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.

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TLDR

• Coach your funnel to two bars: one in three from qualified lead to conversation, one in five from conversation to wired check.

  • The conversation bar sits at the corpus median; clearing both is where you stop tinkering and add volume.

• Read Carta and AngelList for how rounds end; read founder narratives for how raises run.

  • Cap tables record price, dilution, and instrument. Names contacted, per-step conversion, and wire slippage never reach them.

• Plan one qualified investor name per $5-10K of target round.

  • Cold lists convert at 3-4% contact-to-check; a $750K round wants 80-120 names, not 30.

• Budget the calendar for the build and the wire tail; the active raise is the short middle.

  • Both DocSend’s survey and the corpus put roughly three raises in ten past six months.

• Respect the direct-check ceiling of 10-40 yeses per round; go wider only through a pooled vehicle.

  • Syndicates, RUVs, and crowdfunding are the only rounds in the corpus that broke it.

• Track committed and wired as separate columns from the first yes.

  • The founders who measured slippage counted only wires; verbal commitments evaporated without ever being reported as no’s.

Author’s note

This piece is the data companion to Raising Angel and Preseed Funding for Digital Health [2]. The dataset ships with it: the comparison grid, the per-source verification ledger, and a primary-source link for every account.

This piece was built in Claude Cowork with a custom skill stack that I developed. Claude was my primary collaborator; Gemini, ChatGPT, and Perplexity served as red-team research associates. My use of generative AI is loud and proud, and in this piece it is also the method: Claude agents swept the open web for founder accounts and tabulated them, and every number they extracted was verified against its primary source before it entered the grid. The thesis and the judgment calls are mine.

Contents

1. The numbers that trace back to nobody.

   • Stop planning your raise on funnel folklore.

2. What Carta and AngelList see, and what they can’t.

   • Match each dataset to the decision it can support.

3. How the dataset was built.

   • Judge the verification standard before trusting any figure, mine included.

4. What the data shows.

   • Calibrate your funnel, calendar, check count, and clock against measured raises.

5. What founders should do with it.

   • Run five decision rules in your CRM from the first outreach.

6. What investors should take from it.

   • Read process discipline as a diligence signal; pace angel rounds on angel math.

7. Contribute your raise.

   • Add your account to the corpus, or correct your row.

1. The numbers that trace back to nobody

[001]When Jon Lee was raising Pickle’s pre-seed, I read his funnel every week, the way I read every founder’s I coach: qualified leads into conversations, conversations into checks. The week the two rates turned healthy, I told him to stop worrying about the raise, stop tinkering with the pitch and the targeting, and put 20-30 new investors into the top of the funnel every week; the round would close in three months. It closed three months later, almost to the day, and Jon came into my office bewildered at the precision of the forecast. No magic was involved. Healthy conversion against known weekly volume is arithmetic.

[002]The arithmetic came from a vantage almost nobody gets. For fifteen years my venture partner and I led the Future Labs at NYU, where we supported 300+ teams to $2.7B in capital raised and 44 exits. Watching that many raises run side by side is what taught me what healthy conversion looks like, when to stop tinkering with a venture’s design, pitch, and investor targeting, and how to forecast a close. The discipline works at cohort scale: 80% of my final cohort at Catalyst, NYU Tandon’s public-facing pre-seed-to-seed accelerator, closed a round within three months of graduation, and conversion-driven coaching is how we achieved that. 99% of founders will never sit inside a network like that, and the public record is no substitute: the conversion figures circulating in accelerator curricula and fundraising posts trace back to nobody (the deck cites a talk that cites a post that cites nothing), and no measured aggregate of per-step angel-round conversion had ever been published. I built this dataset to put the thing the Future Labs dataflow gave me into anyone’s hands, with sources attached.

[003]The gap it fills is concrete. How many investor names does a $750K round need? How many weeks will the raise take, and which weeks do what? What does a healthy funnel convert at each step, and when does a quiet one mean the pitch is broken rather than early? The advisory tradition answers with first-person judgment. Alex Iskold, Paul Graham, Charlie O’Donnell, Elizabeth Yin, and Mark Suster have shared theirs generously for years, and it is woven through the parent guide [2]. But judgment can’t be audited, and when two advisors disagree, the founder has no way to adjudicate.

