Prior authorization & UM · Health plan prior auth AI
Banjo Health
Banjo Health provides AI-assisted prior authorization, appeals, and clinical criteria automation software for health plans and PBMs. It is plan-side UM software, not a provider EHR.
Strong fit
- Health plans and pharmacy benefit managers that want AI to speed up prior authorizations, appeals, and clinical criteria work
- Utilization management leaders who would rather automate criteria without coding than hire more outside nurse reviewers
- Buyers that want to manage prior authorization cases with decision trees they build themselves
Weak fit
- Provider groups that only want a tool for submitting prior authorizations through portals
- Plans that have committed to one existing utilization management suite and have no budget for another AI tool
- Buyers that need visibility into specialty lab prior authorizations rather than decision tools for plans
Bottom line
Banjo Health is worth using for health plans and pharmacy benefit managers that want AI help with prior authorizations, appeals, and grievances. It suits plans ready to code their clinical criteria into the system. Plans that want mature AI prediction or a long-established suite may prefer Xsolis or Agadia.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Banjo Health is a prior authorization software company in Washington, DC, founded by CEO Saar Mahna. Health plans and pharmacy benefit managers use BanjoPA and related modules to run authorization cases and appeals.
A composer tool lets them turn clinical criteria into decision trees without writing code, so they rely less on adding more nurse reviewers. It is software for health plans, not a bot for provider portals or a full claims system.
The company is earlier in its growth than Agadia or Xsolis, with fewer than 50 employees after early venture rounds. Implementation is a utilization management project in which the plan codes its criteria into the system, and pricing follows a standard enterprise software model.
Among prior authorization tools we review, Banjo Health scores below Xsolis on mature AI prediction and above narrower tools when a plan wants authorization workflow plus criteria automation. The score reflects focused automation for plans, with less commercial scale than the largest vendors.
Other tools fit different needs. Agadia suits a plan or pharmacy benefit manager that needs the mature PAHub suite, and Xsolis fits when AI prediction of utilization is the main purchase. Careviso fits providers whose problem is prior authorization for lab tests and specialty care.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Anterior | 8.1 | 8.1 |
| Xsolis | 7.8 | 7.3 |
| Infinitus | 7.7 | 7.4 |
| Humata Health | 7.7 | 7.4 |
| Agadia | 7.6 | 7.2 |
| Careviso | 7.6 | 7.3 |
| Silna Health | 7.3 | 7.1 |
| Itiliti Health | 7.3 | 7.1 |
| Banjo Health | 7.2 | 7.0 |
| Hindsait | 7.0 | 6.7 |
| Rhyme | 6.5 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Software for prior authorizations, appeals, and grievances, with prices quoted for each plan or pharmacy benefit manager. |
| What usually drives cost | Authorization volume, which modules you buy (prior authorization, appeals, composer), and how many systems it must connect to. |
| What to ask before you buy | The yearly fee at your prior authorization volume, including moving your criteria over and training. |
| Published pricing | Banjo Health does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Banjo Health to function | |
| People who own your clinical review criteria | The composer stalls if no one owns the criteria. |
| Current prior authorization volume by specialty | Without volume numbers, you cannot estimate the return. |
| A plan to connect to your utilization management systems | Cases need one official place to live. |
| A map of how appeals and grievances work today | The modules fail if the process is unclear. |
| A compliance review of the AI features | AI at a health plan needs policy approval. |
| What will maximize your value | |
| Start with your busiest specialty | Starting every specialty at once multiplies the problem cases. |
| Measure turnaround and how often decisions are upheld | This shows the value for utilization management. |
| Write rules early for providers exempt from review (gold-carding) | One-off exceptions break automation. |
| Deal-breakers | |
| You only want a provider portal tool for prior authorization | |
| You have no utilization management staff to run it | |
| You need to replace your core claims system | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Workshops on criteria and processes | 4-8 weeks |
| 2 | Pilot in one specialty | 6-12 weeks |
| 3 | Expand to more specialties | 8-16 weeks |
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.