and an effector arm for delivery both human and economic impact at scale. Most healthtech companies that announce a path to risk-bearing never even come close to reaching those destinations.

The Kinetic Reflex sensor detects high-risk lifting and bending postures among warehouse workers. When it sees one, it provides haptic feedback to the worker in real time. The accompanying software shows managers where risk is clustering by department, job type, and time of day, creating something employers rarely have: ground truth about how work is being performed. The employer value proposition is therefore twofold: fewer injuries and lost workdays, plus a usable map of the behaviors likely to produce the next injury. The product is not merely a warning buzzer; it is a sensor-to-coaching-to-management system aimed at a specific workers’ compensation loss.

Kinetic launched in 2014 as a workplace-safety wearable. By November 2021 it had launched Kinetic Insurance, a workers’ compensation program sold and serviced by Kinetic, with Nationwide’s excess-and-surplus and specialty organization underwriting the policies. Unsurprisingly, Nationwide Ventures has been involved with Kinetic since its 2020 series A.

Most healthtech companies that claim a path to “taking risk” never leave the much lower-margin strata of vendor economics. Some reach outcomes-priced contracts where a performance fee can be lost while the payer still owns the medical claim. Some accept capitation and bear delegated medical-cost downside. A much smaller group administers an insurance product. “Taking risk” collapses these into one sentence; this field guide separates them.

What Kinetic did is still the rare thing: it satisfied the operating and evidentiary prerequisites in sequence, in public, before moving into a delegated insurance role. The sequence is applicable and deeply instructive even when the strategic endpoint differs.

The loss event Kinetic addressed (strain and sprain claims in industrial work) is unusually well-suited to risk-bearing: a compensable injury is observable, maps to the policy and employer, settles inside the policy period, has decades of workers’-comp actuarial baseline, and posture, lift technique, repetition, and workstation design are operationally addressable.

In Section 2, we’ll work through these properties as the five conditions for a successful risk program, how Kinetic’s venture design passes each, and what new lessons new teams can apply to their own work.

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Hi, I’m Vadim.

Healthtech venture is hard, a fact I’ve learned firsthand as an operator, accelerator director, and investor. I’ve watched too many good teams repeat avoidable mistakes others have already paid for. I write Healing Healthtech to distill research and the experiences of top-tier operators into actionable tactics and frameworks, so we can all aim to make only new mistakes.

Subscribe for a twice-monthly dispatch of free and paid field guides meant to help you build more deliberately toward something better in human health. And if it’s useful, please forward it to other healthtech builders in your network.

Drafted with support from Claude as a research assistant, with GPT and Gemini as a red-team.

Angel rounds are achievable. Anywhere in the world.

Every bubble in that chart is a real raise from a verified founder account [22]. Read it as a promise and a warning at the same time: angel rounds close at every size and on both sprint and marathon clocks, and none of them closed without sustained, disciplined effort against a wide funnel.

Angel investors aren’t magical. They’re mid-to-late-career professionals who remember what it was like to be a fresh builder, and who are now interested in and able to bet on younger founders in exchange for some of those founders’ upside. Most angels underwrite 1-3% of the deals they see, when they feel they have met a pitch that embodies their beliefs about founders, problem, and solution spaces [17]. Roughly 500,000 angels will fund some 60,000 companies around the world this year [38]. The goal of this guide is to help you find yours.

So when an angel raise fails, it fails for one of three reasons:

1. The founder never ran a disciplined process.

2. Something is fundamentally wrong with the plan or the proposed business.

3. The founder didn’t adapt the plan, the pitch, and the angel targeting as the process taught them things.

The first and third are fixable with method, and this guide is the method.

Over 15 years, my venture partner and I led the Data Future Lab at NYU, where we supported 300+ teams to $2.7B in capital raised and 44 exits; this guide exists to put that playbook in the hands of the thousands of founders I’ll never meet. It is keyed to my core domain of digital health, but I have personally coached teams running these exact strategies on thin films for solar panels, fintech SaaS platforms, and math tutoring programs for charter schools.