[004]The Founder Narratives dataset is fifty verified first-person accounts of angel and pre-seed raises, published between 2008 and 2025, each reduced to the same operating row. Across the fifty, founders worked roughly 4,500 investor names, held some 2,000 first conversations, closed several hundred checks, and banked more than $150M across three continents [1].

Figure 1. Active weeks against dollars raised; bubble area is names contacted. Source: HH Founder Narratives [1].

If you read Part II, you have seen this chart; it opens the process guide as a promise and a warning. Every bubble is a real raise from a named founder, and no existing dataset could have produced it. The promise comes first: angel rounds close at every size, on both clocks, and on three continents. The supply side backs the promise up. The US alone counted 422,000 active angels in 2023, funding 55,000 ventures with $18.6B; Europe’s formal networks add 45,000 more, and that census misses the informal majority [8]. Roughly 500,000 angels will write checks into some 60,000 companies around the world this year. Your angels exist. What separates the founders who find them is process, and process is learnable.

2. What Carta and AngelList see, and what they can’t

Carta operates the largest cap-table dataset in the industry, roughly 500,000 signed SAFEs and notes since 2018 and more than 10,000 seed rounds from 2021 through late 2025, and Peter Walker has spent years cutting it against most of the consequential questions a founder faces: round sizes, valuations, dilution by instrument, cofounder splits, founder salaries, time between rounds [3]. AngelList’s deal flow has given its data science team, Abe Othman’s work in particular, a comparable vantage over the syndicate market [4]. DocSend’s index adds deck behavior: read time by intro type, slide order, survey-recalled fundraising duration [5]. Parts I and II lean on all three.

Each instrument records an ending. A cap table is a record of how a raise finished: who owns what, at what price, on which instrument. Platform deal flow records the transactions that reached the platform. Deck telemetry records what happened after the deck arrived. None of them can see how the raise ran: how many names the founder contacted, what fraction became conversations, how long she calibrated before opening the round, how many verbal yeses never wired. Those numbers live in the founder’s CRM, or in her head, and they never touch the systems the industry aggregates. No platform’s data exhaust contains them, so no platform could have published them.

A selection problem sits on top of the measurement problem. Cap-table and platform data are conditioned on completion: a round appears in Carta because it closed, and on AngelList because it transacted there. The raise that stalled at week ten and the funnel that leaked at the conversation step leave no record at all. The Narratives corpus carries its own selection bias, which section 3 takes seriously; the point is that the two biases run in different directions, and a founder planning a process needs the process-level view even when it is noisier.

The missing data was sitting in public the whole time. Founders have been writing detailed post-mortems of their raises for fifteen years, on Medium, Substack, LinkedIn, and company blogs, some with complete funnel tables: Niels Hoven published the anatomy of Mentava’s 50-angel round, Obum Egbuna the 300-name outreach behind Chezie’s $780K, Williams the 99-contact, 36-check funnel behind Urban Jungle’s £1M [1]. Each was read as a story and cited as an anecdote. Fifty of them, held to one schema and verified against their sources, become a dataset. That aggregation is the entire contribution.

3. How the dataset was built

Claude agents swept Substack, Medium, LinkedIn, and the open web for first-person accounts of angel and pre-seed raises, with three admission criteria: a named founder, a first-person telling, and specific process numbers rather than a victory lap. Each candidate account was fetched in full and its figures checked against the primary source. Accounts that couldn’t be fetched (login walls, deleted posts, audio-only) were held as pending and excluded from every aggregate. A per-source verification ledger records what was checked, when, and to what standard [1].

From every account we extracted the same operating row: names contacted, first conversations, checks closed, and the conversion at each step; calibration time versus active raise time; check sizes and dollars raised per name; the instrument and cap-table container; what the anchor check changed; and whether committed money failed to wire. Blanks are data: where an account doesn’t report a field, the cell stays empty and nothing is imputed. Derived values (currency conversions, midpoints, floors under “50+” claims) are flagged as derived, with the basis recorded in the grid.

Where the public record ran out, I reached out to the founders directly on LinkedIn, and many happily responded, filling in data points missing from every published account. Williams confirmed that his 36 closed checks exclude two or three verbal yeses that never signed or wired, which he quietly reclassified as no’s; published yes-counts likely absorb their dropouts the same way. Egbuna confirmed zero commit-to-wire slippage on Chezie’s RUV round. Three of the six fully documented funnels in Part II are founder-confirmed by direct correspondence [1].

