Prior authorization & UM · AI utilization management
Xsolis
Xsolis is an AI utilization management platform for hospitals and health insurers. Its Dragonfly software gives every hospital patient a medical necessity score that updates in real time, which helps utilization review teams decide patient status with less manual review.
Strong fit
- Health systems and payers that want AI help with utilization review and patient status decisions
- Teams that track avoidable hospital days and review workload during stays
- Groups willing to connect clinical data so the AI can help with decisions
Weak fit
- Groups that only need a fax portal for occasional prior authorization forms
- Buyers who do not want to change how review nurses work
- Groups that cannot share the clinical data the AI needs
Bottom line
Xsolis is a strong option for health systems and payers that want AI help with patient status decisions and a shared view between hospitals and insurers. It works best for teams that will track avoidable days and retrain review staff. Specialty practices that need prior authorization help may prefer Silna.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Xsolis sells to health systems and insurers that run utilization review, the process of checking whether a hospital stay or level of care is medically necessary. Its Dragonfly platform reads each hospital patient's clinical data, keeps a running medical necessity score, and estimates the level of care the patient is likely to need. Because Xsolis sells to both hospitals and insurers, the two sides can work on status decisions from the same information. Its contracts are aimed at large and mid-sized buyers.
Results are strongest when review leaders set measures such as avoidable days and review workload up front and train reviewers to both trust and question the AI's output. Projects stall when the software is installed and review habits stay the same. Among the prior authorization and utilization tools we review, Xsolis scores 7.8 overall, second to Anterior (8.1) and ahead of Infinitus and Humata Health (both 7.7) and Agadia (7.6). Silna Health suits specialty providers that need prior authorization and benefits work handled together.
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 contracts for large and mid-sized buyers of utilization review and patient status tools. |
| What usually drives cost | Number of facilities or covered members, modules, and how much EHR and payer system integration is needed. |
| What to ask before you buy | First-year cost at your volume, which data feeds are required, and targets for avoidable days. |
| Published pricing | Quoted for each customer. Prices are not listed on the website. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Xsolis to function | |
| Clinical data access for the AI | The AI needs clinical data feeds to suggest a patient status. |
| Review leaders willing to change how reviews are done | Avoidable days fall only when reviewers change how they work. |
| Agreements to work with payers or providers where needed | The shared network pays off only when both hospitals and insurers take part. |
| Targets such as avoidable days or review turnaround | Clear targets show whether the program is working. |
| IT capacity for EHR and analytics integration | Connecting EHR and analytics data is a large part of the project. |
| What will maximize your value | |
| Pilot on the busiest service lines first | Starting with a few service lines makes it easier to see what is working. |
| Track when nurses override the AI to tune how much they trust it | Override rates show whether nurses trust the AI too much or too little. |
| Share outcome dashboards with finance | Finance leaders should see the change in avoidable days. |
| Update review criteria when policies change | Outdated criteria bring back manual work. |
| Budget for change management as well as licenses | Getting reviewers to change their routine is where these programs usually stall. |
| Deal-breakers | |
| You only need occasional faxed prior authorization forms. | |
| You cannot provide clinical data access. | |
| Review leaders will not change habits for reviews during the stay. | |
| No one owns the avoidable day or turnaround numbers. | |
| IT cannot integrate for the next two quarters. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | From signing to kickoff | 4-8 weeks for security review, data mapping, and choosing pilot units |
| 2 | From kickoff to first use | 3-6 months for a meaningful utilization review pilot |
| 3 | From first use to steady results | 4-8 months of adjusting before expanding to more facilities |
Leadership
- Lee RivasChief Executive Officer
Joan ButtersCo-founder
Recent news
- October 2026: Xsolis appointed Lee Rivas chief executive, succeeding co-founder Joan Butters, who had led the company since 2013. Rivas previously ran Cloudmed, a revenue cycle technology company, through its sale to R1 RCM, and then led R1 as CEO through its take-private deal.
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.