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Risk Adjustment Consulting: Learn how to create a complete view of patient conditions for Value Based Care

  • Optimize reimbursement to ensure patient care
  • Connect to all relevant data sources
  • Build AI models based on your specific data & population

Learn how in a free consultation with Dennis Joseph, our healthcare lead.

Increase the number of patients your team can review by 50X

Accurate 
RAF Score

Risk Assessment that is reflective of a comprehensive view of all chronic & acute patient conditions.

Automated Workflow

Personalized recommendations delivered by an automated AI/ML model assessing multiple datasets.

Optimized Reimbursement

Accurate RAF score leads to accurate reimbursement needed to deliver optimal care for the patient.

case study highlight

Custom Risk Adjustment Factor solution delivers reliable scoring model

Best in class organizations delivering value based care are required to manage patient risk levels. Inability to do so leads to inaccurate risk scores, declining reimbursements, and sub optimal patient care. A targeted engagement to shore up these key elements led this organization back to delivering value based care at the highest levels.

Impact

Higher recapture rates at ICD/HCC levels improved risk score accuracy, reducing hospitalizations. Clinicians gained holistic patient views, leading to integrated care planning. Accurate coding minimized errors, optimizing CMS reimbursement for ACO

Context

A large value-based care provider, serving 20K+ patients in 7 states, faced CMS reimbursement challenges due to coding gaps. Complex growth and tech hindered patient care, requiring better coordination among 200+ providers. Addressing gaps was vital for improved patient care and reimbursement.

Challenge

Ensure accurate RAF scores reflecting all conditions, prompt capture of chronic/episodic conditions, timely clinician assessments, and compliant, comprehensive data for precise reimbursement.

What we did

Evaluated Inputs

Team carefully assessed and determined the list of inputs that had to be evaluated:

  • Prior patient diagnosis history through Gehrimed and PCC
  • Prior patient assessments like HRA’s (Health Risk Assessments),
 MDS (Minimum Data Set), etc.
  • Medication records
  • Claims data going back multiple years
  • Lab records

Analyzed Inputs

Multiple models leveraging AI were developed to analyse the inputs:

  • Co-morbidity models across population and population subsets
  • Custom Machine Learning (ML) models to analyze source data
  • Generative AI model to recommend potential conditions for
 clinical feedback
  • Continuous learning model to absorb clinician feedback on AI’s
 recommendation
  • Peer reviewed CMS scoring model
  • Targeted accuracy for diagnosis was greater than 90%

Provided Targeted Recommendations

  • Custom UI was created to deliver recommendation within
 clinician workflow
  • Clinician feedback on dropped and probable diagnosis were
 ingested real time for continuous learning

Project deliverables

  • Detailed documentation of current and future state covering data,
 architecture, insights, reports, UI
  • All code for ML and AI models
  • Co-morbidity model
  • CMS risk scoring model updated with the latest CMS codes
  • Custom UI to feed AI recommendations and ingest clinician
 feedback
  • Integration into Gehrimed Workflow
Gehrimed
Point Click Care – Black
Acclivity Health
  • CMS-HSS Risk Adjust Models
  • ACO, SNP Population Health
  • RxNorm / RxClass
  • Claims Analytics
  • ICD-10 Coding

Schedule a free consult with our healthcare practice lead