EHR-native pre-screening and outreach OS for cardiology and obesity trial sites to fill studies from routine specialty visits.

1. New capital into AI trial recruitment makes specialty-site operators more willing to buy infrastructure instead of adding coordinators.
2. Cardiology and obesity expansion creates immediate demand for software tuned to cardiometabolic workflows rather than legacy GI research ops.
3. Network-of-sites matching favors software embedded in clinic operations, where patients already appear, over broad external recruitment campaigns.
4. Patient-study matching is now an explicit budget line, making it easier to sell a focused pre-screening product with measurable enrollment ROI.

Catalyst. Iterative Health's financing and move into cardiology and obesity show sponsors and site networks now need specialty-specific recruiting infrastructure fast enough to support new protocol expansion.

The product connects to the specialty practice's EHR, scheduling feed, and referral intake to run protocol-specific eligibility logic continuously. It creates daily coordinator worklists for pre-visit outreach, in-visit study discussion prompts, and post-visit follow-up with documented inclusion and exclusion reasoning. Research leaders get site-level dashboards showing candidate flow, screen-fail reasons, and protocol bottlenecks by clinic. Over time, the system learns which sites, diagnoses, and visit types convert into enrollments, helping sponsors and site groups choose better studies and open fewer underperforming sites.

What's different. Most trial-recruitment vendors start with external lead generation or sponsor dashboards. This company starts inside specialty practice operations, where eligibility signals are freshest and coordinators can act before patient leakage. That creates a proprietary feedback loop on which visit types, diagnoses, and site behaviors actually produce screened and enrolled participants in cardiometabolic trials.

Jobs to be done

flowchart LR
  Buyer[Research ops lead] --> Pain[Manual chart review misses eligible patients]
  Pain --> Product[Protocol-specific pre-screening OS]
  Product --> Outcome[Faster enrollment and higher site utilization]

Executive takeaways

• Enrollment pain is real, but the most actionable gap is no longer generic lead generation; it is converting routine specialty visits into protocol-specific worklists before the patient leaves the clinic.
• Recent funding and partnership activity shows AI-enabled recruiting is moving from GI into cardiology and obesity, validating the proposed beachhead rather than forcing the startup to create demand from scratch.
• The strategic opening is at the site-operations layer: large RWD and sponsor platforms help with feasibility, but they still leave last-mile coordinator execution and pre-visit outreach fragmented.
• Cardiometabolic demand is structurally attractive because obesity and heart disease burdens are large, while representation gaps in heart-failure and lipid-lowering trials keep pressure on sites to improve screening reach and documentation.
• Integration is the gating risk: FHIR and SMART reduce friction, but local scheduling feeds, protocol translation, and audit-ready human review still determine time-to-value.
• Broad recruitment, DCT, and site-tech competitors are well funded, so the startup only wins if it stays opinionated about cardiometabolic specialty workflows instead of becoming another generic recruitment dashboard.
• A plausible year-3 outcome is a modest but meaningful specialty-site footprint rather than mass-market scale; the upside comes from later expansion into CRO feasibility, sponsor forecasting, and benchmark data.

Market definition

This market is U.S. clinical-trial operations software for research-active cardiology and obesity specialty practices. It includes EHR-native pre-screening, coordinator work queues, outreach tracking, and site-level enrollment analytics for sponsor-funded cardiometabolic studies. It excludes consumer trial marketplaces, full EDC/CTMS systems, generic CRO services, and sponsor-only feasibility tools.

Customer and buyer

The day-to-day user is the clinical research coordinator or centralized site-operations team. The economic buyer is typically the VP/Director of Research Operations or COO of a multi-site specialty platform, because the deployment touches clinic workflow, IT access, compliance, and sponsor enrollment performance.

Buying triggers

• A newly signed obesity, heart-failure, or cardiovascular study exposes how much referral and chart-review work still happens manually, making enrollment risk immediate rather than theoretical. [8][2][20]
• Sites under pressure to improve representation and enrollment quality need auditable ways to identify and document eligible patients earlier in care flow. [14]
• Availability of FHIR, SMART launch, and maturing EHR recruitment tooling makes pre-visit matching more operationally feasible than it was a few years ago. [48][46]

Willingness to pay

Budget already exists inside adjacent site-ops and recruitment categories: providers and sponsors are already being sold enterprise recruitment, patient-engagement, diversity, and feasibility tools. A focused wedge can win spend when it ties directly to screen throughput, referral quality, and missed-enrollment risk rather than asking for speculative AI budget. [7][16][13][17][28]