Fifty self-published accounts are a self-selected sample: founders who closed, and who chose to write about it, skew more transparent and more successful than the population. Market regimes aren’t comparable; a 2021 yes-rate and a 2023 yes-rate describe different worlds. UK tax relief inflates British angel yes-rates against American ones. And accounts define “contacted” differently (emailed, listed, met), so the grid tracks each account’s denominator and refuses cross-denominator averages. The corpus argues from named raises and recurring clusters, and holds its aggregates more loosely than one would hold Carta-grade data. The claim standard is pattern recurrence: when the same structure appears across founders who never read each other, it is worth planning around.

4. What the data shows

A healthy funnel is one in three, then one in five.

The corpus’s central numbers: a healthy angel raise converts about one in three qualified leads into a real conversation, and about one in five conversations into a wired check. Compounded, roughly one qualified lead in fifteen becomes a check. The two fully documented under-networked funnels ran below that bar, at 3-4% lead-to-check on cold lists, and still closed; the lean funnel bought a longer raise. Warmth moves every number. Own-network founders ran 14-36% lead-to-check, and the relationship-rich skipped the funnel entirely: the shortest raise in the corpus closed $555K on a single ask, twenty-one days, no deck, seventeen years of earned trust [1].

Figure 2. Per-step conversion for six fully documented funnels. The drawn ~1 in 3 bar applies to the contact-to-meeting step; the conversation-to-check bar is ~1 in 5 (Figure 3). Source: HH Founder Narratives [1].

The location of the leak names the problem. Leads that never become conversations are a targeting or story problem: you aren’t earning the meeting. Conversations that never become checks are a proof, anchor, or close-date problem: you aren’t closing it. The two send you to different fixes, and adding leads multiplies the conversion you already have. The rates are not readable until you have pitched about twenty check-writing strangers; friendly meetings and practice pitches do not count, and a thinner sample is tuition rather than a verdict. Expect roughly half the healthy rates until the first meaningful check lands; account after account shows the anchor changing the psychology of every meeting that follows [1].

Be precise about what each bar asks. Across the ten documented funnels, the median conversation-to-check is about one in five: two checks from ten real conversations is the middle of the corpus, with warm and inbound raises above one in two and cold institutional funnels below one in ten. The conversation-to-check coaching bar sits at that median. The top step runs higher; documented funnels cluster around one in three lead-to-conversation, and that is the bar there. A funnel clearing both is healthy: stop tinkering with the pitch and the targeting, feed the funnel on a weekly schedule, and let the arithmetic run.

Figure 3. Conversation-to-check for the ten documented funnels, with 95% intervals; the coaching bar sits at the corpus median. Source: HH Founder Narratives [1].

Table 1. Median 22%; the 30% mean is pulled up by the warm and inbound rows. Per-row caveats (institutional-only counts, inbound funnels, denominator definitions) are in the chart data tabs [1].

The active raise is the short middle

Ask a founder how long a raise took and you get one number; decompose the reported timelines and you get three. The build (sharpening the pitch, assembling the list, accumulating proof) and the wire tail (collecting money already committed) surround a short active window. Hoven spent four months iterating Mentava’s pitch, four weeks raising full-time, and roughly three more chasing wires from an oversubscribed round [1]. The pattern holds down the chart: the part that looks like fundraising is the shortest part, and the calendar mistake founders make is budgeting for the middle while the build and the tail spend the runway.

Figure 4. Build, active raise, and wire tail, in reported order. Source: HH Founder Narratives [1].

On total duration, DocSend’s seed survey and the corpus agree where it matters: roughly three raises in ten run past six months, so the marathon is a third of the market rather than a failure case [5][1]. They disagree at the short end (35% of corpus raises ran six active weeks or fewer, against 15% in DocSend’s recall) because they measure different quantities: DocSend records survey-recalled total fundraising time, the Narratives measure documented active raise time. Plan your calendar on the decomposition, not on a single recalled number.

Figure 5. Survey recall versus documented founder accounts. Sources: DocSend seed survey [5]; HH Founder Narratives, n = 26 [1].

Direct checks have a ceiling

Plot average check size against round size and the direct-check rounds cluster between the 10-check and 40-check diagonals: rounds built from individually signed checks top out at roughly 10-40 yeses, because every signature is a relationship, a paperwork packet, and a cap-table line. The rounds that broke the ceiling pooled their long tail into a container: a syndicate, an RUV, or crowdfunding. Egbuna’s RUV took checks from $1,000 by debit card and landed fifty investors on one cap-table line [1]. Part II’s $10K floor on direct checks falls straight out of this chart: below the floor, take the believer’s money through a container or not at all.