Part I — The map

1. The stages — the capital ladder, what each round buys, and the markup principle that runs through all of them.

2. The strategic fork: growth versus viability — know the plan at the start of the round, lock the choice with no less than 50% runway remaining, and run the next-round-investor diagnostic before you commit to growth.

3. Raising less money is not easier — the small-round trap, quantified, with the healthtech amplifier.

4. What makes a fundable healthtech company — the scale math, the operational machine, the acquirer pool, and the credibility tells.

5. Accelerator or nah? — when a program is worth the equity and when it isn’t; the comet distribution, the hedging-accelerator model, two diligence checks, and my advice from getting the most out of a program.

Part II — The process (phases run in parallel, not in sequence; a given investor sits at a different phase on any given day)

• Phase 0: Set the table — vesting, 83(b)s, IP assignments, cofounder splits, option pool, the data room.

• Phase 1: Build your list — finding 80-120 qualified names when your network is thin; the warm-intro priority stack.

• Phase 2: Before you’re raising — meeting investors months ahead; lines, not dots.

• Phase 3: Decide the round — price, instrument, size, founder salary, the procurement-cycle runway.

• Phase 4: Choose your outreach pattern — the sprint versus the marathon, the forwardable, the investor-side comparator funnels.

• Phase 5: Work the conversation — format for your first call with a new prospect, cadence, reverse diligence, reading and responding to the slow no.

• Phase 6: Diagnose and calibrate the raise — the two-stage instrument, reading the clock and the funnel, asking the angels who passed, recalibrating before you scale.

• Phase 7: Close and collect — stack checks behind your first investor, collect every yes the day it lands, the staged close, the SAFE stack, strategic checks.

Part III — The data — aggregate data sources organized by what you can dig into, with link-outs.

References

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This document is a free sample. It’s part of larger three part series on

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TLDR: Do your best to get into a top decile accelerator. They’re genuinely helpful. Otherwise, you’re better off spending those 3-4 months focused on fundraising, product, and biz dev.

For an under-networked clinical founder, an accelerator is often pitched as the alternative path through friends-and-family and angel: a program that promises to manufacture the qualified investor funnel, the health-system intro graph, and sometimes a first pilot in one move.

The largest global study (8,580 startups, 408 accelerators) finds that participation roughly doubles the probability of raising venture capital, from a ~3% baseline to ~6.4%, and adds about $1.8M to first-year funding [1].

But subsequent matched-pairs research (~900 graduates vs ~900 non-participants) shows the first-year bump dissolves: over the three years after graduation, graduates raised about $9M less in total funding, were more often acquired in talent-driven acqui-hires than technology acquisitions, and shut down earlier and more often [2].

A long tail, not a bell curve.

Underneath both findings sits a comet distribution of accelerator outcomes. Top-tier programs delivered large gains, second-tier delivered beneficial learning, and bottom-tier programs actively inhibited venture development along some dimensions [3]. The accelerator average (+2x funding likelihood) is the wrong unit to plan against; the specific program is.

The vantage for what follows: I ran the Catalyst and Innovention Accelerators at NYU Tandon Future Labs for four years, and Steve Kuyan, now my venture partner at Mighty Middle Ventures, ran them for eleven before me. We supported 300+ teams who went on to raise $2.7B and produce 44 exits (a 15% outcome rate). I have watched multiple Catalyst and Innovention alumni go on through Techstars, Y Combinator, a16z Speedrun, Forum, ERA, and many others, which means I have seen the same companies before and after the brand-name programs and can compare what those programs added.

Empathy demands that we reject mediocre accelerators

I learned this myself. As a young founder I was considering a nationally known healthtech accelerator network, since thankfully shuttered. Before applying, I reached out to alums. Nine in ten told me their time there had been a complete waste, and I did not apply. The information was already in the community. It was sitting in the peer network rather than in the marketing copy. The community knew. The community had no way to say so to the next founder.

Personal runway is finite. Spousal tolerance is finite. The patience of friends-and-family backers is finite. A mediocre accelerator that absorbs three to four months of that scarce resource does damage that does not show up in the program’s own metrics: the founder still finished demo day, still got the certificate, still appears in the alumni count. What is missing from the count is the venture that might have raised, sold, or pivoted in those same months.

