ASHER health
Turnkey telehealth infrastructure, built from zero.
ASHER health gives wellness entrepreneurs — gym owners, med spa operators, chiropractors — compliant access to medical weight loss, peptides, supplements and lab work without requiring a clinical license.
Development 180 built the platform that makes it run.
Trevor BriggsCEOASHER health
Richard FisherCTOASHER health
MarkCTODevelopment 180
What they needed
Telehealth sounds simple until you look at what sits underneath it. A patient orders. A licensed provider has to review and approve. A prescription has to reach a real pharmacy with the right formulation, in a state where that pharmacy is permitted to ship. Lab work has to be ordered from a lab network, come back as structured results, and land in front of the right clinician in a form they can act on quickly. Every one of those handoffs is a different system with a different data model, and none of them were designed to talk to each other.
ASHER health needed all of it to behave as one product — for the patient, for the provider, and for the partner running the business.
What we built
- A provider workflow that respects clinical judgment.Prescribers see the orders assigned to them with the clinical context attached: intake responses, prior orders, and lab history. Approval and denial are first-class actions with reasons captured, because in healthcare the record of why matters as much as the decision.
- An admin review layer with enforced checkpoints.Before an order reaches a provider it passes an internal review queue. Certain conditions raise a soft stop that a reviewer has to acknowledge explicitly rather than click past. That is a deliberate piece of friction: the point is that nobody advances an order they haven’t actually read.
- Lab ordering and results, end to end.Orders go out to the lab network, results come back as structured biomarker data, and the platform maps them to standard codes, applies reference ranges, and flags what’s out of range. Results render as a report a clinician can read in seconds rather than a wall of numbers.
- AI-assisted lab interpretation.Results are processed automatically as they arrive, ahead of the clinician opening them, so the interpretation is ready rather than generated on demand. Because that processing takes a couple of minutes, the interface tells the clinician when a result is still being prepared instead of showing an empty state and letting them guess.
- Pharmacy routing.Prescriptions route to the pharmacy that can actually fill them, which depends on the formulation and on where the patient lives. Pharmacies that can’t serve a given state don’t appear as options, so the constraint is enforced by the system rather than remembered by staff.
- Partner operations and reporting.Partners get their own view of their business: patients, orders, activity, and exports that combine partner and order data so they can analyse what’s working.
- Permissions that match how these businesses actually run.Owners and employees have genuinely different capabilities, particularly around billing. An employee can run the day to day without being able to change how money moves.
- Releases that can be audited.Production changes go through a gated release rather than a push. A product owner approves, then an engineer approves, then the build runs and deploys — with an automated review pass before anyone signs off. Every step is recorded and announced. In healthcare that isn’t ceremony. It’s the difference between being able to answer “who changed this, when, and who approved it” and not being able to.
The part that’s harder than it looks
Most of the difficulty in this system isn’t in any single feature. It’s in the seams.
Third-party systems change without telling you. Lab data arrives incomplete in ways the documentation doesn’t mention. A pharmacy platform ships a release and a field that existed last week stops existing. Handling that badly means a clinician sees a blank screen or, much worse, a wrong number.
Reference ranges are the clearest example. Lab results don’t always arrive with structured minimum and maximum values — sometimes the only range is buried in a free-text note. Handled naively, that produces a chart where an abnormal value is still drawn inside the normal band. The platform parses the range out of the note when the structured fields are missing, and where no range can be established at all, it declines to draw one rather than drawing a misleading one. Showing nothing is safer than showing something wrong.
So the working assumption throughout is that upstream data is untrustworthy until proven otherwise. Missing fields degrade to a safe state rather than a plausible-looking one. Long-running work happens ahead of time rather than while someone waits. And when an integration does break, the failure is visible and attributable rather than silent.
That’s the difference between a demo and a system people rely on to do their jobs.
Where it is now
Live and in daily use by providers, admin staff, and partner businesses. A dedicated DevTeam6 pod works on it continuously — engineering, QC, and project management — shipping fixes and features week over week rather than in quarterly releases.
In their words
“We appreciate everyone on the team and the effort put in every single day. Without you guys, Asher Health would have stayed an idea and never come to life.”
Mark’s note on this build is on its way.
Mark · CTO of Development 180ASHER health came to us as an idea. It runs as a business.