targeted analytics: using core measures to jump-start enterprise analytics
DESCRIPTION
How top healthcare organizations are realizing the benefits of data analytics in such core areas as core measures, clinical alerting, surgical analytics, service line profitability, diabetes management, revenue cycle management, claims management and utilization.TRANSCRIPT
Targeted Analytics:Using Core Measures to Jump-Start Enterprise
Analytics
About Perficient
Perficient is a leading information technology consulting firm serving
clients throughout North America.
We help clients implement business-driven technology solutions that
integrate business processes, improve worker productivity, increase
customer loyalty and create a more agile enterprise to better
respond to new business opportunities.
PRFT Profile
Founded in 1997
Public, NASDAQ: PRFT
2010 Revenue of $215 million
20 major market locations throughout North America— Atlanta, Austin, Charlotte, Chicago, Cincinnati, Cleveland,
Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Minneapolis, New Orleans, Philadelphia, San Francisco, San Jose, St. Louis and Toronto
1,500+ colleagues
Dedicated solution practices
500+ enterprise clients (2010) and 85% repeat business rate
Alliance partnerships with major technology vendors
Multiple vendor/industry technology and growth awards
Perficient brings deep solutions expertise and offers a complete set of flexible services to help clients implement business-driven IT solutions
Our Solutions Expertise & Services
Business-Driven Solutions• Enterprise Portals• SOA and Business Process
Management• Business Intelligence• User-Centered Custom Applications• CRM Solutions• Enterprise Performance
Management• Customer Self-Service• eCommerce & Product Information
Management• Enterprise Content Management• Industry-Specific Solutions• Mobile Technology• Security Assessments
Perficient Services End-to-End Solution Delivery IT Strategic Consulting IT Architecture Planning Business Process & Workflow
Consulting Usability and UI Consulting Custom Application Development Offshore Development Package Selection, Implementation
and Integration Architecture & Application Migrations Education
Our Speaker
Michael Faloney
• Healthcare Director responsible for development and delivery of business intelligence and analytics solutions
• Responsible for engagement delivery and improving data solutions for Perficient's healthcare clients
• 20+ years progressive professional experience across technology, business, and process domains
• +18 years in the IT fields of data warehousing, business intelligence, project/technical management, and applications development
• Significant experience in developing enterprise data strategies, data architecture, data governance, data quality, data integration, master data management, metadata management, reporting and analytics
• Held technical and management positions focused on delivering data warehousing and business intelligence solutions to the healthcare, financial services, and telecommunications industries
• The Case for Healthcare Business Intelligence
• Options for Healthcare Business Intelligence
• The Targeted Analytics Approach
• Core Measures Example of Building an Enterprise Analytics Platform with Targeted Analytics
• Next Steps
Today’s Agenda
Healthcare Business Intelligence
The Case for Healthcare Business Intelligence
Competitive Pressure
Regulatory Pressure
Cost Pressure
Quality of Care
Innovative Research
Financial Effectiveness
Operational Efficiencies
Regulatory Compliance
Increasing internal & external pressures makes the ability to accurately analyze the organization’s data in a timely manner to make critical financial, clinical or operational decisions a requirement, not a “Nice to Have”
Regulatory Pressure: Pay for Performance Meaningful Use ICD-10
Cost Pressure: Reduced Funding/
Reimbursements Skill shortages Procurement management
Competitive Pressure: Consumer choice Specialist hospitals Attracting the Insured dollar
Healthcare Analytics Examples
Clinical Alerts
Core Measure Analysis
Longitudinal Records
Outcome Tracking
Patient Safety
Diabetes Management
Clinical Pathways Analysis
Personalized Medicine
Clinical Trial Effectiveness Analysis
Population Studies
Surgical Analytics
Material Usage Analysis vs. Outcomes
Cost Management
Service Line Profitability
Scheduling Analysis
Inventory Control Analysis
Claims Management
Service Line Profitability
Meaningful Use
Expanded Granularity using ICD-10
State Reporting
Public Health Reporting
Healthcare BI
Quality of Care Innovative Research
Financial Efficiencies
Operational Effectiveness
Regulatory Compliance
There are many options for business intelligence in Healthcare
Options for Healthcare Business Intelligence
“Top-Down Approach” More likely to have an
enterprise view and support from the beginning
Potentially longer time-line to deliver capabilities
“Bottom-Up Approach” More likely to have
departmental view at the beginning
Potentially shorter-time to deliver capabilities
Potentially requires significant rework to move to an enterprise platform
Can have either a departmental or enterprise view
Pre-built components offer potential for accelerated delivery
Often more of an accelerator based approach vs. shrink-wrap
Often provides only part of the solution and forces users to fit their problem into the package solution
Either a pre-packaged or accelerator –based approach
Typically tied to vendor’s transaction system(s)
Potentially limited ability to work outside their platform
Often not technology independent
EDW/DATA MARTS FEDERATED DATA MARTS
HEALTHCARE VENDOR/MAJOR
SOFTWARE COMPANY
SOLUTIONS
PACKAGE APPLICATIONS
What is the Targeted Analytics Approach?