Brad Feld’s Startup Communities lays the cornerstone for how regional ecosystems work. Entrepreneurs lead. “Feeders” (universities, government, investors, service providers) support. Feeder-led programs that misallocate founder time and credibility do real damage to the community as a whole. Feld’s “give first” ethos functions as a normative filter; actors who extract more than they contribute should not become central figures. Even if Feld does not say it in those words, bad-actor accelerators need to be identified and named. A community that protects its mediocre programs out of misplaced civility is failing its founders.

My proposed framing is as follows.

If a startup accelerator fails to get at least 50% of its teams attributable follow-on funding or a substantive attributable new contract within 3 months of graduation, that accelerator is likely doing more harm than good by absorbing precious and finite founder runway.

There are 500,000 active angels around the world. If a program couldn’t shape and nurture 50% of it’s teams into a form factor that a handful deem fundable, that is an outright failure.

The kinder posture is the harder one. Empathy for founders requires the community to tell them when a program is mediocre, even when the program is run by well intentioned operators. The alternative, letting those programs absorb founder runway in the name of being supportive, is a failure of empathy dressed as collegiality.

The Hedging Accelerator

One model to screen out, and it’s a newer one gaining ground: the hedging accelerator. It admits a large cohort but only invests in, and takes meaningful equity from, a fraction of it, never more than 30% of the teams. The rest get the standardized programming and the demo day.

The bet is rational for the accelerator: three to four months of close-quarters work gives them a strong read on both your team and your customers, so they back the few they come to believe in. The risk lands on you, and it’s a signaling one. You’re far more likely to sit in the cohort’s “not funded” slice than its “funded” one, and you’ll spend real effort in your next round explaining to investors why the accelerator that watched you for a quarter chose not to write the check.

MD matters more than the brand.

The brand on the accelerator is a wrapper. The implementation is the work of the managing director, and that’s where the variance lives. The cleanest example in the literature: one accelerator ran two cohorts a year apart — one produced strongly positive effects on fundraising and employment, the other reduced participants’ web traffic during the program and showed no detectable effect on fundraising. Same brand, opposite outcomes, twelve months apart. The accelerator effect lives in program design and operator quality, not in the marquee.

What the MD controls and the brand book doesn’t: mentor recruiting, cohort selection, which VCs come to demo day, whether your time gets protected or strip-mined. I’ve mentored at six Techstars affiliates and the founder experience varies enormously across them. The diligence question isn’t “is this a well-regarded brand?” but “who’s the MD, how long have they been here, and can I talk to alums from THEIR last two cohorts?” A famous logo with an MD who arrived nine months ago is a brand-new program.

Geography Matters, but not How You’re Assuming

Location matters far less than founders assume, and the MD point is most of the reason why. For many years I felt the two best healthtech Techstars programs were LA, led by Matt Kozlov, and Fort Worth, where Trey Bowles was managing director and Jordan Warnement ran the program. I volunteered my time with the Fort Worth team and was given their all-star mentor award, so I am not neutral, but the quality of that program had nothing to do with being in Fort Worth rather than a coastal hub.

Fort Worth Techstars has since shuttered. Trey now runs 1845 Ventures, a dedicated healthtech fund and studio in Fort Worth. I would assume without hesitation that the thoughtfulness, drive, and wisdom he brought to Techstars teams is still there. The brand on the door changed; the operator did not.

The geography that does matter is your customers’, and it cuts the opposite way from intuition. Seth Weill, formerly an investor on the venture team at Mount Sinai Health System in New York City, gave my portfolio companies the consistent advice: raise in the major metros, but find your payer and provider customers elsewhere. VPs of utilization at payers and CMIOs at hospitals in major metros are desensitized to local teams pitching them the latest widget. A hospital in Louisville or Tulsa is no less relevant as an anchor customer, and is often a more willing one. Pick a program for its MD and network, not its zip code, and run your commercial motion where the buyers still pick up the phone.

Incidentally, Seth has taken a version of his own advice. As of June 2026, he has left Mount Sinai Ventures to build healthtech ventures at General Catalyst in Ohio. GC has effectively infinite money and dedicated teams in NYC and SF. Despite this, they chose to buy a health system as their innovation petri dish in the Midwest.

