Revenue cycle · Denial prediction & payments intelligence
Sift Healthcare
Sift Healthcare makes software that predicts which hospital claims payers will deny or underpay and shows revenue cycle teams what to fix, with RevProtect flagging risk while care is documented.
Strong fit
- Hospitals and health systems with high clinical denial rates on inpatient stays
- Utilization review, clinical documentation, and coding teams that want to see payer risk before a claim goes out
- Revenue cycle leaders who want claims follow-up ranked by the chance of payment
Weak fit
- Physician practices that want a billing service
- Buyers who want AI agents to do claim follow-up for them
- Buyers who need a published price list
Bottom line
Sift Healthcare is worth using for health systems that want to stop clinical denials before claims go out. It predicts which hospital claims payers are likely to deny or underpay and tells revenue cycle teams what to fix. Groups focused on underpayment recovery and patient estimates may prefer MD Clarity.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Sift Healthcare makes software that predicts which hospital claims payers are likely to deny or underpay.
Justin Nicols, who had worked in investment banking and on the executive teams of advertising technology and e-commerce companies, founded it in Milwaukee, Wisconsin, in 2017.
Sift joins clinical, coding, authorization, and payment data and trains models on how each payer has paid in the past.
Its main product, RevProtect, shows utilization review, clinical documentation, and coding staff which cases are at risk of denial while the patient is still in the hospital, and why. RevCollect ranks unpaid claims by the chance of payment, RevEngage suggests payment plans for patients, and RevTrack tracks results.
Sift raised a $20 million Series B led by B Capital in May 2024. In February 2026 Hartford HealthCare, a Connecticut health system, signed on to use Sift to prevent denials earlier in care. Sift publishes an annual report on payer denial trends.
Compare Adonis when a health system wants denial analytics with AI agents that do the follow-up, and MD Clarity when underpayment recovery and patient estimates matter most.
Janus Health fits when claim status and payer portal work should be automated. Enter Health fits a group that wants AI across the billing cycle.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Adonis | 7.8 | 7.6 |
| Enter Health | 7.3 | 7.0 |
| SuperDial | 7.2 | 7.0 |
| Sift Healthcare | 7.1 | 6.8 |
| MD Clarity | 6.9 | 6.7 |
| Janus Health | 6.9 | 6.9 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom contract quoted by Sift Healthcare. |
| What usually drives cost | Claim volume, the number of hospitals, which products are used (RevProtect, RevCollect, RevEngage, RevTrack), and integration with the EHR. |
| What to ask before you buy | Whether the fee is fixed or tied to money recovered, the time to first results, and how savings are measured. |
| Published pricing | Sift Healthcare does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Sift Healthcare to function | |
| Revenue cycle leader who owns denials | Findings need an owner. |
| Claims and remittance data | Models learn from it. |
| Utilization review and clinical documentation teams | They act on the alerts. |
| EHR integration plan | Alerts show where staff work. |
| Baseline denial and write-off rates | You need them to show the change later. |
| What will maximize your value | |
| Start with the highest-denial payers | Biggest early gains. |
| Route alerts into existing worklists | Staff use them more. |
| Review results with payers | Patterns support contract talks. |
| Deal-breakers | |
| No access to claims data | |
| Wants a billing service | |
| No team to act on alerts | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and data connection | 1-3 months |
| 2 | First predictions live | 2-4 months |
| 3 | More products and hospitals | 6-12 months |
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.