Prime Dimensions' future state event-driven architecture (EDA) effectively integrates relational, non-relational and stream data structures to create a unified analytics environment. EDA describes an architecture where layers are partitioned and decoupled from each other to allow better or easier composition, asynchronous performance characteristics, loose coupling of APIs and dependencies and easier profiling and monitoring. Its goal is a highly reliable design that allows different parts of the system to fail independently and still allow eventual consistency. EDA also enables discovery, or exploratory, analytics, which rely on low-latency continuous batch processing techniques and high frequency querying on large, dynamic datasets. This type of architecture requires a different class of tools and system interfaces to promote a looser coupling of applications to streamline data access, integration, exploration, and analysis. It is also designed to deploy real-time Web applications using NoSQL databases, RESTful interfaces and advanced platforms that maximize throughput and efficiency by providing evented, asynchronous I/O and guaranteed, non-blocking libraries, thereby sharing code between the browser and server, effectively eliminating the Web server layer.

1. IDENTITYANDACCESSMGMT.INFORMATIONASSURANCEHIPAA/HITECHCOMPLIANCE
Solution Architecture for Operating a Data-driven,
Event-enabled Healthcare Organization
Hadoop
HDFS
YARN
Hive
MapReduce
Clinical Data Warehouse
DATA
PROFILING
METADATA
MANAGEMENT
INDEX
CATALOG
TAXONOMY
MASTER DATA
MANAGEMENT
DATA
QUALITY
System Interfaces and APIs
REST SOAP
Enterprise Service BusLOG FILES
PROTOCOL
CONVERSION
MESSAGING
QUEUING BUFFERING
SEQUENCINGEVENT HANDLING
SourceSystems
Data
Transformation
Prime Dimensions Proprietary
DataAccess
Data Extraction
Services
Data ServicesDATA
CLEANINGDataIntegration
Clinical and LOB
Applications
PresentationLayer
Reports, Dashboards
and Visualizations
IN-MEMORY
DATABASE
Scale-up
NOSQL
DATABASE
Scale-out
Analytic ApplicationsPREDICTIVE
MODELING
ADVANCED
ANALYTICSOPTIMIZATIONFORECASTING
AnalyticsEngine
Enterprise Application Integration
PHM
HIE
CDSS
HRA CCP
Analytics Process APIs
DataDistribution
Services
TargetSystems
Patient ActivationClinical Informatics
Connected Wellness
Patient Reported
Outcomes
HL7
INTEGRATION
ENGINE
Analytic Offload
HIE – Health Information Exchange
PHM – Population Health Management
CCP – Care Coordination Portal
HRA – Health Risk Assessment
CDSS– Clinical Decision Support System
OLAP
Real-timeDataStreams
PRO – Patient Reported Outcomes
EHR – Electronic Health Records
CAS – Cost Accounting Systems
CPS – Claims Processing Systems
DM/CM – Disease Management/
Case Management
EHR CAS DM/CMCPS PRO
MULTI-DIMENSIONAL
MODELS
Data Warehouse
Augmentation
DEVICE AND
SENSOR DATA
Real-timeDataStreams
KEY FEATURES
• Promotes operational effectiveness, process
automation and analytic excellence
• Enables advanced analytics and clinical informatics
through an interoperable and scalable infrastructure
• Streamlines technology insertion based on agile
development methodology for rapid deployments
• Balances sustainment, incremental advances, and
transformational technologies
• Controls IT operational costs by eliminating
redundancies and aligning capabilities
• Supports strategic planning, organizational alignment,
and continuous process improvement
• Provides a practical framework for defining and
realizing the evolving future state
• Integrates multi-structured and stream data using
advanced technologies that provide high velocity data
capture, discovery and analysis
• Establishes a virtualized data environment and
extensible service-oriented architecture that supports
both Restful and SOAP APIs, allowing multiple data
structures and formats (JSON, XML, etc.)
• Provides an application development platform with
domain-specific enclaves for evolving from “systems of
record” to “systems of engagement”
KnowledgeManagementInformationManagementContentManagement
PrivacyandSecurity(Robustintegratedsecuritylayer)
BENEFITS
 Improved quality of care and
patient outcomes
 Reduced life-cycle costs
 Minimized financial and
operational risk
 Greater privacy and security
 Better planning and execution
 Improved knowledge
management
 Emphasis on innovation and
transformation
REAL-TIME
EVENT
PROCESSING