But they promised me contracts?

Be especially skeptical of the promised buyer access, because the health-system programs are where it underdelivers most.

Here are two anonymized examples, both from brand-name hospital accelerators and where I have direct knowledge of how things played out.

At one major east coast academic medical center, the accelerator’s venture arm was completely separate from the clinical service lines, and the service-line leaders got annoyed enough at cohort teams running customer discovery against their staff that they capped how senior a person any startup was allowed to talk to. Sell-through to the host system was poor as a result: the program could get you in the building, but not past the org chart.

The second case is subtler and more expensive. A device team went through a brand-name accelerator at a major coastal hospital system with a tool focused on Emergency Department usage. The ED’s medical director loved it, and the CFO loved it enough that the hospital invested in the company off its own balance sheet. Then purchasing ran the numbers: this category of device came as part of a vendor bundle, and dropping the incumbent’s clearly inferior ones would have repriced everything else in that contract. Even with top-to-bottom leadership support, the system bought two cartons, ever. A hospital can love your product, invest in your company, and still be unable to buy it, and the accelerator’s promised buyer access does not survive contact with procurement.

Before you join any program, run two checks.

• Talk to founders from the most recent cohort, not the alumni the program puts in front of you. Last cycle’s teams know what it delivered before a good exit rewrote the story, so ask them what happened when their raise went quiet and who opened a door.

• Treat the managing director and the program manager as two advisors you’d have to pay in equity, and ask whether you would.

A program is its MD and its program manager. Ninety percent of the value you’ll derive from an accelerator is driven by those two people, and this holds even at the YC level: the teams I’ve worked with have had vastly different experiences depending on which YC partner they drew. If those two aren’t worth advisor shares on their own merits, the brand and the demo day won’t change that.

A template for reaching a recent-cohort founder cold:

“Hi [Name],

I’m applying to [program]’s next cohort. Would it be possible for us to connect? I’d love to hear about outcomes and experiences from someone who just went through the program.

Two questions: 1. what did the program actually do for your raise when it got hard, and 2. would you do it again knowing what the equity and time cost? Fifteen minutes by phone whenever’s easy would be hugely helpful. I’m available [option 1], [option 2], or [my calendly].

Please feel free to share your calendar and and I can grab time if that would be more convenient.

Thanks!

-New Founder”

Be honest, too, about why you’re joining. If the answer is to learn how to sell B2B enterprise into healthcare, an accelerator is an expensive way to do it. Three resources will teach you more in a week of evenings:

• Julie Yoo’s go-to-market playbooks for digital health startups at a16z Bio + Health, the cleanest single resource on commercial design for digital health [4].

• Koby Conrad’s “6 Growth Channels That Actually Work”, by Rupa Health’s then-head of growth, who scaled the platform from $5M to $75M in annualized lab sales before founding his own company [5].

• A 2023 workshop I ran on selling to health plans, with Jacob Victory (former Centene executive) and Bobby Murphy (a sales lead with exclusive Medicaid-plan experience) on the panel, and a fireside chat moderated by Rohan Siddhanti, then GTM lead at the EHR Healthie and now in a healthtech GTM role at Anthropic [6].

Or wait for the B2B healthtech GTM field guide I’m publishing in late July. The equity an accelerator costs is worth it for the MD and the network behind the program. It’s rarely worth it as a place to learn enterprise sales.

Even the best accelerators don’t replace a fundraise

The dollars that you get from most programs will not replace the need to run a dedicated fundraise after demo day.

Raising from angels and pre-seed funds is entirely doable even without an accelerator on your cap table. This document is part of a series of field guides that I’ve written on the topic through the lens of having coached MANY successful founders.

(Free Subscribers) How to build a pitch deck and startup narrative
(Paid Subscribers) How to find investors and run an angel or pre-seed process

If you join, three pieces of advice

I ran these programs for years, so let me close with the advice I gave every team I took on. All three come down to the same thing: a program can only help with what you put in front of it.

Be the squeaky wheel. When you ask, a good director and their team will bend over backward to get you the intro, the mentor, the contact, whatever you need. We can’t read minds. If you don’t tell us what you’re stuck on, we can’t unstick it.

