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Rev/Collect - ML-Driven Denials Workflow Optimization

Rev/Collect - ML-Driven Denials Workflow Optimization

Rev/Collect - ML-Driven Denials Workflow Optimization

3 verified clients
Rev/Collect - ML-Driven Denials Workflow Optimization
Rev/Collect - ML-Driven Denials Workflow Optimization


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Rev/Collect - ML-Driven Denials Workflow Optimization is a solution provided by Sift Healthcare which was founded in 2017. It belongs to the digital health solution Revenue Cycle Management.
It has 3 verified clients.
Rev/Collect - ML-Driven Denials Workflow Optimization integrates with major EMRs such as Allscripts, Cerner, and CPSI.
Some other resource(s) that may be helpful in learning about Rev/Collect - ML-Driven Denials Workflow Optimization include: Q&A with Dominic Foscato of Sift: A data-driven approach to financial engagement and Top Patient Billing and Payment Companies Report | 2023

Payers constantly change the rules. Sift evens the playing field. Sift equips healthcare organizations to fully leverage their payments data to work smarter, protect their margins and accelerate cash.

Actionable Denials Intelligence, delivering a longitudinal view of clinical, coding, claims and remittance data. Sift establishes a data foundation that gives providers unprecedented access to their payments data and intelligence tools to better manage their denials, identify root causes and prevent future denials. 

  • Unified, normalized and organized claims and remittance data.
  • Delivering an accessible and complete picture of claim behavior, payer trends and the drivers of denials.
  • Curated, consultative analysis pinpointing where your team can take action to prevent denials and optimize workflows.

Denials Prioritization & Intelligent Automation to better manage touches and lower the cost of delivering each dollar of cash.

  • Sift’s machine learning optimizes workflows by prioritizing your team’s denial work efforts around ROI and by delivering Smart Claim Edits that improve first-pass yield. 
  • Active-Learning Claim Scrubber analyzes daily claims and remittances to curate high-impact claim edit recommendations.
  • Machine learning models that score denials at an atomic claim level, using over 500 attributes to determine each denial’s likelihood to overturn.
  • ROI-based denials worklists seamlessly integrate into your EMR, prioritizing high-recovery denials in staff workqueues.
  • Scoring that enhances existing automation capabilities, enabling the strategic automation of low-yield accounts while avoiding over-automating recoverable accounts.

Denials Prevention. By unifying clinical, coding and payments data, Sift's ML predicts denials before claims are created and provide recommendations for upstream interventions. 

Sift’s ML models predict the likelihood of denial and provide pointers for intervention and prioritized user analysis, working to optimize payment outcomes.

  • Machine learning models score encounters around their likelihood of being denied, proactively flagging encounters for intervention before claim submission.
  • Denial category prediction and root causes pointers enable routing to the appropriate mid-cycle workflow for mitigation.
  • Mid-Cycle Denials Intelligence that ties back-end billing, denial and overturn patterns to upstream workflow data inputs to deliver root cause analysis and prevention recommendations.

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EHR integration

Acute care EMR, Ambulatory EMR, Access +/or revenue cycle
Recommended, but not required
Allscripts, Cerner, CPSI, eClinicalWorks, Epic, Other
Desktop, Other
Use cases and differentiators

Sift’s ML scores efficiently identify opportunities to prioritize staff touches on high-complexity accounts and leverage automation on low-complexity accounts, enabling providers to better facilitate AR management and accelerate cash.

VP of Revenue Cycle, Revenue Cycle Director, Denials Team, Revenue Cycle Teams

  1. ML to predict propensity-to-overturn, enabling dynamic denial prioritization -- rather than rules-based systems (i.e. EMR offerings).
  2. ML to predict propensity-to-deny, enabling denial prevention before claims are created. 
  3. Rev/Track Insights Reports with curated intelligence and action items.
  4. Unified and accessible data foundation, including:
  • Matched claims and remittance data
  • Unified and normalized payments data
  • Payments data matched to corresponding CDI and coding data.
None provided
Company information

Founded in 2017

2.5M total equity funding



Sift Healthcare Fully Leveraging Payments Data To Prevent Denials.png
Sift Healthcare Enabling Intelligent Denials Automation.png
Sift Healthcare ROI-Based Denials Prioritization.png
RevCollect unifies healthcare payments data and prioritizes AR follow-up

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