AN ANALYTICS JOURNEY: CASE STUDY OVER SEVEN YEARS RICH POLLACK CHCIO, FCHIME VP&CIO VCUHEALTH

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THE VCU HEALTH SYSTEM

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• VCU Health System

• Only academic medical center in Central Virginia

• 2014 McKesson-AHA Quest for Quality Prize Recipient

• Referral center for the state and Mid-Atlantic

• 37,000 admissions and 650,000 outpatient visits

• MCV Hospitals and Clinics

• Teaching hospital of the VCU Health System

• 1125 licensed beds

• 90,000 patients are treated annually in the hospital’s emergency department,

which is the region's only Level I Trauma Center

• 100+ primary care, specialty and sub-specialty outpatient clinics

• NCI designated Cancer Center

• 11,000 employees

• MCV Physicians

• 720-physician, faculty group practice

• Virginia Premier Health Plan

• 200,000 member Medicaid Health Plan

7 YEAR JOURNEY

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2009-11

2012

2013

2014

2015

2016

Outside Consultant

Engaged an

outside firm with

deep expertise to

conduct an

assessment

(current state –

future needs) and

optional

recommendations

for moving

forward including

management and

governance

structures.

Educating my peers

Slowing building

the case thru 1:1

sessions with

over 20 leaders

to help them

understand the

current state and

future need and

how to close the

gaps

Approval of EA Effort

After two abortive

attempts, was able to

get the endorsement

of senior leadership

to elevate analytics,

consolidate siloes,

rename the service,

establish new

leadership, setup

data governance

structure, and

increase investment

in Analytics

Enriching the EDW

Began populating the

Oracle tables with

EMR data via direct

ODBC feed from

EMR production

node. Began transfer

of EMR

data/reporting

expertise from EMR

team to EA team

Current State

17 person EA team.

Director reporting to

corp VP of Strategy.

Data Architect, ETL

specialist, Tableau

visualization tool,

genomic data,

serving researchers

with clinical data.

Mature data

governance

Maturing Data Gov.

Brought in

outside experts in

data gov., and

healthcare

analytics to help

catalyze our

efforts.

Developed robust

committee

structure led by

COO with broad

representation.

Started sub

committees

focused on MDM

and data integrity.

CURRENT STATE IN 2006

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• Small 5 person decision support team reporting to CMO

• EDW migrated from mainframe to C/S Oracle and Cognos

• Primarily populated with billing data in star schema

• 4 to 5 other “siloed” data marts existed

ABORTIVE ATTEMPT TO USE EMR VENDORS DATA WAREHOUSE FOR CLINICAL ANALYTICS

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• Incompatible tools Business Objects versus Cognos

• Inability to easily import non EMR data

• Little focus on addressing non clinical reporting needs

• Lack of data management tools (MDM/Data Dictionary)

DISCUSSIONS WITH CEO 2008 – 2009

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• Change in IT strategic focus to emphasize Analytics

• Predicted dramatic growth in need and resources

• Discussed need for different structure, elevation in the org, elimination of siloes and enlargement of team

• Need for data governance and data management structure and process

• Charged CIO to begin peer educational effort

VCUHS today

VCUHS future

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2008 DISTRIBUTION OF DATA MARTS:

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CTSA DW I2B2

(1 FTE)

Cerner DW Business Objects

(2 FTE)

IDX Globalworks

Cognos8 (2 FTE)

DSS Cognos8 (6 FTE)

Core Transaction Systems GE/Lawson/Cerner/etc

Cancer Center DW

(4 FTE)

VPHP DW

(~20 FTE)

OHI Population Health

(1 FTE)

If left unaddressed, continued future growth of data silos

PEER EDUCATION FROM 6/2009 THRU 6/2011

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• Development of a comprehensive educational presentation illustrating where we needed to go and the gap that existed between that future state and current capability.

• Persistent 1 on 1 meetings and discussion with peer VPs and other leadership across the health system.