Figure 6. Average check versus round size, log-log; dashed diagonals mark a constant number of checks. Source: HH Founder Narratives, n = 17 verified raises [1].

The clock is a demand reading

The corpus documents two pacing patterns and almost nothing between them. The sprint piles up intros during a calibration period, then lands every meeting in one dense window with a close date every investor can see. The marathon keeps the round open while proof accrues, with a deadline set investor by investor. The meetings in the two diagrams below are identical; the only thing that changes is when they happen, and that timing is the entire difference in leverage [9].

Figure 7. The Sprint: conversations accumulate early; decisions land together. Concept after Daniel Olmedo’s meeting-density diagram [9].

Figure 8. The Marathon: each conversation starts before the last one resolves, on the same 12-week skeleton. Concept after Daniel Olmedo’s meeting-density diagram [9].

In the corpus, sprints closed materially larger rounds than marathons, and the causal arrow points backward: founders don’t raise more because they sprint, they get to sprint because demand is strong [1]. That makes the clock a reading the market takes of you, and one you re-check as the raise runs. A planned sprint still raising at week ten is the market answering the demand question; the response is to resize the round, reset each investor’s deadline, and re-declare the mode as a decision, because a sprint that decays into an undeclared marathon reads, from the investor’s side, like a deal that has been shopping for months.

Committed money fails to wire at a measurable rate: Riesen’s pipeline carried four investors in the closed stage who never wired, and Williams filed his two or three silent yeses as no’s [1]. The founders who could report their slippage precisely were the ones who had tracked committed and wired as separate columns. The gap between those columns is a real funnel stage with its own loss rate, and no published yes-count includes it.

5. What founders should do with it

Five decision rules, runnable in a CRM from the first outreach.

• Size the list to the round: one qualified name per $5-10K of target. A $750K round wants 80-120 named angels, operator-angels, and micro-VCs that write at angel sizes. At healthy conversion, every check sits on ten qualified names; on a cold list, contact-to-check runs 3-4%, so 30 names is a rehearsal, not a fundraise [1].

• Instrument two conversion columns from day one. Lead-to-conversation and conversation-to-check, read against the bars (one in three, one in five) once about twenty check-writing strangers have heard the pitch. Fix the leaking stage before adding volume; a bigger list multiplies the conversion you already have. Once both steps clear the bar, stop tinkering and feed the funnel on a weekly schedule; healthy conversion against known volume is a close date you can put on a calendar.

• Pick your clock in writing, and re-read it at week ten. Sprint or marathon, declared, with every conversation carrying its own deadline. The marathon is a legitimate third of the market; the undeclared marathon is a shopped deal.

• Set a $10K floor on direct checks, and stand up a container below it. The 10-40-yes ceiling is structural. An RUV or syndicate turns the long tail of small believers into one cap-table line instead of thirty signature chases [1].

• Collect every yes the day it lands, and track committed versus wired. A SAFE binds one investor at a time; nothing rewards waiting, and the corpus’s slippage data says a verbal yes is a pipeline stage, not money [1].

The founders who struggle most with this, in my coaching, have been the clinicians, engineers, and scientists coming to venture for the first time; the open-endedness of a raise sits badly with people whose day jobs run on protocols and proofs. They are also the founders best built for it. Each of those professions trains its people to make informed decisions with limited knowledge: a treatment plan from an incomplete picture, a design margin against loads nobody can fully model, an experiment that pays for itself in information either way. Conversion-driven fundraising is the same discipline pointed at capital: instrument the funnel, read it once twenty strangers have heard the pitch, act on the leak it names. That reframe is what has gotten my most uncertainty-averse founders over the hump for years, and sharing it beyond the founders I can coach in person is half of why this dataset exists.

None of this is fundraising exotica. A named list, weekly stage transitions, conversion read at each step, leaks fixed rather than worked around: this is the same discipline your company will need for sales, partnerships, and hiring. The raise is a trial run for the business-development function, and the corpus founders who closed on the timeline they planned ran it that way [1].

6. What investors should take from it

The same numbers read differently from the other side of the table.

