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Denials Optimization

Denials Optimization

Denials Optimization

By Sift Healthcare

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Select which hospital or health system you work at and see a personalized match score.


Sift’s Payments Intelligence Platform powers data-driven recommendations and interventions that optimize full-cycle denials management.

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Key Clients
Clients Your Size
0 People
3 Organizations
EHR integration


Acute care EMR, Ambulatory EMR, Access +/or revenue cycle

EMR Integration & Relevant Hardware:

Recommended, but not required

EMRs Supported:

Allscripts, Cerner, CPSI, eClinicalWorks, Epic, Other

Hardware Compatibility:

Desktop, Other


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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Use cases


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.

COVID-19 use cases:

With the changing payer mix and documentation and coding challenges during a time when higher margin elective procedures have disappeared and patients are even less able to pay balances due, turning receivables into cash as efficiently as possible is more important than ever. Payer specific claim edits updated daily based on raw claims and denials data (837 and 835) mean higher first pass payment rates. For denials that persist, ensure your teams are laser focused on the denials with the highest value to the organization, the highest likelihood of overturn with significant payment.


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


Differentiators vs EHR Functionality:
  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.
Differentiators vs Competitors:

None provided

Diversity, equity, and inclusion practices

DEI is short for diversity, equity, and inclusion. Learn more about DEI practices.
Product Design

Data accessibility

Product Delivery


Company information

Founded in 2017

2.5M total equity funding

Select which hospital or health system you work at and see a personalized match score.

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