AI-Enabled EDI Validations
First No SQL
Experience the future of Healthcare EDI with
AI-enabled auto scalable w Kubernetes
HiPaaS AI Enabled EDI
AI enabled EDI validation platform : with automated data correction and work queues. Simplified Trading Partner onboarding and configuration. Companion Guide setup for L1/L2 Validations. Configure validation and compare rules based on trading partner, line of business, state, or transaction types.
837, (I, P, D), 834, 835, 270/271, 276/277, 278, 274, 275, 820, 277CA
Claims Management : Claims validation, automation, claims status and remittance management . Manage automated claims corrections, rejections , claims status and claims payments
Real Time Transactions : Realtime Eligibility, PreAuth management, Claim status, Claims Attachments and Provider Directory
Enrollments Management : Manage inbound and outbound enrollments. Medicare, Medicaid enrollments management. Full file and Audit file compare, Compare with membership data in real time and manage updates
Availity Integration : Out of box integration with Availity Clearing house and QNXT for all transactions
HPS Enrollments Integration : Integrated with HPS enrollments for commercial and group plans to handle multiple coverages.
HiPaaS EDI Transactions
HiPaaS EDI Capabilities
Trading Partner Management : Simplified Trading Partner onboarding and configuration. Schedule file pickups and file hold for monthly and daily files. Auto scaling based on trading partner volumes. Trading partner self service setup
Companion Guide setup for L1/L2 Validations :Import or setup companion guide based on trading partner, line of business or transaction types
Configure EDI validation rules : Configure validation and compare rules based on trading partner, line of business, state, or transaction types. Define rules templates and import and export the rules. Ignore rules as warnings or errors. Add API's to rules to call enterprise validation services like member match, npi match and others. Configure transactions to hold based on rules
Auto Correct and auto learn rules and data : Setup auto data corrections and rules based on incoming data conditions .
Auto reconcile rules : Setup reconciliation setups and APIs based on transactions. Example - Claims payments reconciled to claims data. Compare Enrollments.to membership data api, Memebr match api
API driven architecture to integrate to QNXT , Facets, Healthedge and other systems REST JSON API, XML or other interfaces available out of box to integrate to your claims or enrollments systems.
Autoscale Real time transactions Auto scale real time transactions using services. Invoke QNXT, Facets, Healthedge API's for real time transactions data
Work-queues for manual corrections Out of box Standard and customized work-queues for the business user to view and update data.
Business Reporting Dashboards Simplified dashboard to check the validation status, trading partner performance, provider performance
Trading partners & Provider Self service Simplified dashboard to check the validation status, trading partner performance, provider performance
HiPaaS Getting Started
On Cloud :Start using in weeks with our HIPAA cloud option
On Premise : Deploy on your data center or choice of private cloud
Pay as you go : Pay monthly as you use it.
Why choose HiPaaS ?
Healthcare Neo4j GraphDB
Healthcare Interoperability Solved
Secure Healthcare API
Today’s healthcare insurance EDI analyst spend a significant amount of time performing routine manual reconciliation and corrections of EDI transactions. Additional complexities in the EDI processes related to validations and custom rules, makes large, unapplied EDIi transactions a common – and constant – pain point and risk area for healthcare organizations.
There is also pressure to covert batch to real time or in time payments of claims and additional data like attachments and preauth required.
The EDI system’s used are traditional COTs software and rules developed many years ago. This restricts the agility of making changes to react to market changes. Additionally enabling new line of business and or M&A makes it impossible to implement quickly. Due to COVID-19 there is surge of medical EDI transactions and memberships, this also requires auto scaling of EDI systems. The old EDI systems need large amount of hardware and software to achieve the scale.
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