There's a version of the Series A benchmark conversation in healthcare that's borrowed wholesale from enterprise SaaS: $1M ARR, 3x growth, net revenue retention above 110%. Those numbers aren't wrong exactly — but they're often measuring the wrong things for a healthcare company, and founders who optimize for them can end up with a story that looks good on a slide and falls apart the moment an investor does real diligence.
Healthcare is different because the sales cycles are longer, the buyer structures are more complex, and the relationship between revenue and clinical deployment is messier. A digital health company might have $800K ARR but be live in 3 hospital systems across 4 states with a clear contract pipeline worth $4M. That's a better Series A story than a company with $1.2M ARR concentrated in one buyer who's still deciding whether to expand. The number matters, but it matters in context.
The metrics we actually scrutinize
We've developed a framework over years of looking at healthcare Series A companies. The metrics we care about fall into roughly four buckets, and none of them are the standard SaaS metrics in isolation.
Clinical deployment depth: How many patients or clinical encounters is the product touching, and what does the trajectory look like? This matters more than contract count for early-stage digital health. A company deployed on 2 contracts touching 40,000 patient-months is more interesting to us than one with 15 contracts touching 3,000. Volume of clinical use suggests the product is actually being used in workflow, not just purchased and shelf-warehoused.
Net Revenue Retention by buyer type: Aggregate NRR in healthcare can be misleading because buyer types renew and expand on fundamentally different timelines. Hospital system NRR is typically slower to realize than employer or PBM contracts — budget cycles are annual and expansion often requires committee approval. A 95% NRR that looks low against a SaaS benchmark might be excellent for a hospital-selling company. We want to see NRR separated by channel, not blended.
Time to clinical go-live: This tells us something about implementation quality and sales cycle. A company that signs contracts and gets to clinical go-live in 60 days has figured something out operationally that a company requiring 180-day implementation hasn't. Faster go-live means faster evidence collection, faster NRR realization, and usually better customer satisfaction. We track this as a proxy for operational maturity.
Clinical outcomes data quality: For healthcare companies, we want to see whether the company is collecting outcomes data systematically, not just claiming outcomes anecdotally. Even early, preliminary data — 6 months of post-deployment outcome measurements on a 200-patient cohort — tells us the company is thinking ahead. Companies without any outcomes data collection infrastructure at Series A face a long and expensive problem at Series B.
What "strong traction" looks like in different sub-sectors
| Sub-sector | Series A Traction Signal | Revenue Range |
|---|---|---|
| Digital therapeutics (Rx) | Pilot with 1-2 pharma or health system partners, prescriber count growing | $500K–$2M ARR |
| Remote monitoring platform | 3+ contracts live, 5,000+ enrolled patients, RPM billing working | $1M–$3M ARR |
| Clinical AI / SaMD | 2+ health system deployments, EHR-integrated, outcomes pilot started | $600K–$2.5M ARR |
| Biotech / platform | IND filed or pre-IND data package, 12-18 month runway to Phase I | N/A (grant/non-dilutive funding stage) |
| Medtech (Class II) | 510(k) cleared or active submission, 3+ clinical sites, LOIs for commercial launch | $300K–$1.5M initial revenue |
The metrics that concern us regardless of numbers
Revenue growth rate and total ARR can look excellent while the business is fundamentally fragile. The patterns that concern us most at Series A:
- More than 50% of ARR from a single buyer — even if it's a marquee name, concentration risk in healthcare is severe because budget cycles are unpredictable and clinical champions turn over
- Revenue growth driven entirely by new contracts with zero expansion of existing accounts — this often signals the product isn't sticky enough to expand within accounts
- Declining clinical utilization within deployed accounts — contracts renewing but actual usage falling is a yellow flag that becomes a red one quickly
- No pathway from current revenue model to the reimbursement or payor revenue model — some companies are sold on direct hospital contracts today with an implicit assumption that payor revenue is coming. We want to see the bridge, not just the assumption.
How we think about the pre-revenue biotech case
For biotech and platform companies that haven't generated product revenue yet, the Series A metrics conversation is entirely different. We're evaluating scientific de-risking milestones: What does the IND-enabling data look like? How tight is the CMC package? What's the regulatory path to Phase I, and has FDA informally validated the approach?
The dollar equivalent of Series A traction for biotech is the quality of the scientific case and the experienced-ness of the team around it. We've backed biotech companies with zero revenue and a $15M Series A because the preclinical data was clean, the target was validated, and the founding team had done this before. We've passed on companies with impressive preclinical packages because the CMC strategy had fundamental problems that would surface in manufacturing scale-up.
The common thread across all of these: what we're looking for isn't a number. It's evidence that the founding team has done the hard work that only they could have done. The metric is how it reads when you dig into it, not what it says on the summary slide.
