Patient engagement · Patient access voice AI
Assort Health
Assort Health makes AI voice agents for specialty practices that answer patient calls, book appointments using each practice's scheduling rules, and route complex calls to staff.
Strong fit
- Orthopedic and other specialty groups whose phone scheduling depends on strict provider rules
- Multi-site practices that want AI agents to book, triage, and route calls inside the EHR
- Groups ready to hand most routine calls to an AI agent and track how many it resolves
Weak fit
- Practices that mainly want call analytics and staff coaching
- Small offices with one front desk and low call volume
- Buyers who need a published price list
Bottom line
Assort Health builds AI voice agents that answer and place patient calls for specialty practices, starting with orthopedics. Each deployment loads the practice's own scheduling rules, so its agent books the right visit type with the right provider and hands harder calls to staff. It fits specialty groups with heavy call volume and complex scheduling rules.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Jeffery Liu and Jon Wang founded Assort Health in San Francisco and run it as co-CEOs. Liu was head of product engineering at Athelas and Commure, where he helped lead an AI scribe used by more than 100,000 physicians. Wang published seven papers on healthcare AI at Stanford and was an MD candidate at the University of California, San Francisco.
Assort started in orthopedics, where surgeons often see only certain conditions, so its agent has to learn which provider takes which complaint. Its first deployment, with a group in Chicago in late 2023, resolved about 55 percent of calls without a person on day one. That rate later rose above 95 percent.
Assort has raised more than $222 million. Its $120 million Series C, led by Menlo Ventures in June 2026, valued it at $1.2 billion, less than a year after a Series B led by Lightspeed Venture Partners. Paul Ricci, the founding CEO of Nuance, joined as an advisor. Its staff grew from about 15 to nearly 250 in a year.
Assort's own AI model, Synapse, learns specialty scheduling patterns from every deployment. It then creates the unusual cases and tests each new practice needs, so a new customer's provider rules do not start from scratch. At Barrington Orthopedic, unanswered calls fell 34 percent, and at Peninsula Orthopaedic, callers hanging up before reaching anyone fell 75 percent.
Other tools fit different needs. Hyro suits a health system that wants AI agents across its call center, website, and text messages. Keona Health suits organizations where nurse triage protocols and a call center CRM matter most, and PatientPrism suits those whose goal is coaching staff from call analytics.
For smaller groups, Hello Patient suits an outpatient group that wants the agents run as a managed service, and Parakeet Health suits a dermatology group that wants to pay based on results.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Assort Health | 8.2 | 7.6 |
| Hyro | 7.9 | 6.4 |
| PatientPrism | 7.7 | 7.1 |
| Keona Health | 7.4 | 7.7 |
| Infermedica | 7.4 | 7.1 |
| Hello Patient | 7.2 | 8.4 |
| Parakeet Health | 6.9 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom contract quoted by Assort Health. |
| What usually drives cost | Call volume, the number of specialties and locations, and which workflows the agents handle, such as scheduling, intake, referrals, refills, and payments. |
| What to ask before you buy | The price per call or per resolved call, what happens to cost when calls go to staff, and what setup and EHR connection cost. |
| Published pricing | Assort Health does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Assort Health to function | |
| Written scheduling rules for every provider | The agent can only follow rules it is given. |
| EHR or practice management access | Booking happens inside the scheduling system. |
| Phone routing plan | Decide which calls go to the agent first. |
| Owner for escalated calls | Staff still take what the agent hands off. |
| Current call numbers | Hold times and hang-ups let you measure the change later. |
| What will maximize your value | |
| Start with one specialty or region | Rules are easier to tune in one place. |
| Review resolution rates every week | At early customers, the share of calls the agent resolved rose over time. |
| Add outbound calls after inbound works | Refills and recalls use the same rules. |
| Deal-breakers | |
| No one can document scheduling rules | |
| The agent cannot get access to EHR scheduling | |
| You only want call analytics | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract, security review, and EHR connection | 2-6 weeks |
| 2 | First calls live | 1-2 months |
| 3 | More specialties and outbound calls | 3-6 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.