Targeted analytics is a structured approach to building out an enterprise analytics platform through the implementation of a series of individual
applications focused on solving business critical issues
Structured Approach
• Leverages Governance, Technical & Implementation Frameworks
• Methodical identification & Use of Accelerators
Building an Enterprise
Analytics Platform
• Enterprise View of Data
• Quality, Consistent, Accurate Use of Data
• Enhanced Reporting & Analytical Capabilities
• Empowers Decision Making
Series of Individual
Applications
• Quicker Return on Investment
• Improved Time to Market
• Accelerated Realization of Benefits
• Flexibility to Address Changing Business Priorities
Solving Business Critical Issues
• Allows for short-term benefit of solving current business issues, while building for the long-term benefit of using enterprise data for competitive advantage
Accelerator-Based Implementation Framework
Perficient BI Enable™ Approach
Targeted Analytics Framework
Governance Framework
Enterprise View
Strategic Direction
Data Stewardship
Data Ownership
Data Guardianship
Enterprise Architecture FrameworkArchitectural
VisionTechnical Oversight Standards Technical
Direction
Accelerator/Reusable Component Library
VisualizationComponents
Integration Components
Metadata Components
Data Model Components
Other Components
Project Management Functional Expertise
Envision Execute Evolve
The governance framework ensures an enterprise view is maintained as the targeted analytics applications are implemented
The accelerator-based implementation framework:
Based on Perficient’s BI Enable Approach
Balances process, technology and organizational constructs
Heavily leverages the accelerator library
Provides project, functional and technical oversight
Heavily leverages prototyping as a design/development technique
The enterprise architecture framework provides the supporting technical vision for the required functional capabilities, as well as ensures the developed applications meets the appropriate standards
ENTERPRISE ARCHITECTURE
How Does Targeted Analytics Work?
1Develop Initial
Application (SCIP Core Measures)
2Populate
AcceleratorLibrary
3 Develop Additional Applications
Bu
sin
ess C
ap
ab
ilitiesPneumonia
Core Measures
Diabetes Cardiovascular
MeaningfulUse
ClinicalAlerting
GOVERNANCE
ACCELERATOR LIBRARY
VisualizationComponents
Integration Components
Metadata Components
Data Model Components
Other Components
Enterprise View
Strategic Direction
Data Stewardship
Data Ownership
Data Guardianship
Architectural Vision
Technical Oversight Standards
Technical Direction
Arc
hite
ctu
re V
isio
n
Imp
lem
en
tatio
n E
fficie
ncy
Enterprise View
Strategic Direction
Core Measures Example
Starting with SCIP Core Measures, you set the initial foundation of your analytics platform through the creation of enterprise level, re-usable components
SCIP VALUE
PROCEDURE
CORE MEASURE
TYPE
PHYSICIAN
DIAGNOSIS
PATIENT
CORE MEASURE
DESC
TIME
Data Source
1
Data Source
2
Data Integration
Core Measures Example
The development of the first analytical application provides a number of accelerators that can be reused in future analytical applications. The governance function provides
the enterprise view to ensure the re-usability in future analytical applications.
