Hassan Naeem
Manager
PriceWaterhouseCoopers
October 7, 2016
Considerations for Developing a Health
Data Analytics Strategy
Topic Pg.
Analytics Strategy 3
Industry Overview 6
Understanding the Full Power of Analytics 9
Case Study 10
Demos – IA Analytic “Use Cases” 12
Appendix 13
PwC Contact Information 15
Health data analytics agenda
2
Considerations for Developing a Health Data Analytics Strategy
PwC
Analytics Strategy
Foundation for effective data analytics
Analytics are executed against defined process and quality standards that result in repeatable and sustainable procedures
Understanding of the data landscape and what is most relevant to meet your objectives
Change
People
Data
Process Infrastructure & Tools
Business Value
Structure for establishing ownership and accountability across the business including core analytics, internal audit, compliance, operations
Dedication to modern technology platform provides advanced analytics capabilities including ETL, visualization and predictive/trending techniques
Teaming with stakeholders to address relevant business needs in a timely manner
Vision Acceptance
Mandate Funding
Relationships
4
Considerations for Developing a Health Data Analytics Strategy
Maturity scale for Data analytics
5
Considerations for Developing a Health Data Analytics Strategy
Top healthcare industry trends
6
Considerations for Developing a Health Data Analytics Strategy
Regulation
OIG’s updated Work Plan focuses on data and analytics
HRSA releases 340B Program 'mega guidance‘
Presidential election will result in the first time a new Administration takes charge of the implementation of the Affordable Care Act
Payment Reform Consolidation
Providers and insurers plan aggressive push to new payment models
The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) promises to fundamentally change the way the United States evaluates and pays for healthcare
CMS has drastically increased the
PQRS requirements for avoiding the PQRS penalty
Health systems, insurance plans and physician practices are on the verge of rapid consolidation
If the deals pass regulatory scrutiny unscathed, 3 major players will dominate the insurance market by 2017
50% of current health systems will likely remain in 10 years
How can we help
7
Considerations for Developing a Health Data Analytics Strategy
Regulation Payment Reform Consolidation
Physician Contract Compliance
Compliance & Controls for Quality Reporting
340B Compliance
EHR Analytics
Analytics based approach to the assessment of physician contracts for regulatory contract compliance, fair market value comparisons of physician compensation, and false claims identification
A consolidated and analytical reporting solution which creates the foundation for a unified data infrastructure used to achieve compliance across the different quality programs (P4P, HEDIS, MIPS, etc.)
Custom analytics based 340B operations program with a key focus on the rules related to drug diversion, duplicate discount, GPO exclusion, and orphan drug exclusion
Pre- and Post- go live analysis of EHR claims, master files, interfaces and configurations identifying key areas of risk stemming from EHR implementations and upgrades
How we execute
8
Considerations for Developing a Health Data Analytics Strategy
End-to-End Service Delivery Framework and Flow
Framework for selecting pilot analytics
9
Considerations for Developing a Health Data Analytics Strategy
PwC
Case Study
Moving up the curve – approach to analytics
What is the goal
in the use of
Data Analytics?
How will we use
DA to meet our
objectives?
What will enable
us to meet these
objectives?
The ultimate goal is to leverage data analytics to transform its how we measure risk an
identify opportunities, in order to provide stakeholders with stronger insights into
opportunities, issues and root causes, and a greater level of assurance.
Risk Assessment
DA will be used to perform
risk based comparisons of
auditable processes. This will
allow ABC Payer to assess and
compare auditable processes
as part of the continuous risk
assessment.
Audit Planning
DA will assist in audit
planning by assessing entire
populations of transactions
prior to initiating audits. Trends
and outliers will be analyzed to
help identify areas for further
inquiry or assessment.
Transactional Testing
DA will be used to assess
entire populations of
transactions to help validate
adherence to policies and
procedures, providing greater
insights than traditional
controls testing.
Continuous Monitoring
DA capabilities (e.g. risk
monitoring dashboards)
developed through
partnerships with business
owners, which will enhance
management oversight of risks,
and will allow IAD resources to
focus on new risk areas.
Technology
• Advanced tools
and infrastructure to
support DA program
• Subject matter
specialists across
various domains
(e.g. programmer,
statistician, etc.)