ENTERPRISE BUSINESS INTELLIGENCE AN EXPLORATION OF THE STRATEGIC IMPLICATIONS AND POSSIBILITIES FOR THE HEALTH SYSTEM

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Rich Pollack MS CPHIMS FHIMSS

VP & CIO

VCUHS September 14, 2010

EXECUTIVE SUMMARY

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To succeed in this new era of accountability and regulatory prescriptions, we need to possess a level of insight and analysis around operations, finance, clinical care and research that has heretofore been absent. To achieve that future state there will be a need for investment in either incremental or dramatic improvements in our core analytics capability.

Today, we believe there exists a “gap”, between current delivery of reports and analysis via DSS (and other data marts) – and the very real needs of decision makers for reporting that is more multidimensional, for financial modeling capability and for easy patient cohort queries in near real time. Accordingly we need to:

Understand the needed roadmap to realize the unmet potential of our of our rich underlying business/clinical data set to provide actionable real time intelligence across patient populations and venues of service delivery

Calculate and establish the effort needed to raise our analytic delivery engine to equal those healthcare organizations much further along in executing best practice in this space

Geissinger

Partners

Northwestern Memorial

Ohio State

Intermountain

Mayo

EXECUTIVE SUMMARY (CONT.)

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Specify the particular use case objectives for the clinical, operational and financial domains based upon business/clinical goals and priorities clearly articulated by leadership.

. Evaluate the current infrastructure and tools available in-house and from VCUHS’ core vendors, and understand the “gap” between anticipated needs and vendor capabilities.

Finally, we will define VCUHS future data integration and warehouse strategy to ultimately provide robust business and clinical intelligence support for both current and future needs.

Therefore, our intent is to initiate an early planning process to identify VCUHS’ critical business and clinical dashboard metrics, performance reporting requirements and clinical research needs that may be met through improved data warehouse technologies and data integration.

We will map those requirements to current analytics capabilities and infrastructure, and will develop the go-forward plan to leverage current tools, as well as develop the future VCUHS analytics and data warehouse architecture and resources.

Strategy Development

Capital Allocation

Revenue Cycle Management

• In what services do I make money? Lose money? Why?

• In which services should I invest? Divest? Improve financial performance?

• How do my hospitals services compare across the health system?

• How can I optimize service location? Should I consolidate services?

• What is my profitability across service lines by hospital?

• Where should I focus my cost reduction initiatives? How do I track success?

• Why are costs for X service increasing, is it labor, supplies or physician practices? How can we decrease these costs?

• Are patient care processes following protocols? Why not? Is it physician or patient complexity driven?

• What physician should set standards for others?

• What impact to my bottom line will a rate change create? Can I afford to sign this managed care contract? Can I afford not to?

• What types, how much and where are my denials originating?

• How relative is my pricing to cost? Comparatively throughout my system?

• Are my payors paying correctly? Timely?

• Can I compare my payors profitability and score them?

• Am I charging for all the services I am performing?

Operations Improvement

• What services should I invest capital? What financial return can I expect?

• What is the ROI of a capital purchase?

• What incremental ancillary capacity will be affected by additional capital purchases?

Quality, Patient Safety and P for P

• What metrics do I measure and how do I measure them?

• How do I get information to front line caregivers and managers to make a difference?

• How can I efficiently collect and report quality and patient safety metrics?

INFORMATION NEEDS TO BE TIMELY AND RELIABLE TO BE USED FOR STRATEGIC DECISION-MAKING AND DERIVING OPERATIONAL INSIGHT

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SAMPLE CLINICALLY RELEVANT YET DIFFICULT QUESTIONS TO ANSWER

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• What are the incremental cost reductions. Length of stay reductions and improved outcomes for patents treated using the CAP protocol compared to those not on the protocol?

• How did treatment differ by physician for DRG stratified patient cohorts (using EMR data)?

• How many pneumonia cases were re-admitted for pneumonia within 6 month by provider by co-morbidities?

• How many patients on heparin have experienced a platelet count drop of 15% in the last 24 hrs?

• Which nurses have the most contact with patients who test positive for MRSA?