SCIP VALUE
PROCEDURE
CORE MEASURE
TYPE
PHYSICIAN
DIAGNOSIS
PATIENT
CORE MEASURE
DESC
TIME
Data Source
1
Data Source
2
Data Integration
DATA
GO
VERN
ANCE
ACCELERATOR COMPONENT LIBRARY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Core Measures Example
Using the base created with the first application, the implementation of another core measure area is significantly accelerated. The architecture function function provides
structure and process for leveraging the accelerators for future application
PROCEDURE
CORE MEASURE
TYPE
PHYSICIAN
DIAGNOSIS
PATIENT
CORE MEASURE
DESC
TIME
Data Source
1
Data Integration
ENTE
RPRI
SE
ARCH
ITEC
TURE
ACCELERATOR COMPONENT LIBRARY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Core Measures Example
Using the accelerators previously created as a based, additional functionality can be delivered in a more time-sensitive manner
PROCEDURE
CORE MEASURE
TYPE
PHYSICIAN
DIAGNOSIS
PATIENT
CORE MEASURE
DESC
TIME
Data Source
1
Data Source
3
Data Integration
FACILITY
PNEUMONIAMETRICS
ENTE
RPRI
SE
ARCH
ITEC
TURE
ACCELERATOR COMPONENT LIBRARY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Core Measures Example
Once the second application is developed, the accelerator library is populated with the additional re-usable components
PROCEDURE
CORE MEASURE
TYPE
PHYSICIAN
DIAGNOSIS
PATIENT
CORE MEASURE
DESC
TIME
Data Source
1
Data Source
3
Data Integration
DATA
GO
VERN
ANCE
ACCELERATOR COMPONENT LIBRARY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
FACILITY
PNEUMONIAMETRICS
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description• Facility
Metrics:• SCIP Value• Pneumonia Metrics
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
ENTE
RPRI
SE
ARCH
ITEC
TURE
Extending Past Core Measures
Once the second application is developed, the accelerator library is populated with the additional re-usable components
PROCEDURE
PHYSICIAN
DIAGNOSIS
PATIENTTIME
Data Source
1
Data Source
3
Data Integration
ACCELERATOR COMPONENT LIBRARY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
FACILITY
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description• Facility
Metrics:• SCIP Value• Pneumonia Metrics
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
ENTE
RPRI
SE
ARCH
ITEC
TURE
Extending Past Core Measures
Once the second application is developed, the accelerator library is populated with the additional re-usable components
PROCEDURE
PHYSICIAN
DIAGNOSIS
PATIENT
DISCRETE MEASURE
TIME
Data Source
1
Data Source
3
Data Integration
FACILITY
Meaningful Use Metrics
ENTE
RPRI
SE
ARCH
ITEC
TURE
ENCOUNTER
ADMISSION DATE DISCHARGE DATE
NURSING UNIT
Data Source
4
Data Source
5
ENTERPRISE ANALYIC PLATFORM
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description• Facility
Metrics:• SCIP Value• Pneumonia Metrics
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Extending Past Core Measures
Once the second application is developed, the accelerator library is populated with the additional re-usable components
DATA
GO
VERN
ANCE
ENTE
RPRI
SE
ARCH
ITEC
TURE
ENTERPRISE ANALYIC PLATFORM
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:• Time• Patient• Diagnosis• Procedure• Physician• Core Measure Type• Core Measure
Description Metrics:• SCIP Value
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Metadata Enterprise
Definitions for Data Elements
Data Integration Mappings Transformations Data Quality Rules ETL Components
Data Model Dimensions:
Metrics:• SCIP Value• Pneumonia Metrics• Meaningful Use Metrics
Hierarchies Descriptive Attributes
Presentation/Analytic Capabilities
Dashboard Framework Dashboard Widgets
Data Visualization Report Templates
Other Security (ex. Roles) Automation
Constructs
Time Encounter
Patient Admission Date
Diagnosis Discharge Date
Procedure Nursing Unit
Physician Discrete Measure
CM Type CM Description
Facility
PROCEDURE
PHYSICIAN
DIAGNOSIS
PATIENT
DISCRETE MEASURE
TIME
Data Source
1
Data Source
3
Data Integration
FACILITY
Meaningful Use Metrics
ENCOUNTER
ADMISSION DATE DISCHARGE DATE
NURSING UNIT
Data Source
4
Data Source
5
In Summary
Q & A
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