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Use cases
EHR integrations
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Jump to:
Categories
Solutions
Description
Compatibility Level
Clients
Use cases
EHR integrations
Client types
Differentiators
Keywords
Media
Company details
HCC Accuracy
HCC Accuracy

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Categories

Solutions

Description

Product Description:
The VDT Affinitē Risk Adjustment module includes: • Multi-user access to the stand-alone Risk Adjustment Module of the Affinitē platform for CMS HCC Risk Adjustment. With Affinitē Risk, HEALTH PLAN NAME will receive the following: o Implementation o Data Integration and Analytics, Warehousing and Daily Processing o Dashboard high-level insights including the status of HCC capture, the performance of providers and provider groups, and HCC opportunities o Reporting o Estimated RAF scores at the member, provider, and provider group level based on claims data o Estimated member level RAF score build-up incorporating demographics, acute and chronic HCCs, disease interactions, and HCC payment count o Identification of HCCs obtained from claims data and chart review o Visibility into detailed claim data, highlighting HCCs that impact the RAF score o Visibility into detailed pharmacy data including refill status and ordering provider o Comparison of estimated RAF scores for current and prior year(s) o Identification of Missing HCCs (year-over-year gaps) for all open years at the member level o Member level identification of possible suspect HCCs based on claims and supplemental data for all open years. o Preparation of RAPS claim submission files in CMS required format o Preparation of RAPS supplemental submission files from chart review in CMS required format o Ability to monitor providers and provider group related to HCC capture, HCC gaps and average RAF score o Train-the-trainer training and support on the VDT Affinitē solution
About Vital Data Technology, LLC:

Vital Data Technology empowers health plans to continuously improve outcomes, accelerate compliance and control costs. Vital Data’s Affinitē™ platform unifies formerly siloed data and leverages artificial intelligence and machine learning to drive actionable interventions. The platform’s modules include: Affinitē™ PlanLink™ for real-time CareFlow™ automation and case management for stakeholders throughout the care continuum; Affinitē™ ProviderLink™ for prioritized, actionable patient interventions; MyVitalData™ for mobile app member engagement; Affinitē™ Quality for optimization of quality management and HEDIS® performance, and Affinitē™ Risk for Hierarchical Condition Category (HCC) risk adjustment.

Vital Data Technology is trusted by health plans nationwide across Medicare Advantage, Medicare, Medicaid, commercial and self-funded adult and pediatric populations and has received NCQA HEDIS® Certification for five consecutive years. For more information visit: www.VitalDataTech.com or call (866) 482-8399.

Product Description:

PulseData's ML/AI solution identifies undiagnosed and under-diagnosed patients at risk for chronic conditions and significantly improves HCC accuracy.

About PulseDataAI:

PulseData is advancing healthcare with data-driven actionable clinical insights giving payers, providers and other value-based care organizations the power to improve clinical and financial outcomes, close care gaps, and improve HEDIS and CAHPS scores. PulseData leverages large, diverse datasets and deep clinical expertise to effectively identify patients with chronic conditions. Using cutting-edge machine learning and AI models, PulseData enables early intervention, equipping healthcare teams with informed, data-driven insights precisely when they matter most – at the point of care.

PulseData’s thoughtful model development avoids machine learning biases, prioritizes model explainability and ensures optimal model performance. Our tailored collaborative approach with our valued clients creates meaningful, impactful, and relevant workflows. The resulting actionable insights can be viewed on our platform and seamlessly integrated into clinical workflows to enhance and support care management workflows.

At the nexus of technology and healthcare, PulseData empowers providers to make well-informed decisions, which improve patient outcomes, reduce costs, and improve program performance. Learn More at pulsedata.ai.

Compatibility level

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Clients

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Use Cases

Description:

None provided

Pediatric use cases:

None provided

Users:

None provided

Description:

Improve HCC Accuracy

Pediatric use cases:

None provided

Users:

PulseData's ML/AI solution identifies undiagnosed and under-diagnosed patients at risk for chronic conditions and significantly improves HCC accuracy.

EHR Integrations

Integrations:

None provided

EMR Integration & Relevant Hardware:

None provided

EMRs Supported:

None provided

Hardware Compatibility:

None provided

Integrations:

Acute care EMR, Ambulatory EMR, Ancillary EMR, ERP system, Patient portal, Pop health platform, Home health, Behavioral health, Community based organizations, ADT, Access +/or revenue cycle, Credentialing, Website / public online sources, Other

EMR Integration & Relevant Hardware:

Recommended, but not required

EMRs Supported:

Epic, Cerner, Meditech, Allscripts, NextGen, athena, GE, eClinicalWorks, McKesson, Other, Allscripts/Eclipsys, Athenahealth, Azalea Health/Prognosis, CPSI, Evident, Healthland, MEDHOST, MedWorx, QuadraMed, Self-developed, Would prefer not to disclose, Point Click Care

Hardware Compatibility:

Desktop, Mobile / Tablet (web optimized), Mobile / Tablet (native app), Other

Client Types

None provided

Differentiators

Differentiators vs EHR Functionality:

None provided

Differentiators vs Competitors:

None provided

Differentiators vs EHR Functionality:

PulseData's ML/AI solution identifies undiagnosed and under-diagnosed patients at risk for chronic conditions and significantly improves HCC accuracy.

Differentiators vs Competitors:

PulseData's ML/AI solution identifies undiagnosed and under-diagnosed patients at risk for chronic conditions and significantly improves HCC accuracy.

Keywords

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Downloads

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PulseData Overview 2023.pdf

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Company Details

Founded in 2016

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