• How often does each resident internist ignore drug interaction alerts?

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Understanding Levels of Business Intelligence and Advanced Analytics

Analytics go beyond standard business intelligence capabilities and now draw on advanced analytics and predictive modeling techniques from efforts developed in various industries

What best practices or innovation should we

adopt? Evidence-based innovation

What outcomes should we expect? Predictive modeling

What patterns can we leverage? Information discovery

What if these trends continue? Forecasting / extrapolation

Why is this happening? Statistical analysis

Alerts What actions are needed?

Dashboards / benchmarking How are things going - relative to plan?

Drill down reports & analytic pathways Where exactly is the problem?

Ad hoc reports How many, how often, where?

Standard reports What happened?

Advanced

Analytics

Standard

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eti

tive

Ad

va

nta

ge

Source: Adapted from a graphic used in Tom Davenport’s Competing on Analytics

• Engagement of a 3rd party consultant to independently lay out roadmap for organizational governance and structure. fully engaged all leadership. 6/2011 – 4/2012

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Next: Outside Validation and Guidance.

OUTSIDE CONSULTANT FINDINGS

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• Significant number of able “analytics employees” (>35) are already in place/funded (just missing a few key skills (enterprise data architect/modeler; ETL/parser developers)

• Immediate opportunities exist to merge Core system teams and applicable data marts

• Further opportunities exist to consider leveraging non-Core systems; health systems need to increasingly be more payer-like (e.g., value based purchasing)

• Enterprise data governance is nascent, ad-hoc and informal contributing to some stakeholder data mistrust (ugly data syndrome)

DATA MANAGEMENT CHALLENGES UNCOVERED

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VCUHS CURRENT STATE ASSESSMENT

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TCO SUMMARY

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PRESENTED PROPOSAL TO SENIOR LEADERSHIP - 11/2012 - 1/2013

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• 2 tries required before endorsement

• DSS renamed EA with new VP at corporate level

• Commitment made to greatly expand staffing, consolidate siloes and improve technology

REORGANIZATION OF DSS TO ENTERPRISE ANALYTICS (EA) (AND ASSOCIATED GOVERNANCE)

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Data Governance Executive Committee

COO (Chair), CIO, CMIO and most operational VPs

Representatives from university, research and HMO

Data Management Committee (Additional Members TBD)

Abdoul Sousseh (chair) Jeremy Utz Jo Weller Lori Lynch Scott Wead Rusty Pitts Arthur Palmer Bonita Walker Melissa Wimmer Liz Locus David Summers

DM Task Groups: •Master Data management

•Data Dictionary/Metadata

•Data Stewardship/Ownership

•Data Integrity

Technical Advisory Panel:

Overarching Governance for Analytics Efforts

Overarching Data Governance Council & Process

Provider Side Virginia Premier VCU School of Medicine

This approach allows VCUHS to continue the current highly responsive delivery of information to departmental leadership while deploying the tools and teams to move eventually towards a single enterprise data warehouse

At the team level, each employee reports to a single manager to support departmental BI/Analytics needs while simultaneously being available for collaborative exchange of knowledge and joint solution efforts with other departmental BI/Analytics teams.

Team Level

MCVPGlobal Work (2)DSS (6)

CernerEDW (2) I2B2 (2)20

Overarching Data Governance Council & Process

ENRICHING EDW WITH CLINICAL EMR DATA 2013 - 2015

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• Achieved thru a direct OBDC connection (Oracle to Oracle table joins) from production EMR (remote hosted) to EDW

• Re-align EMR report writing specialists from EMR team to EA to provide clinical data SME badly needed by EA analysts

LEVERAGING EA AND EDW TO MEET MU 1 AND MU 2 REQUIREMENTS – 2012 - 2015

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• Because clinical data and billing data exist in disparate systems and because physician-patient relationship differs in both venues, the EDW was the only solution to merging the clinical data with billing based patient-physician identity

• Result was a highly effective and accurate physician performance dashboard out of the EDW that is still in use today

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FURTHER ENRICHING THE EDW WITH NEW DATA SOURCES 2016 - 2017

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• Have now (2016) begun to populate structured genomic test data (patient specific) into the EDW

• Began more extensive engagement with the research side of the health system to include helping lead a research EDW effort.