Governance Structure
• An organizational model
that provides appropriate
resources to execute DA
• Clear support for the use
of analytics and initial
investment from
management
People
• Shift in business
auditors mindset when
it comes to executing
audits (i.e. fully
embrace use of DA)
• Formal competency
models and learning
maps for developing
personnel
Process
• Redefined planning and
execution protocols to
embed DA across the
audit lifecycle
• Standardized
methodology to select
and execute DA
• Formal review and
quality control
standards for DA work
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Considerations for Developing a Health Data Analytics Strategy
Integrating the team with data specialization - key characteristics of operating models
Pro
s
Co
ns
E
xam
ple
Utilizing DA Resources
(Not part of IAD)
Build DA Team
(Product Focused)
Build DA Team
(Not Product Focused)
+ DA Team
Portfolio A
DA Sub-Teams Support
Specific Portfolios
Business Auditors
DA Team
DA Team Supports all
Portfolios
+
Business Auditors
DA Team
Portfolio B
DA Team
Portfolio C
• Accelerated timeframe to start
executing analytics
• Potential ability to utilize resources
with deep knowledge of business
areas being audited
• Accelerated timeframe to start
executing analytics
• Optimal knowledge sharing across all
portfolios
• DA team will develop a broader
understanding of ABC Payer’s
business, over time
• Accelerated timeframe to start
executing analytics
• Enhanced ability to create advanced
analytics in critical areas, due to
focused efforts
• Potential efficiencies in developing
analytics due to focused efforts
• Internal - Potential resource
constraints if greater priorities arise, as
resources are not dedicated to IAD
• External – Potential loss of company
knowledge if different resources are
used for each project
• Potential inability to develop advanced
analytics in critical and complex areas
due to lack of focus
• Potential inefficiencies in developing
analytics due to lack of focus
• Lack of knowledge sharing on issues
that may be occurring in other
portfolios
• DA team will have narrow / limited
knowledge of ABC Payer’s business
+
Business Auditors
DA Resources (Not part of IAD)
DA Resources Support all
Portfolios
1 2 3
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Considerations for Developing a Health Data Analytics Strategy
Overview of Demo’s - Analytic “Use Cases”
Claims Outlier Model PwC’s proprietary statistical model analyzes 100% of physician claims at the procedure code level to identify statistically valid outliers. Furthermore, the model ranks outliers according to multiple variables to help payers prioritize remediation.
Internal Sales Bonus This use case demonstrates highly targeted auditing of sales bonuses analytics modeling. The audit objective is to assess the accuracy and completeness of bonuses granted to employees through highly-targeted sampling.
Appeals Compliance Monitoring The appeals process contains a large number of regulatory requirements, such as timeliness metrics, to acknowledge and process appeals. This use case shows how these types of metrics can be effectively (and continuously) monitored to mitigate regulatory risk and increase performance.
Customer Service This analytics use case demonstrates continuous monitoring of the CMS Customer Service metrics across their organization. The tool provides a high level view of the customer service performance relative to the CMS thresholds and additional “drill-down” insights to assess the root cause for non-compliance.
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Considerations for Developing a Health Data Analytics Strategy
PwC
Appendix A – Technology Workbench (Example)
Technology Workbench (Example)
A technology workbench should allow youto analyze new data sources and apply new techniques to the existing testing solution. The following is an example of what a technology workbench could include:
Connect
Store
Model
Analyze
Connection to Data Sources to Extract
Data Connect to enterprise systems to get
data directly from the source systems
• SQL tools
• Talend
• Pentaho
Storage of Data and Analysis
Storage of data extracted from enterprise
systems and from analysis conducted by
the Data Analytics team
• SQL Server
• Hadoop
• Oracle
Data Modelling and Analytics
Analytics tools designed to modify and
transform data for insight
• ACL
• SAS
• R
• Python
• Excel
• Paxata
• Alteryx
• Lavastorm
Data Visualization
Tools designed for interacting and
visualizing data to support the analysis of
data and extraction of business insight
• Tableau • Spotfire
• Qlikview
Technology Component Core Tools* Other Tools to Consider
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Considerations for Developing a Health Data Analytics Strategy
Hassan Naeem (703) 789-4401 PwC | Manager – Advanced Risk & Compliance Analytics
Email: [email protected]
PwC contact information
16
Hassan is a Manager in PwC’s Health Industries practice, specializing in Advanced Risk and Compliance Analytics.
Hassan has over eight years of relevant experience largely within client driven consulting within multiple sectors including Healthcare and Federal/Government Regulations. Hassan primarily assists his clients within healthcare in identifying key risk indicators and compliance assessments utilizing Data Analytics. Hassan has extensive experience in both Payer and Provider services.
Hassan’s experience includes compliance program and risk assessments, mock audits, Data Universe Validation, Claims outlier identification and data validation by conducting root-cause analysis, and using key metrics to monitor the results of process improvement initiatives.
Considerations for Developing a Health Data Analytics Strategy
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