lecture 6

• Data Integration

  • sharing of data by combining from various sources
  • data is exchanged or transferred between systems
  • source is the system of record or a reliable source
• Electronic Data Interchange

  • standards-based data exchange, quality of transaction
  • system-to-system exchange of highly structured data
  • HIPAA mandates EDI standards
  • electronic commerce - multiple ways to communicate unstructured data
• Data Integration Plan

  • take advantage of specialized applications
  • reduce data complexity
  • increase the value of data through unified systems
  • make data available
  • easy data collaboration
  • data integrity
  • understand data
  • make data live
• Data Cleansing

  • use of software to improve the quality of data
  • scrubbing- remove inconsistencies
• Data Mining

  • Looks for hidden patterns or relationships
  • academic exercise in healthcare
• Steps in Integrating

  • requirements gathering- understanding app need
  • defining data sources
  • finding data owners
  • understanding technical requirements of source application
  • developing process for data extraction, transformation, and transmission testing
• How is it Integrated

  • Flat file- data sent in a file with all various fields
  • Calling API
  • Webservices- synchronous (immediate response received), asynchronous (delayed response)
• Large Data Import/Indexes

  • more data in the table slows unsorted record import
  • Index speeds up retrieval
  • need additional writes and storage space to maintain index data structure
  • indexes quickly locate data
  • indexes can be created using one or more columns, providing rapid random lookups and efficient access of ordered records
• Interfaces Benefits

  • reduces # of exports and import endpoints
  • reuse data
  • easier method to interface a new or replaced app
  • ability to monitor system at one time
  • ability to notify using visual display and email
• Unidirectional Interface

  • single direction
  • limits the type of data stream that can be processed
  • greater security
• Bidirectional Interface

  • Two way direction
  • allows real-time transactions and updates
• ADT Transaction Types

  1. Patient admit
  2. patient transfer
  3. patient discharge
  4. patient registration
• clearing houses

  • companies that forward claims info from healthcare providers to insurance payers
  • each state has regulations; each carrier has its internal software infrastructure
  • claim scrubbing - checking claims for errors
  • sends claims to individual payers
  • status message sent back to the payer
• benefits of clearing houses

  • fix claims in minutes
  • fewer denied claims
  • faster reimbursement
  • sends claims at once in a single batch
  • real-time support
  • single location to manage all electronic claims
  • stops individual connection to payers for submission
• Electronic Remittance advice files (835)

  • notice of payments and adjustments sent to providers, billers, suppliers
  • faster account reconciliation
  • reduces costs
  • more detailed than paper
  • reduces denials
• Healthcare Claim File (837)

  • sent directly to payers or clearinghouses
  • allows coordination of benefits info
  • allows claims to be filed faster than paper
  • quicker resubmission of claims after denials
  • secure electronic submission
  • can be done by remote staff
• INSURANCE ELIGIBILITY REQUEST (270)

  • Transmit Health Care Eligibility Benefit Inquiries associatedwith a subscriber or dependent
  • sent from providers, insurers, clearinghouses
  • broken down into inquiry types (general, categorical, and specific-specific type of service)
• Insurance eligibility response (271)

  • response from healthcare Eligibility Benefit inquiry
  • General responses (Max benefits, deductible, copays, status)
  • Categorical responses (procedure coverage dates, max amount, co-insurance amounts, remaining deductibles)
• Data Flow: Interface Engine

  • goes through simple engine
  • proactive monitoring on a single screen
  • max control and flexibility with data
  • can copy stream to another app
• Data Flow: Point to Point

  • data stream individually built
  • cost effective
  • multiple vendors/apps involved
  • sending and receiving apps must be analyzed
  • less hardware/software needed