Tell us when things are going wrong. Teams break up, commercial champions change roles, roadmaps drift. We can’t help with what you don’t share, and the problems you hide are usually the ones that sink the round.

Do the work. Showing up for the PMF seminars and demo day and nothing else is a waste of everyone’s time. The best outcomes among my teams came, every time, from the founders who actually showed up and did the work between the sessions.

References

1. Valentina Assenova & Raphael Amit, “Poised for Growth: Exploring the Relationship Between Accelerator Program Design and Startup Performance,” Strategic Management Journal 45(6), 1029-1060, 2024. 8,580 startups across 408 accelerators in 176 countries (GALI data, 2013-2019). Accelerator participation roughly doubles the probability of raising venture capital (from ~3% baseline to ~6.4%) and adds about $1.8M to first-year funding. Peer-reviewed. Comparator is other applicants, not a clean experiment; the positive headline average is built on a highly unequal distribution dominated by a small cluster of strong programs. https://doi.org/10.1002/smj.3581

2. Sandy Yu, “How Do Accelerators Impact the Performance of High-Technology Ventures?” Management Science 66(2), 530-552, 2020. Matched-pairs design comparing ~900 accelerator graduates against ~900 non-participants. Three years after graduation, graduates raised about $9M less in total funding, were more often acquired in talent-driven acqui-hires than technology acquisitions, and shut down earlier and more often. Yu’s framing: accelerators “resolve uncertainty around company quality sooner, allowing founders to make funding and exit decisions accordingly.” Peer-reviewed. The first-year bump dissolves over the 3-year horizon; compressed time-to-clarity, not compounding capital, is the real product. https://doi.org/10.1287/mnsc.2018.3256

3. Benjamin L. Hallen, Susan L. Cohen & Christopher B. Bingham, “Do Accelerators Work? If So, How?” Organization Science 31(2), 378-414, 2020. Accepted-versus-almost-accepted design at a set of top US accelerators, controlling for startup quality. Some but not all programs aided venture development: top-tier produced large gains, second-tier delivered beneficial learning without the sorting/signaling premium, and one of the studied programs actively inhibited venture development along some dimensions. Mechanism credited: “broad, intensive, and paced consultation.” Peer-reviewed; accepted-vs-almost-accepted quasi-experimental design. https://doi.org/10.1287/orsc.2019.1304

4. Julie Yoo (a16z Bio + Health), “The New Go-to-Market Playbooks for Digital Health Startups.” The cleanest single resource on commercial design for digital health. Named-investor essay; fetched in full. https://a16z.com/the-new-go-to-market-playbooks-for-digital-health-startups/

5. Koby Conrad, “6 Growth Channels That Actually Work” (video). Conrad scaled Rupa Health from $5M to $75M in annualized lab sales as its head of growth before founding his own company; a masterclass in multi-channel healthtech B2B sales. Disclosure: I’ve sent this to many founders and invested in Conrad’s friends-and-family round.

1. Vadim Gordin, “Selling to Health Plans,” 2023 workshop (video). Panel with Jacob Victory (former Centene executive) and Bobby Murphy (a sales lead with exclusive Medicaid-plan experience), and a fireside chat moderated by Rohan Siddhanti (then GTM lead at the EHR Healthie, now in a healthtech GTM role at Anthropic). First-person workshop.

2.1 Five clinical truths that persist in the research literature
2.2 Where Apps vs. SMS wins
2.3 LLMs in the care delivery stack

2.1. Five Clinical Truths

Five claims hold across the strongest studies. Most operator decisions follow from them; trial detail lives in the appendix.

First: SMS improves medication adherence at chronic-disease scale.

Thakkar 2016 [16] pooled 16 RCTs (n=2,742) across five chronic conditions: SMS roughly doubled adherence odds (OR 2.11, 95% CI 1.52-2.93). The effect generalizes; design choices (personalization, frequency, bidirectionality, content) didn’t separate in subgroup analysis. Configuration is the operator’s call.

Second: program adherence predicts outcomes; app engagement does not; small SMS-driven adherence gains in short trials do not predict endpoint changes.

