accelerating your move to value-based care

Accelerating Your Move to Value-Based CareAchieving Information Management Maturity for Faster Results

1

Dan Schultz – Information BuildersRahul Ghate – Prosperata

Moving from Volume to Value Based Care…

2

The Industry is Reacting to These Pressures

Consolidation, Mergers and Acquisitions

Ecosystem Convergence

Shared Risk/Savings

Evolution of Patient to Consumer

3

Today, It’s All About Facing Data Challenges…

Clinical Data Challenges

4

These Challenges Aren’t Small, Either…

Patient Matching Difficult to identify across continuum of care No common identification number for a person

IT Resource Staffing in Small Physician Groups Lack dedicated staff Little knowledge of IT requirements for data

sharing

Data Volumes Overwhelming amount of data in healthcare Vital to identify data relevant to clinical

measures that improve cost & quality of care

5

And Sometimes, It’s Process and Technology…

Incorrect Data

More harmful than a lack of information

Leads to inaccurate or incomplete treatment

Data Quality & Terminology Gaps

Provider systems struggle with compatibility

Numerous standards and clinical terminologies

Local proprietary codes need to map standard codes

6

Information Builders Data Management Platform

7

Introducing Omni-HealthData™

Omni-HealthData

A Person-based Information Management solution for Health Insurers and Providers:

Pre-built data models for mastered and transactional domains

Pre-built processing, quality, mastering, and remediation rules

360 Degree View on Members/Patients and Providers through Data and Analytics

8

What is Omni-HealthData?

Omni-HealthData

Programs & Applications Quality Reporting Programs – HEDIS & STAR Care coordination and Transition of Care in PCMH setting Value based reimbursement models

Risk Stratification/Adjustment Greatest details about patient health and risks Validate risk assumptions and predictions

Optimize Utilization Reduce/avoid redundant testing and variability in care Address fraud or medically unnecessary utilization

Optimize Costs Real time integration with HIEs and EMRs Reduce manual chart chase

Member Outreach Faster and more targeted campaigns

(High Risk Patients with multiple Chronic Conditions)

9

Business Value

Improved Patient and Provider Experience

Total cost of care and 360 view of patient

Timely intervention

Build trust – single version of truth across a spectrum of care

Omni-HealthData

Richer Data Set Vital Signs, Lab Results Social History, Family History

More Complete Set of Diagnoses More than just what physician bills in EMR Clinical Data can be used to impute diagnosis

Timeliness Clinical data is available near real-time Claim data could be delayed by weeks

Longitudinal View Patient history vs. particular Visit/Encounter

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Clinical Data

Claims Data

Diagnoses

Family History

Social History

Vital Signs

Lab Results

Omni-HealthDataMap, Master, and Steward

Downstream appsProvider relationsClaims adjudication

AnalyticsData warehousesData marts

ExternalProvider & member portalsReimbursement

On

ramp

s: C

CD

, relation

al, XM

L, etc.

Co

nsu

mp

tion

: H

EDIS G

rou

per, C

CD

, views, etc.

Integrate, Cleanse, Correlate, Steward

Reference data

Code sets: HLI

Internal dataMember (e.g., Initiate)ClaimsEligibility

External data MemberAdministrativeClinical (CCD, HL7, etc.)FacilityProvider info

Omni-HealthData

Built from the Omni Repository

Consumption Views: De-normalized for easy consumption in BI and analytics

Metrics Views:

Pre-analyzed, materialized views

Supports standard volume and quality metrics

Healthcare analytics and regulatory metrics

HealthViews

Omni Repository

HV - Consumption Views

HealthViews - Metrics Views

Customer Queries / Presentation Views

Cu

stom

Omni-HealthData Insights, WebFOCUS, Cohort Builder

But, It Takes Work to Get to an Omni-HealthData…

13

Success in Value-Based Care with Mature Information Management

3 KEY STEPS

15WHY WE EXIST

• Help healthcare organizations use their data

assets and technology to:

Thrive in the world of value-based care

Meet revenue and profitability targets

Achieve your version of the triple-aim objectives

Copyright © Prosperata, LLC. All Rights Reserved.

Prosperata wants healthcare to prosper with its data

16Tough Questions Require Better Analytics for Better Decisions

We need to manage

diabetes populations. How

can I identify the population

and develop a strategy that

improves outcomes?

How can we maximize our

in-network referrals to

better accommodate

Veterans needs?

Healthcare

Executive

Our project portfolio is over

budget. How can I get to

the root-cause and turn this

around?

We need to reduce the

number of redundant MRIs

how can I identify the

outliers and prevent future

outliers?

