Considerations for Developing a Payer Data Analytics Strategy · Considerations for Developing a Health Data Analytics Strategy Payment Reform Regulation Consolidation ... reporting

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


Maturity scale for Data analytics

5


Top healthcare industry trends

6


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


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


End-to-End Service Delivery Framework and Flow

Framework for selecting pilot analytics

9


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

11


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


executing analytics

• Optimal knowledge sharing across all

portfolios

• DA team will develop a broader

understanding of ABC Payer’s

business, over time


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

12


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.

13


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

15


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.


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act, in reliance on the information contained in this publication or for any decision based on it.

© 2016 PricewaterhouseCoopers LLP. All rights reserved. In this document, “PwC” refers to PricewaterhouseCoopers LLP

which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal

entity.

Documents

Considerations for Developing a Payer Data Analytics Strategy · Considerations for Developing a Health Data Analytics Strategy Payment Reform Regulation Consolidation ... reporting