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3.1. How to instrument and measure an effective program.

Builder TLDR: Instrumentation answers two questions: did the prescribed action happen, and does what you measure tie back to “effective.” Bottleneck pattern names the action layer; engagement metrics answer neither.

Effective as used in our field guides means something very specific. A program that survives its unit economics, hits criteria that enterprise healthcare buyers purchase and renew on, and most importantly, reduces human suffering.

A note on scale. What follows describes instrumentation for a program past first design. A team running its first 150 patients only needs to know which layer the mechanism sits in and instrument that layer; the full panel comes with scale.

Lens 1: Did the prescribed action happen?

The first instrumentation question is whether the prescribed action actually happens. Three bottleneck patterns recur, each pointing at a different action layer.

Titration-bottlenecked conditions (hypertension, polypharmacy, oncology dose adjustment, anticoagulation) make Layer 4 (clinical process and escalation) the operator’s primary leading indicator.

Track titration rate, escalations closed, time-to-human-review, false-positive burden. Bressman BP Pal demonstrates the pattern at the mechanism layer: program-driven data flow produced higher titration in the SMS arm, and titration is what moves the outcome the buyer pays on. (The trial was null on its primary six-month SBP endpoint with high attrition; the titration-rate signal is exploratory and operator-relevant, detailed in Section 2.1, Truth 4.)

Behavior-bottlenecked conditions (weight loss, glycemic management via diet and exercise, smoking cessation, physical activity) make Layer 3 (program adherence) the primary leading indicator. Track compliance ≥80% or the condition-specific equivalent: smoke-free days, food-log compliance rate, step counts above threshold. A patient who logs meals and consequently eats less is moving the outcome directly.

Reach-bottlenecked programs (Medicaid outreach, post-discharge, public-health campaigns where the binding constraint is delivery rather than action) make Layer 1 (reachability) the primary leading indicator. Track currently reachable, dormant-but-reachable, hard unreachable, re-engagement attempt success.

Figure 10. Engagement vs. program adherence as leading indicators for clinical outcomes.

Engagement metrics don’t answer the action-completion question.

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1.1. The False Binary

Figure 1. The reach-vs-response job-allocation frame.

“Should we build an app or send texts?” frames SMS and app as competing technologies. They are channels for different jobs. A chronic care management program runs many tasks: enrollment, reminders, vital capture, symptom escalation, education, trend tracking. Each has an optimal channel. Sending all of them through a single channel leaves performance on the table. The default single channel is the app, because apps are the venture-backed default.

The operative distinction is reach vs. response. SMS is the channel when reach is the goal; app is the channel when response is the goal. Reach jobs (medication prompts, post-discharge check-ins, appointment confirmations) succeed when the message arrives and the patient acts outside the channel. Response jobs (logging vitals, multi-drug regimen entry, behavior-change modules) succeed only when the patient produces structured input inside the channel. Most chronic care programs contain both kinds of job.

The productive question is which jobs are reach jobs and which are response jobs, not which channel wins.
We’ll go into detail on this distinction in section 2.2, but it’s important to establish here upfront.

1.2. The Operating Stack

Every digital chronic care program runs the same causal chain from message delivered to economic outcome. Each layer is its own measurement object, its own design decision, and its own failure mode.

Figure 2. The Operating Stack — the six-layer causal chain every digital chronic care program runs.

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This document is meant to be an actionable artifact for builders.

Primary (numbered) references are cited inline in the body; Supplemental references in each specialty section are additional reading.

These are organized by specialty rather than alphabetical order or the sequence in which they appear in the text.

Many of the references below are open-access and freely available. Others sit behind academic subscription paywalls. For paywalled papers, Google Scholar is the best place to start; it often surfaces a PDF or preprint via an institutional repository or author’s homepage. PubMed Central (PMC) holds full text for NIH-funded work after the 12-month embargo. When all that fails, sci-hub.ru remains a working option for readers who choose to use it.

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• Allocate by reach vs. response.

  • SMS for reach jobs (action outside the channel);

  • Apps for response jobs (structured input inside it).

  • Most chronic care programs need both.

• The platform is never the intervention.