The “tough” questions in healthcare are fundamentally enterprise data challenges and require a

comprehensive enterprise approach.

“Predicting and preparing for the world of tomorrow is no easy task. Reliably forecasting outcomes, events, and patterns will in most cases require not only substantial data, but clean and correct data, along with sophisticated models and analysis.”

HFMA - Healthcare Financial Management

17Understanding Data 3.0

Data 1.0

Data related to specific business processes

• E.g. Departmental data marts

Data 2.0

Data focused on enterprise wide initiatives

• E.g. EDW, SCM, ERP

Data 3.0

Data is actionable, explainable, trusted and contextualized

Data front and center, used to transform businesses and support new business models


18DATA MATURITY in 3 STEPS

•Develop strategic plan to differentiate with Data

Information Management

Strategy

•Deploy better Data Governance

•Evaluate and improve Data Quality

Data Governance,

Data Quality

•Modernize existing BI/Data investments

Modernization to Data 3.0


1

2

3

Current Status: 35% Current Status: 23%


STEP 1 – Information Management Strategy

2014-POINT DATA/ANALYTICS MATURITY MODEL


Maturity: Informal Incipient Organized Operational Transformative

ORG

AN

IZATIO

N

Technical Expertise

No experience managing formal repository and workflow systems

Struggling 1.0 implementations ofsome systems

More advanced version 2.0+ implementations of systems with focus on business-critical content

Managing repository & workflow systems is a core IT skill, with mature systems in place

Pro-active experimentation & learning about emerging content technologies

Business Experience

Ignorance about value and role of EIM

Growing sense of need for EIM, supported by fragmented initiatives

Departmental ownership of EIM initiatives; analytical teams built independently

Executive ownership of EIM as a practice; process & data analysis are core skills

Information management is a required employee skill & part of their HR reviews

Process Few or no standardizedprocedures

Basic process analysis leads to some ad-hoc information workflows

Identification of interdepartmental information dependencies, with partial automation

Automated information dissemination processes span systems & departments

Robust processes to cover exception-handling & experimentation

Alignment Key business drivers are not well understood by IT strategists, resulting in EIM gaps in IT portfolio

Improved IT-business communication, but IT mostly disconnected from business outcomes

Sustained efforts for IT-business collaboration, results still dependent on negotiation

Execution of IT & business strategies is cohesive, with fewer instances of “push pull” model

IT and business are true partners, performance metrics fully aligned with strategic business objectives

INFO

RM

ATIO

N

Metadata No formal inventory or classification

Departmental inventories and initial content tagging

Enterprise inventory underway; controlled vocabularies initiated

All new repositories & content types registered; global taxonomies created

Ongoing metadata reviews are standard practice

Quality Data quality is an afterthought Ad-hoc initiatives and manual interventions

Data quality criteria developed, partially implemented

Data quality process implemented and automated

Routine quality reviews and proactive monitoring of data processes

Lifecycle No lifecycle management Most content archived haphazardly; some loose records management (RM) initiatives

Development of formal electronic & paper-based RM process; implementation initiated

Implementation of electronic & paper-based RM across the enterprise

All content types go through formal lifecycle management

Governance No policies & procedures Scattered policies; few or no formal procedures

Development of information governance structure & codification of procedures

Policies & procedures widely disseminated; Enterprise ownership in place

Active review & adaptation; executive support at highest levels

Re-use Content routinely duplicated Some informal consolidation initiatives

Structured content analysis & creation of mitigation plan

Information repurposed across systems & channels

Checks in place to prevent future duplication

Findability Information is hard to find, requiring manual effort and dependency on select few

Systems support search capability with basic metadata applied

Controlled vocabulary terms leveraged for search

Consolidation of search capabilities across key systems

Implementation of enterprise &/or federated search applications

APPLIC

ATIO

NS

Analytics Focus on operational reporting Historical data analysis; dashboards & scorecards

Ad-hoc analysis, information delivery; what-if modeling; forecasting

Pervasive self-service capability; predictive analytics for selected use cases

Deep predictive & prescriptive analytics; routine experimentation with new technologies

Architecture No architectural consistency across systems

Initial attempts at reference architecture; Documentation for key areas

Reference architecture used for key projects; Logical data model available; Thorough documentation


Enterprise architecture adopted; Architectural governance in place

Security No security regime in place Security dependent on capability of individual systems

Formal projects initiated to address gaps & redundancies due to multiple solutions