Category dynamics

Growth signal 8.2% CAGR (top-down cross-check for the broader clinical trials market)

Validation signals

• Iterative Health’s recent $77M round signals investor conviction that AI-enabled clinical trial recruiting and matching remain a live infrastructure category.
• Iterative’s partnership with US Heart & Vascular is direct evidence that cardiovascular community-site research infrastructure is being assembled now.
• TriNetX markets clinical design and recruitment capabilities on top of large provider-linked EHR datasets, showing mature buyer appetite for data-driven recruiting workflows.
• Antidote’s own content emphasizes referral leakage and obesity-specific readiness gaps, which is exactly the operational pain a pre-visit intake layer is designed to address.
• Medable and Medidata continue shipping site- and AI-oriented trial tooling, confirming that incumbents see site burden and workflow automation as active roadmap territory.

Regulatory & technical constraints

• Clinical research workflows require investigator oversight, process control, and audit-ready documentation consistent with GCP expectations; fully autonomous outreach is unlikely to be acceptable in early deployments.
• Research-oriented FHIR resources and SMART launch help, but production deployments still depend on what each site can expose for appointments, diagnoses, medications, and user context.
• Literature on EHR recruitment support and functionality gaps shows protocol criteria translation, local data quality, and workflow fit remain limiting factors.
• Representation and enrollment gaps in cardiovascular trials create both an opportunity and a constraint: the software must expand reach without weakening documentation discipline.

The competitive map clusters into five classes: sponsor-first data platforms, broad clinical-trial operating systems, community access/recruitment networks, external patient-acquisition vendors, and in-house/manual workflows. The startup is most differentiated if it stays inside specialty practice operations—especially pre-visit screening, referral triage, and coordinator follow-up—rather than competing head-on as a general recruitment marketplace.

Why incumbents do not win by default

• Sponsor platforms. Medidata-class platforms win broad study operations, but they are optimized for horizontal workflow coverage across studies rather than cardiometabolic pre-visit eligibility work inside specialty clinics.
• Real-world data networks. TriNetX-class networks are strong on feasibility, cohort discovery, and provider connectivity, but they do not win by default at the daily coordinator layer where upcoming appointments, exclusions, and outreach timing determine conversion.
• Recruitment marketplaces and site networks. Antidote, Circuit, and Elligo can broaden top-of-funnel access, yet their default model still leaves the target practice to operationalize last-mile qualification and in-clinic follow-through.
• Open standards and EHR-native reporting. FHIR, SMART, and research resources make integration more realistic, but standards alone do not translate protocol logic into site-specific worklists, audit trails, and conversion benchmarks.
• In-house coordinator teams. Manual chart review remains flexible, but it scales poorly across clinics and protocols; the startup wins only if it cuts coordinator workload while preserving investigator oversight and documentation quality.

This company would sell an EHR-native pre-screening and outreach operating system to U.S. multi-site cardiology groups and obesity medicine networks running sponsor-funded cardiometabolic trials. The immediate pain is manual chart review and coordinator spreadsheet triage, which delay first screens, miss eligible patients in routine visits, and waste site capacity. The proposed beachhead is PE-backed cardiology platforms with 20-100 clinics and a centralized research function, especially when they are launching obesity or heart-failure trials and face near-term enrollment risk. The first product should stay narrow: protocol-specific pre-visit worklists, human-reviewed eligibility explanations, and audit-ready outreach tracking tied to live appointments. The go-to-market system is coherent because the same buyer who feels the missed-enrollment risk also controls research operations, can start with one paid pilot, and can expand across clinics once days-to-first-screen and coordinator hours improve. The main competitive advantage is not generic AI matching but cardiometabolic workflow depth inside specialty practice operations, where sponsor platforms and recruitment marketplaces are weaker. Research supports a real but bounded initial market, with an estimated $180M TAM, $42M SAM, and $6M year-3 SOM; expansion into CRO feasibility and sponsor benchmarking is required for larger venture upside. The largest risks are integration drag, unclear budget ownership between sites and sponsors, and the possibility that incumbents bundle similar features before the startup builds a data moat. Exact pricing benchmarks and the true count of research-active cardiology platforms remain incomplete in the source material, so early packaging and sales capacity should be treated as testable operating assumptions rather than facts.

