Prior authorization & UM · Specialty medication access AI
Latent
Latent makes AI software for health system pharmacies that reads the patient chart and payer rules to complete eligibility reviews, prior authorizations, and appeals for specialty medications.
Strong fit
- Health systems with specialty, retail, or infusion pharmacies that handle many prior authorizations
- Pharmacy teams managing high-demand drugs such as GLP-1 medications and immunology therapies
- Systems that want eligibility reviews, authorizations, and appeals handled by the same engine
Weak fit
- Independent practices without a health system pharmacy
- Buyers who need help mainly with imaging or procedure authorizations
- Buyers who need a published price list
Bottom line
Latent is worth using for large health systems that want specialty drug approvals to move faster without adding pharmacy technicians. Its AI works inside the EHR, under pharmacy team oversight, to prepare eligibility reviews, prior authorizations, and appeals. Practices that want authorizations started from the prescription may prefer Develop Health.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Latent, based in San Francisco, makes AI software for health system pharmacies. Sri Somasundaram, a machine learning researcher who worked on the founding team of the AI company Hebbia, founded it in 2022 with Rishabh Jain, a Cornell-trained computer engineer and his best friend since first grade.
Its agent sits in the EHR and reads notes, medications, and lab results. It checks them against payer policies, claims, and drug labels to work out what each medication request needs.
It prepares or completes eligibility reviews, prior authorizations, and appeals for specialty drugs, including GLP-1 medications and therapies in immunology, rheumatology, and cardiology. Each health system's clinical pharmacy staff oversee the work. It also flags patients who qualify for a therapy and helps keep them on it.
Adoption has grown quickly. Spark Capital and Transformation Capital co-led an $80 million Series A in March 2026, with Conviction, McKesson Ventures, General Catalyst, and Y Combinator taking part.
Latent grew from 4 to more than 45 health systems in a year and was live in 60 by September 2026. They include Cleveland Clinic, Mayo Clinic, Mount Sinai Health System, Yale New Haven Health, UCSF Health, and WVU Medicine. It expects to process more than 5.8 million medication cases over the following 12 months.
When a drug coverage program made several million members eligible overnight, Latent set up agents in seven to 10 days to check eligibility. That helped hospitals meet a 20 to 30 percent rise in demand.
At WVU Medicine, the specialty pharmacy saw faster turnaround within the first month and kept handling more cases over the next five months.
Develop Health fits a practice or health system that wants benefits checks and medication authorizations started from the prescription. SamaCare fits when the drugs fall under the medical benefit.
Valer fits when authorizations and referrals for many services run through one queue. Linear Health fits staff who want a desktop AI assistant for specialty authorizations.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Latent | 7.9 | 7.4 |
| SamaCare | 7.1 | 7.0 |
| Develop Health | 7.0 | 6.8 |
| Valer | 6.6 | 6.4 |
| Linear Health | 6.5 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | A custom contract quoted by Latent. |
| What usually drives cost | How many medication cases you have, which therapy areas and pharmacies are covered, whether appeals and patient outreach are included, and EHR connection. |
| What to ask before you buy | Whether you pay per case or a platform fee, what share of cases it completes with no staff work, and who submits requests to payers. |
| Published pricing | Latent does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Latent to function | |
| A pharmacy leader who owns getting patients their drugs | This person sets priorities and reviews results. |
| EHR access to notes, medications, and labs | The agent reads the chart. |
| Access to payer portals and the pharmacy system | Submissions and status checks run through them. |
| Starting turnaround and approval rates | You need them to show the change later. |
| Pharmacists to oversee the agent's work | Clinical judgment stays with staff. |
| What will maximize your value | |
| Start with one high-volume therapy area | It has clear criteria and many cases. |
| Add appeals once authorizations run smoothly | This recovers denied cases. |
| Track time to therapy by drug | This shows where patients wait. |
| Deal-breakers | |
| You have no health system pharmacy | |
| There is no way to connect to your EHR | |
| No one in pharmacy owns the work | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and EHR connection | 1-3 months |
| 2 | First therapy areas running | 1-2 months |
| 3 | More pharmacies and drug classes | 6-12 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.