Standardized policies & procedures exist & are system enabled

Security is a centralized shared service; Proactive monitoring of threats

Usability Lack of systems make end user usability considerations moot

Employee adoption rates measured, but dissatisfaction

Some initiatives use ScenarioAnalysis & User Persona techniques

User-centered design underpins all system designs, with formal

Usability is a guiding principle in all system activity

Ongoin

g I

mpro

vem

ent

thro

ugh M

easu

rem

ent

& M

onitoring

21OUR PROVEN MATURITY MODEL



ORG

AN

IZATIO

N Technical Expertise

Business Experience

Process

Alignment

INFO

RM

ATIO

N

Metadata

Quality

Lifecycle

Governance

Re-use

Findability

APPLIC

ATIO

NS Analytics

Architecture

Security

Usability

14 areas of maturity, organized into 3 broad categories and 5 levels of

progression

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• Exhaustive list of enterprise data assets organized by subject area, data quality and ownership

Data Asset Inventory

• Clear understanding of priorities of individual business units and their dependence on data/analytics

Business Workload Analysis

• Selection of the most viable architectural components to solve business workloads

Architectural Component

Mapping

• 3-5 year strategic yet practical roadmap, ready for execution

3-5 Year Execution Roadmap

TYPICAL COMPONENTS OF IM STRATEGY INITIATIVE

23SAMPLE TIMELINE


Analysis of business objectives & workloads

W10W9W8W7W6W5W3W2W1 W4

Project kickoff meeting

Biz stakeholder meetings begin

C-level buy in IM strategy

ready for execution

Info architecture

review

3-year maturity targets

Access current IM maturity state and set transformation targets

Deep-dive technical reviews

Tech capability discussions

W11 W12

Current state IM assessment

Prioritization

Assemble Roadmap, Iterate

Review draft roadmap

Iterate to develop final version

VP-level prioritization and buy in

C-levelbuy-In

PPT component

mapping

24

Ongoin

g I

mpro

vem

ent

thro

ugh M

easu

rem

ent

& M

onitoring


ORG

AN

IZATIO

N

Technical Expertise

No experience managing formal repository and workflow systems

Struggling 1.0 implementations ofsome systems

More advanced version 2.0+ implementations of systems with focus on business-critical content

Managing repository & workflow systems is a core IT skill, with mature systems in place

Pro-active experimentation & learning about emerging content technologies

Business Experience

Ignorance about value and role of EIM

Growing sense of need for EIM, supported by fragmented initiatives

Departmental ownership of EIM initiatives; analytical teams built independently

Executive ownership of EIM as a practice; process & data analysis are core skills

Information management is a required employee skill & part of their HR reviews

Process Few or no standardizedprocedures

Basic process analysis leads to some ad-hoc information workflows

Identification of interdepartmental information dependencies, with partial automation

Automated information dissemination processes span systems & departments

Robust processes to cover exception-handling & experimentation

Alignment Key business drivers are not well understood by IT strategists, resulting in EIM gaps in IT portfolio

Improved IT-business communication, but IT mostly disconnected from business outcomes

Sustained efforts for IT-business collaboration, results still dependent on negotiation

Execution of IT & business strategies is cohesive, with fewer instances of “push pull” model

IT and business are true partners, performance metrics fully aligned with strategic business objectives

INFO

RM

ATIO

N

Metadata No formal inventory or classification

Departmental inventories and initial content tagging

Enterprise inventory underway; controlled vocabularies initiated

All new repositories & content types registered; global taxonomies created

Ongoing metadata reviews are standard practice

Quality Data quality is an afterthought Ad-hoc initiatives and manual interventions

Data quality criteria developed, partially implemented

Data quality process implemented and automated

Routine quality reviews and proactive monitoring of data processes

Lifecycle No lifecycle management Most content archived haphazardly; some loose records management (RM) initiatives

Development of formal electronic & paper-based RM process; implementation initiated

Implementation of electronic & paper-based RM across the enterprise

All content types go through formal lifecycle management

Governance No policies & procedures Scattered policies; few or no formal procedures

Development of information governance structure & codification of procedures

Policies & procedures widely disseminated; Enterprise ownership in place

Active review & adaptation; executive support at highest levels

Re-use Content routinely duplicated Some informal consolidation initiatives

Structured content analysis & creation of mitigation plan

Information repurposed across systems & channels

Checks in place to prevent future duplication

Findability Information is hard to find, requiring manual effort and dependency on select few

Systems support search capability with basic metadata applied

Controlled vocabulary terms leveraged for search

Consolidation of search capabilities across key systems

Implementation of enterprise &/or federated search applications

APPLIC

ATIO

NS

Analytics Focus on operational reporting Historical data analysis; dashboards & scorecards

Ad-hoc analysis, information delivery; what-if modeling; forecasting


Deep predictive & prescriptive analytics; routine experimentation with new technologies