Problem

• Specialty cardiology and obesity trial sites still depend on manual chart review, coordinator memory, and generic EHR reports, so eligible patients are missed during routine care.
• Slow enrollment delays sponsor milestones, increases coordinator labor, and leaves multi-site specialty platforms with underused research capacity even when patient volume is already present.

Solution

• Connect to the practice EHR, scheduling feed, and referral intake to run protocol-specific eligibility logic before upcoming visits.
• Generate coordinator worklists for pre-visit outreach, in-visit study discussion prompts, and post-visit follow-up with documented inclusion and exclusion reasoning.
• Give research leaders site-level dashboards on candidate flow, screen-fail reasons, and visit-to-screen conversion so they can adjust staffing and protocol mix.

Why we win

• The product starts at the site-operations layer, where upcoming appointments and coordinator actions determine whether a matched patient ever gets screened.
• Cardiometabolic specialty depth should yield better protocol templates, lower false positives, and more useful conversion benchmarks than horizontal recruitment tools.
• A reusable protocol-rule library plus cross-site data on referral leakage and screen-fail patterns can become a defensible moat if setup time and proof of ROI improve each quarter.

Milestones

flowchart LR
  Wedge[New obesity or heart-failure study at multi-site cardiology group] --> MVP[Pre-visit protocol worklists and outreach logging]
  MVP --> Proof[Shorter days-to-first-screen and lower coordinator screening time]
  Proof --> Expansion[More clinics and more protocols per customer]
  Expansion --> Moat[Cross-site cardiometabolic benchmark data]

Founding team

Experiment roadmap

Risk assessment

What must be true

• At least 20 priority target accounts in the U.S. have enough live cardiometabolic protocol volume to justify a dedicated intake product.
• A minimal appointment plus clinical data feed can produce a usable pre-visit worklist without a full bespoke EHR integration.
• Buyers will pay from research operations or sponsor pass-through budget before waiting for a full sponsor-led procurement process.
• Coordinators and investigators will trust human-reviewed recommendations enough to use the workflow on live studies.
• The company can achieve repeatable pilot-to-production conversion before incumbents or services firms close the workflow gap.

Open diligence questions

• Which two EHR or scheduling environments dominate the first 20 target accounts, and what data is actually accessible pre-visit?
• What KPI has historically unlocked budget for research-operations tooling at specialty practices?
• How many active obesity, heart-failure, or broader cardiometabolic protocols does the average target platform run per year?
• What compliance constraints govern study-related outreach before explicit study consent at target sites?
• How often do sponsors or CROs influence or subsidize technology selection for site-level recruiting workflows?

Model sanity

• Revenue engine. Base-case revenue comes from active sites growing from 12 at Y1 exit to 50 at Y3 exit at a research-backed $120K annual ARPU per site.
• Must go right. The company must keep pilot-to-production conversion near 60% while reducing protocol setup time enough to preserve the planned 75% gross margin.
• Model breaks if. Cash risk becomes material if sales cycles stretch to about 9 months or lower-automation onboarding pushes ARPU closer to $108K per site.
• Next-round proof. A credible seed case is 36 active sites, repeatable implementation in about two weeks, and the first sponsor or CRO referral motion working before the next raise.

Scenarios

Sensitivity

flowchart LR
  Leads --> PaidPilots
  PaidPilots --> ActiveSites
  ActiveSites --> Revenue
  Revenue --> GrossProfit
  GrossProfit --> Cash

Flags: Model counts customers as active revenue-generating research sites rather than parent practice platforms; logo count is only 6-8 by Y3. · The 75% gross-margin assumption is from the business plan target and may prove aggressive if pilots remain implementation-heavy. · Revenue concentration is high because 50 active sites likely sit inside a small number of specialty-platform customers. · Cash flow is modeled as EBITDA only; no debt, capex, or delayed collections are included.

• Integration drag. Specialty practices may have fragmented EHR setups that slow deployment and reduce time-to-value. Mitigation: Start with the most common specialty EHRs and a light-weight CSV or report-based onboarding path before full integration.
• Workflow proof gap. Buyers may doubt that pre-screening alerts will materially improve enrollment versus adding coordinator headcount. Mitigation: Sell against a single protocol with a measured pilot tied to screening throughput, first-screen timing, and coordinator hours saved.
• Incumbent squeeze. Existing trial-tech vendors or site networks could add similar patient-matching features once the wedge proves valuable. Mitigation: Focus on cardiometabolic specialty depth, fastest protocol configuration, and cross-site conversion benchmarks that generic vendors lack.