Technology leaders in Healthcare industry are faced with a changing technology and regulatory landscape. At one end of the spectrum, the industry faces an increasingly tech-savvy population, a huge amount of data which needs to be processed using unconventional methods and the need to improve efficiency of healthcare delivery through effective use of technology. At the other end, there is the challenge of ensuring that systems match the updated and /or evolving federal requirements, meet the privacy constraints and switch to the diagnostic and procedural codes. It’s like walking a tight-rope with an extremely stiff timeline!

1. Welcome to the Webinar on
Healthcare IT: Role of EDI in
Affordable Care Act Reforms
By Harbinger Systems
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2. Panellists
Mandar Kulkarni
Maheshkumar Kharade
Marketing Manager
Harbinger Systems
Associate Architect
Harbinger Systems
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3. Agenda
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Challenges in Healthcare IT
Project Case 1
Project Case 2
Project Case 3
Summary
Q&A
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4. Challenges in Healthcare IT
Growing Data
Real time
Reporting and
Access of Data
Transition to New
Standards
Challenges in
Healthcare IT
Interoperable
Systems to Create a
Truly Enabling
Healthcare IT
Environment
Security of Data
Compliance
Management
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5. Case Study 1
• Business Problem Statement
– Startup ISV in healthcare domain wants to develop
a SaaS application for hospitals and medical
institutions
– ISV has acquired a company with huge database of
health related records stored using legacy database
with size more than 500 GB
– ISV user base is growing rapidly. They want to
quickly access user details from huge dataset and
also cope up with this growth leveraging a big data
technology stack
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6. Case Study 1 (Continued…)
• Key Challenges
– Quick Response time
– Being a SaaS based application, System stores data
for millions of patients
– Every records contains 500+ fields with complex
structure
– Need to support quick look up using any field from
patient record
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7. Case Study 1 (Continued…)
External
Systems
Data Files
HBase
Data Set
Data
Record
Message
Queue
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Solr Indexing
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8. Case Study 1 (Continued…)
• Highlights
– Distributed processing of patient data using
messaging queues
– Indexing process runs on separate indexing server
in the background
– Call to index respective data sets based on
inserts/updates
– Reference to data location is indexed, search is two
step i.e. find reference and then fetch actual record
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9. Case Study 2
• Business Problem Statement
– Streamline and automate the management, identification
and resolution of compliance exceptions within the CMS
regulated Medicare industry
• Challenges
– Manual process for understanding over few hundreds of
business rules for compliance management
– Develop a customizable and flexible model to facilitate
varied customer needs and frequently changing compliance
rules.
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10. Case Study 2 (Continued…)
Analytics
External
Systems
Data Files
Reports
DB
ETL
Batch
Process
Business
Rules
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Message
Queue
DRL(s)
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11. Case Study 2 (Continued…)
• Highlights
– Use of ETL tools and jobs for simplified and fast
implementation of import process for variety of data
sources
– Use of decision table for converting complex business rules
into easy to maintain excel decision table format
– Rule repository in the form of DRL files, to avoid runtime
compilation of rules
– Batch processing to operate on multiple records effectively
– Messaging Queue implementation to offload DB updates
and save data in Async call to improve performance
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12. Case Study 3
• Business Problem Statement
– In order to remain compliant, Medicare Advantage
organizations must have solutions and processes in
place to exchange adjudicated encounter data from
health plans and data from certified professional
coders with CMS.
– Plans must have the ability to receive, store,
submit and process all encounter data in a format
and via a protocol specified by CMS
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13. Case Study 3 (Continued…)
• Key Challenges
– The environment is characterized by often
imperfect source data due to elements such as
claims communications breakdowns, disorganized
provider tables and incomplete source data
originating from a chart review.
– Further, the 837 format is very complex and the
CMS requirements are far more extensive than
exist for the current RAPS(Risk Adjustment) system.
– This volume and complexity results in more
opportunity for errors, exceptions and rejects of
submitted diagnosis codes
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14. Case Study 3 (Continued…)
Worker 1
Claim Files
[837 - 5010]
Claim Files
[Custom/
Old
Formats]
P
R
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P
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S
S
O
R
Validation
Initiator
Message
Queue
Validation
Aggregator
Worker 2
Validations
- Syntax
- CMS Rules
- Situations [DT]
-Member,Provider
Worker n
DB
Message
Queue
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15. Case Study 3 (Continued…)
• Highlights
– Highly scalable architecture with an ability to add/remove
worker nodes at run time
– Step by step implementation for multiple validations,
flexibility to plug-in/out validations as per requirement
changes/customizations
– Ability to map data from older/custom formats into
standard specific schema by means of pre-processor
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16. Summary
• Big data solutions are way forward for handling
growing data size
• Frequently changing compliance rules can be
handled using technology like Decision Tables,
which enables defining rules outside code
• For complex, multi stepped and time
consuming processes, distributed and
asynchronous processing is a good strategy to
achieve desired system performance
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17. Q&A
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18. Thank You!
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