• Process discipline is a diligence signal. A founder who can tell you her two step rates, her denominator definition, and where the funnel is leaking is showing you the sales motion her company will run after the round. The corpus founders who closed on plan all kept named lists and weekly reviews; the improvisers drifted past the runway they intended [1].

• Pace angel rounds on angel math. An angel round needs fifteen to forty separate yeses; an institutional raise ends at one or two term sheets. Judging an angel raise’s week-eight state against fund-raise pacing misreads it, in either direction. The comparator funnels (Right Side Capital’s institutional model, the Gompers 100-to-1 VC funnel) describe a different process; Part II works both [2][6].

• Your pass is data the founder can use. The angels who engaged seriously and passed hold the most useful read on a round, precisely because they have no position. When a founder asks for ten minutes to understand where the process is losing people, take the call; the corpus says the disciplined ones act on it [1][2].

• Pooled vehicles moved the floor. A $1,000 debit-card check through an RUV is now ordinary infrastructure. Expect more rounds shaped as a handful of direct anchor checks plus a pooled tail, and price the cap-table cleanliness accordingly [1].

• Mind what the corpus can’t say. It is conditioned on closing: it shows what closing looked like, not the odds of closing. The base rates sit elsewhere: healthcare-focused VCs fund roughly 1% of what they see, and angel groups fund 3-5% of applications [6][7].

7. Contribute your raise

Fifty accounts is a start. Sixty thousand companies will close an angel round this year [8], and the process data behind those raises should not have to be folklore. If you have raised an angel or pre-seed round and written a first-person account with numbers in it, or are willing to write one, send it to me and I’ll add it to the grid with full credit and a primary-source link. And if your raise is already a row and I have something wrong, tell me: every figure carries its source, and corrections are cheaper than myths. The strategy this data underpins lives in Parts I and II.

References

1. Vadim Gordin, HH Founder Narratives dataset: 50 verified first-person founder accounts of angel and pre-seed raises (2008-2025), comparison grid, chart data tabs with confidence intervals, and per-source verification ledger. Ships with this piece; every founder cited above (Hoven, Egbuna, Williams, Riesen, Zuo, Chalmer, Gillenwater, Olmedo) is keyed to a primary-source link in the grid.

2. Vadim Gordin, Venture Funding for Fun and Profit: “Part I, The Narrative” (https://healinghealthtech.substack.com/p/raising-angel-and-preseed-funding) and “Part II, The Process” (https://healinghealthtech.substack.com/p/raising-angel-and-pre-seed-funding), Healing Healthtech, June 15, 2026. The guide this dataset was built for.

3. Carta, State of Pre-Seed and State of Private Markets series; Peter Walker, Head of Insights. Cap-table data: ~500,000 signed SAFEs and notes since 2018; 10,000+ seed rounds 2021-Q3 2025. https://carta.com/data/

4. AngelList data science analyses of platform deal flow (Abe Othman). Referenced as context for what platform datasets cover; no figures in this piece are drawn from AngelList data.

5. DocSend Startup Index (Dropbox). Deck read-time by intro type and survey-recalled fundraising duration. https://www.docsend.com/index/

6. Paul Gompers, William Gornall, Steven N. Kaplan, and Ilya A. Strebulaev, “How Do Venture Capitalists Make Decisions?” Journal of Financial Economics 135(1), 2020. Median VC funnel ~100 opportunities considered per investment closed; ~78 in the healthcare subsample.

7. Angel Capital Association, “Trends in Funding Rates,” Dealum platform data, 2022-2024. Application-to-funded: pre-seed 3.0%, seed 4.5%.

8. Center for Venture Research (Jeffrey Sohl), The Angel Market in 2023, University of New Hampshire, 2024 (422,350 active US angels; 54,735 ventures; $18.6B); EBAN Statistics Compendium 2023. The rounded global claim is conservative.

9. Daniel Olmedo, “Funding Rounds’ Cheat Sheet 2026,” Fundraising Journey (Substack), Dec 2025. Meeting density as the lever that converts volume into leverage; the sprint and marathon diagrams follow his meeting-density concept.

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Part 1 walked Kinetic from concept to the VC healthcare promised land - owning risk and dominating a category that it had created.

This essay - Part 2 - will use 4 very different companies that found success owning risk supporting mothers during difficult pregnancies.

Before that thought, let’s dig into what we established.

We used Kinetic…

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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

Venture Funding for Fun and Profit

(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

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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.

Read more

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.

Read more

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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