The R&D tax credit for healthtech startups
The short answer
Most healthtech software engineering qualifies for the R&D credit. The usual sticking point is separating technical development from clinical, regulatory, and compliance work, which does not qualify on its own.
What qualifies, and what fights you
Healthtech teams build software under real technical uncertainty. Interoperability engines, matching algorithms, and data pipelines all involve approaches that might not work when the team starts. That kind of engineering generally qualifies for the credit.
The sticking point is that healthtech teams also spend real time on activities that look like R&D but are not. Clinical trial design, medical necessity review, and compliance paperwork are part of running a healthtech company, but they are not technological experimentation. A defensible study separates the two.
HIPAA adds a specific wrinkle. Writing a business associate agreement or filling out a compliance checklist does not qualify. Building the access control system, audit logging, and encryption architecture that actually enforces HIPAA requirements does, because that is engineering work with real design uncertainty.
The four-part test, applied to healthtech startups
Apply the four-part test to a typical healthtech sprint. Qualified purpose: the team is improving patient intake, a diagnostic support tool, or an interoperability platform. Technological in nature: the work relies on distributed systems and data engineering, not medical judgment. Elimination of uncertainty: the team does not know upfront whether a patient matching algorithm will produce clinically usable results, or whether a pipeline can normalize data from a dozen different hospital systems into one schema.
Process of experimentation shows up in how the team gets there. Engineers try different entity resolution approaches, test FHIR mapping strategies against real-world messy data, and revise de-identification logic when it strips too much or too little. That iteration, documented in pull requests and tickets, is what a study points to.
New to the test itself? Read what software work qualifies as R&D first.
Work that usually qualifies
HL7 and FHIR interoperability engines
Building mapping and normalization pipelines that translate data from different EHR formats into a canonical FHIR schema, handling edge cases that vendor documentation does not cover.
De-identification and PHI pipelines
Designing algorithms that strip or tokenize protected health information while preserving enough structure for the data to remain useful for analytics or research.
Clinical matching and triage algorithms
Building and testing algorithms that match patients to providers or triage symptoms, where the right approach is not known in advance and has to be validated against real outcomes.
Real-time monitoring and alerting
Building the system that ingests streaming data from devices or wearables and flags clinically meaningful anomalies without generating excessive false alerts.
Work that usually does not
HIPAA paperwork and compliance checklists
Filling out business associate agreements or vendor security questionnaires is administrative work. There is no technological uncertainty to resolve.
Manual clinical review by licensed staff
A nurse or physician reviewing a case or coding a chart is applying professional medical judgment, not doing software engineering.
Which expenses count
W-2 wages for engineers and data scientists who build and test the qualifying systems count as QRE, along with wages for technical leads who supervise that work directly, such as reviewing architecture for an interoperability pipeline.
US-based contractors doing qualifying engineering work count at 65 percent of what you pay them. Healthtech startups often bring in contract engineers for specialized interoperability or data pipeline work, and that spend is usually eligible.
Cloud infrastructure used to build and test these systems counts too, including development, staging, and synthetic-data test environments kept separate from production PHI. Compute spent training or validating a matching algorithm is a supply cost, not overhead.
A worked example
Hypothetical example. A healthtech startup has 5 engineers building an interoperability and de-identification platform, plus one contractor, over a year.
At roughly 6 to 10 percent of total QRE, the federal credit lands around $45,000 to $75,000. If the company has under $5 million in revenue, it can apply up to $500,000 of that credit against payroll taxes each year instead of waiting to owe income tax.