Architecture No architectural consistency across systems

Initial attempts at reference architecture; Documentation for key areas

Reference architecture used for key projects; Logical data model available; Thorough documentation


Enterprise architecture adopted; Architectural governance in place

Security No security regime in place Security dependent on capability of individual systems

Formal projects initiated to address gaps & redundancies due to multiple solutions

Standardized policies & procedures exist & are system enabled

Security is a centralized shared service; Proactive monitoring of threats

Usability Lack of systems make end user usability considerations moot

Employee adoption rates measured, but dissatisfaction

Some initiatives use ScenarioAnalysis & User Persona techniques

User-centered design underpins all system designs, with formal

Usability is a guiding principle in all system activity

YOUR IM JOURNEY: CURRENT STATE AND FUTURE TARGETS

25TYPICAL OUTPUTS of STEP 1

• Data asset inventory

• Current state data maturity assessment

• 3-5 year strategic Information Management roadmap• Prioritized and time-lined list of initiatives

• ROI/TCO analysis

• High-level execution plan



STEP 2 – Data Governance, Data Quality

27

The organization gets so complex that traditional management of data assets is not sufficient

Data security, privacy and

quality concerns

Complex regulatory,

compliance or contractual

requirements

Weak alignment between IT and

Business lowering ability

to use data assets

WHEN DO ORGANIZATIONS NEED DATA GOVERNANCE?

28TRUST METHODOLOGY


TAILOR Data Governance

RATE Data Quality

UNLEASH Data Stewardship

STANDARDIZE Business Rules

TRANSFORM Data Quality

• Review current Data Governance constructs• Design improved Data Governance program• Train and assist internal team for deployment

• Conduct subjective analysis of data quality• Use profiling for objective analysis of data quality• Design customized data quality initiative

• Identify key data activities that need stewardship• Develop data stewardship processes• Train data stewards

• Develop data quality rules for key subject areas• Deploy data quality rules using leading tool• Develop enterprise data quality dashboards

• Implement data stewardship tool• Train data stewards for remediation activities• Implement automated data quality transforms

29SAMPLE TIMELINE for TRUST


Data Governance Optimization

M10M9M8M7M6M5M3M2M1 M4

Data Governance discussions with business & IT leadership

Remediation process for addressable DQ issues in place

Business Rules for Selected Subject

Areas

Subjective/Objective Analysis

M11 M12

Deploy DQ Tool & Dashboards

Data Stewards Meaningfully Engaged in Data Quality Improvements

DQ dashboards deployed to business & IT stakeholders

Business rules for top subject areas developed

Deploy Remediation Portal

Data quality evaluation complete


STEP 3 – Modernization to Data 3.0

31TRADITIONAL APPROACH TO MODERNIZATION


Data Model Design

Reporting, Advanced Analytics,

Visualization

Performance & Scalability

Security Architecture

Master Data Management

Data Quality

Data Lifecycle Management

ETL Architecture

Data Governance

e.g. Payment Integrity

Financial Data

Clinical Data

e.g. Revenue

Cycle

Data Mart 1

Data Mart 2

Alerts, Dashboards, Reports, Visualization, Discovery, Predictive

Operational Data

Other Data Sources

External Vendor BI Applications

Land & Transform

Data Governance Program

ODS EDWDeep

History

Data Extract Staging

Data Lake

Inb

ou

nd

EDI

Ou

tbo

un

d ED

I

Data Mart 3

Areas of Review

EDI Operations

Cloud Adoption

Big Data & DW Offloading

32“NO REFERENCE” ARCHITECTURE APPROACH


Anticipated Business Workloads (Payer Example)

New ACO initiative needing intake of clinical data

New insurance product roll out

Direct access to data for brokers

Improved process for Appeals & Grievances

Quality checks for data files submitted to CMS

Architectural Components

Traditional Data Warehouse

MDB/Cubes

Data Appliance Data Warehouse Appliance

Columnar Database Data Lake, Hadoop, etc.

NoSQL Data Quality Tools

Data Virtualization Graph Database

MDM Tools Data Stream Processing

BI & Data Visualization Tools

Predictive Analytics Tools

Healthcare Cloud Mobile Technologies

BPM Tools ILM Tools

Natural Language Processing

B2B/HIE Tools

Map Business Workloads to Suitable Architectural Components

33SUMMARY

•Develop strategic plan to differentiate with Data

•4-20 weeks

Information Management

Strategy

•Deploy better Data Governance

•Evaluate and improve Data Quality

Data Governance,

Data Quality

•Modernize existing BI/Data investments

Modernization to Data 3.0


1

2

3