  • Platforms exist to affect what humans do beyond them: patients taking pills, clinicians titrating, care teams closing the loop.

  • A delivery vector, not the mechanism.

• Audience decay is the SMS-vs-App economic divider.

  • App audiences decay continuously through uninstalls, OS push restrictions, and device churn; the reach floor surfaces only when re-engagement fails.

  • SMS preserves the addressable list across years through phone-number persistence.

  • Healthcare CAC is paid upfront, so audience decay erodes the reach floor before outcomes are measured;

  • Medicaid weakens this but doesn’t invalidate it.

• Program adherence (actual patient behavior) is the leading indicator of clinical outcomes; engagement should be limited to a product-health metric.

  • Track completion at 80% (or the condition-specific equivalent).

  • Engagement stays on the product dashboard.

  • The patient heals and buyer renews on health and economic outcomes.

    • Those are what should drive your operational decisions and dashboards.

• Combined modalities beat single-channel automation.

  • Across the trial record, pairing a delivery channel with a staffed action layer beats single-channel automation.

  • Program architecture beats channel.

• Stand up regulatory compliance before launch.

  • Register every brand and use case with The Campaign Registry; build to the FCC treatment-purpose exemption and the CTIA floor.

  • Retrofitting TCPA, HIPAA, or 10DLC after launch is the most expensive architectural choice an SMS program can make.

Author’s note

For the past six years, I’ve mentored healthcare founders through NYU accelerators and public workshops that I host in New York City. The thesis behind that work has been consistent:

Only new mistakes.

VC-backed healthtech has a pattern of new builders impaling themselves upon old failure modes. Research findings stay trapped in journals. Operational lessons stay trapped inside health plans and care teams who’ve done this work for decades.

These Field Guides exist to help close that gap.

The goal is effective digital health programs: interventions that survive their unit economics, improve the outcomes enterprise buyers renew contracts on, and most importantly reduce patient suffering.

Part of my unfair advantage as a mentor is that I have operators in my network who have built and delivered care at population scale. They’ve already learned many of the expensive lessons and have generously provided their feedback and comments on this piece.

• Evan Huang, developed SMS programs reaching 2% of the U.S. chronic care population, First as co-founder of CareSignal and then as CTO of Lightbeam after the acquisition.

• Ramon Lizardo, MD, MBA, built the nation’s largest ED diversion company, covering 9.5M lives across 150 health plans as CEO of Tele911.

• Brittany Sigler, MPH, DrPH, was a product leader across multiple population-scale outreach programs at CVS Caremark and Aetna.

This field guide distills lessons from 30 years of clinical, operational, regulatory, and behavioral literature surrounding healthcare communication systems. It is not clinical, legal, or compliance advice.

This document was developed using Claude Cowork + custom skills as my primary collaborator, with Gemini, ChatGPT, and Perplexity providing redteam support. My use of AI is loud and proud. The arguments and conclusions are mine.

Vadim Gordin.
Only new mistakes.

© 2026 Vadim Gordin. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

Contents

The piece is organized in three parts. Each can be read on its own.

Part 1. The System.

• The conceptual frame: why the channel choice is the wrong unit of analysis,

• the six-layer causal chain from message-delivered to therapeutic and economic outcomes that every digital chronic care program runs,

• the economics of reachability between SMS and Apps, and

• how scaffolding can dramatically improve retention and engagement rates.

• Action beyond the platform is the actual intervention.

Part 2. What the Evidence Supports.

• The five clinical truths the literature supports,

• where apps and SMS each win on a per-task basis,

• what is the cost and consequence of platform failures, and

• how LLMs fit inside the operational stack.

Part 3. Operating the System.

• How to measure and instrument an effective program and

• common failure modes in both commercial and research programs.

• Will me compliance architecture survive scrutiny or create liability?

Appendix

• The appendix is intended to be a reference tool rather than something to read end-to-end

• All 75+ cited clinical trials by topic and specialty.

  • If you’re building a renal care or ob/gyn platform for Medicaid and want to read up on how previous SMS interventions have performed, the appendix is your on-ramp.

• The 3 major regulatory regimes governing SMS in the US organized into an actionable reference for builders.

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