• Engage with the population health team to provide a targeted data mart derived from the EDW.

EAW Oracle DB

Cerner Standby

Cerner Millennium Production

GE HPA/MCVP IDX

Midas

Surginet

Enterprise Analytics Internal Data Sources

HPD

Pharmacy

HR - ULTI

INTRANET

VCC

Teletrackingand Transport

Lawson

Clinical

Billing

OR

Pathology

Registry

OHI

BIC

CTED/MDAS

Subject Matter Areas

Analytic Group Partnerships

Clinical Data Sources

Operational Data Sources

Outbound Connection

Inbound Data Extract

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UNOS

MATURING DATA GOVERNANCE PROCESS - TODAY

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• DG Committee meets monthly with broad exec participation as members. Chaired by COO.

• Sub-committees formed for data integrity, MDM etc.

• Extends beyond health system to Research and University community

Major IT Systems, e.g., Cerner, Lawson, IDX, etc.

Overarching Data Governance Council & Process

Enterprise Analytics Virginia Premier VCU School of Medicine

This approach allows VCUHS to continue the current highly responsive delivery of information to departmental leadership while deploying the tools and teams to move eventually towards a single enterprise data warehouse

At the team level, each employee reports to a single manager to support departmental BI/Analytics needs while simultaneously being available for collaborative exchange of knowledge and joint solution efforts with other departmental BI/Analytics teams.

Team Level

13 I2B2 (2)20

Overarching Data Governance Council & Process

Data Governance Executive Committee

Chief Operation Officer (Chair)

Data Management Committee

Data Architect(Co-Chair) ,

Director of Analytics(Co-Chair)

Data Steward Workgroup

Patient Access Systems Manager (Chair)

EDW Steering:

CIO (Co-Chair)

CRIO (Co-Chair)

DATA GOVERNANCE - STRUCTURE

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Data Governance

Documentation

Stewardship

Policy

Quality

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Charter- Data Governance Steering Committee

Purpose

Recognizing that data is a key organizational asset, the Data Governance Executive Steering Committee will provide guidance, direction and prioritization for the enterprise data governance program.

Scope

Set goals and priorities for the Data Management Committee

Communicate the importance of data governance as a priority for all IT projects

Remove barriers that might prevent data governance from being fully accepted by the organization

Sponsor initiatives designed to improve data quality and drive towards the enterprise goal of “One Source of Truth in Reporting”

Annually measure the performance of the Data Management Committee and the extent of organizational adoption of key principles

7 YEAR JOURNEY

34

2009-11

2012

2013

2014

2015

2016

Outside Consultant

Engaged an

outside firm with

deep expertise to

conduct an

assessment

(current state –

future needs) and

optional

recommendations

for moving

forward including

management and

governance

structures.

Educating my peers

Slowing building

the case thru 1:1

sessions with

over 20 leaders

to help them

understand the

current state and

future need and

how to close the

gaps

Approval of EA Effort

After two abortive

attempts, was able to

get the endorsement

of senior leadership

to elevate analytics,

consolidate siloes,

rename the service,

establish new

leadership, setup

data governance

structure, and

increase investment

in Analytics

Enriching the EDW

Began populating the

Oracle tables with

EMR data via direct

ODBC feed from

EMR production

node. Began transfer

of EMR

data/reporting

expertise from EMR

team to EA team

Current State

17 person EA team.

Director reporting to

corp VP of Strategy.

Data Architect, ETL

specialist, Tableau

visualization tool,

genomic data,

serving researchers

with clinical data.

Mature data

governance

Maturing Data Gov.

Brought in

outside experts in

data gov., and

healthcare

analytics to help

catalyze our

efforts.

Developed robust

committee

structure led by

COO with broad

representation.

Started sub

committees

focused on MDM

and data integrity.

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•Questions?