All SI Tues 240 Governance Panel Krista...

Governance Panel

Krista J. Casey, MBABusiness AdvisorPanel Moderator

©2015 McKesson Corporation. All rights reserved. Confidential and Proprietary.

Introduction - Panelists

Kevin Wacker, Manager, Decision SupportStrategic Financial Servicesg

Baylor Scott & White Health, Dallas, Texas

Beverly Griffith, Manager, Enterprise Analytics

Self Regional Healthcare, Greenwood, South Carolina

Tracy Anagnostis, Director, Decision Supporty g , , pp

Barnabas Health, West Orange, New Jersey

Baylor Scott & White

• Formed in October 2013 by a merger of Baylor o ed ctobe 0 3 by a e ge o ay oHealthcare System and Scott & White Health

• Largest Not for Profit System in Texasg y• Significant Programs in Transplant, Cancer,

Cardiology, Orthopedics and Research• System Owned Health Plan• Signed contract to implement Performance Analytics

across system in October 2014, scheduled final go-live in April 2016

Self Regional HealthcareSelf Regional Healthcare• Mission

Our hearts, hands and minds are leading our community to better health

• VisionThe care expertise and value we provide will be superior for all theThe care, expertise and value we provide will be superior for all the communities we are entrusted to serve

• PurposeAlways create the best experience

Self Regional HealthcareSelf Regional Healthcare• Comprehensive Healthcare Systemp y

– 358 bed acute care hospital– 22 Physician Practices (Family Healthcare and Specialists)

Located in Greenwood South Carolina– Located in Greenwood, South Carolina– Serving a rural, seven‐county region with a population of 250,000

• Major Service Lines That Are Marketedj– Spine Center– Orthopedic Center

Heart Center– Heart Center – Cancer Center– Women’s (Labor and Delivery) Services

System Statistics ServicesSystem Statistics • Largest Integrated Health System in NJ• Second largest employer in the State

Services New Jersey’s only certified burn treatment

facility World-class cardiac surgery services for

adults• Acute Care Facilities 7• Number of Beds 3,490• Annual Inpatient Visits 143,400• Births 18,500• Total Surgical Cases 68 800

adults The oldest, most experienced heart

transplant program in New Jersey ranked by volume in the top 10 in the nation

The only lung transplant program in New • Total Surgical Cases 68,800• Outpatient Visits 1,750,000• Emergency Room Visits 506,000• Hospital Employees 18,200• Physicians 5,800

Jersey Two kidney transplant centers which are in

the top 10 of 240 programs in the country One of the country’s most comprehensive

robotic surgery servicesrobotic surgery services Three certified Joint Centers of Excellence Six nationally certified Chest Pain Centers Accredited certified primary Stroke Centers Renowned neurology and neurosurgeryRenowned neurology and neurosurgery

program Comprehensive cancer services for adults

and children Nationally recognized geriatric services

• Once complete, the transaction will create New Jersey’s largest health care system and one of the largest in the nation -- to be known as RWJ B b H lthBarnabas Health.

Annual Inpatient Visits 260,000 • 11 Acute Care Hospitals• Outpatient Visits 2,000,000• Births 23,000• Emergency Room Visits 700,000• Hospital Employees 30,000• Physicians 9 000

• 3 Children’s Hospitals• A Pediatric Rehabilitation Hospital• A Behavioral Health Center• Ambulatory Care Centers• 5 Fitness & Wellness Centers• Physicians 9,000 • 5 Fitness & Wellness Centers

Question #1Question #1

How did your organization identify the need foridentify the need for

Data/Information Governance?

Response Question #1

• Environmental Needsv o e ta Needs– Effect of Merger – Common Definitions

• Statistics• Classifications (financial class, payor, service lines, etc...)• Procedures and processes (Financial Reporting, Intercompany

transactions, etc...)– Existing Issues

• Service line definitions• Rating agency requirements• Rating agency requirements• Surveys and reporting (AHA, etc...)


• Enhanced CapabilityEnhanced Capability– Integrated Data Warehouse (IDW)

• Common codingCommon coding• Single source of truth• Population Healthp• Comparative data

Response Question #1Response Question #1

• Fiscally strong organization for decadesFiscally strong organization for decades• Maintain strength

Ad i i t ti i d th i h i– Administration recognized the coming changes in healthcare, requiring a need to do things differently

T th i• To thrive– To become a highly reliable, data driven and

ffi i t i ti lli i i litefficient organization excelling in service, quality and safety


• To become the organization we wanted to beTo become the organization we wanted to be requires– Timely and trusted data– Timely and trusted data – Data to assist leaders in making decisions aligned with organizational goalwith organizational goal


T li ith l t i t d


• To ensure compliance with regulatory requirements around PHI– Set process standards on how data is created– How data is used– Data retention and disposal

• Create a single source for corporate reporting– Standardized definitions of key metrics– Reduce redundancies and minimize duplication of efforts

• Create infrastructure for Quality improvementCreate infrastructure for Quality improvement– Predictive Analytics– Population Health management

Response Question #1Response Question #1• To ensure compliance with regulatory requirements

d PHIaround PHI– Set process standards on how data is created– How data is used– Data retention and disposal

• Create a single source for corporate reportingSt d di d d fi iti f k t i– Standardized definitions of key metrics

– Reduce redundancies and minimize duplication of efforts

• Create infrastructure for Quality improvementy p– Predictive Analytics– Population Health management

Response Question #1Response Question #1Goal to move analytics towards optimizationy p

Adapted from Competing on Analytics, Davenport and Harris, 2007

Copyright © 2013 IIA All Rights Reserved


How did your organization begin the Data/Informationbegin the Data/Information

Governance journey?


• Focused on Integrated Data Warehouse designFocused on Integrated Data Warehouse design– Create reportable data (standard coding)

Single location for definitions– Single location for definitions– Feed to newly implemented systems (Performance

Analytics etc )Analytics, etc...)– Eliminate rogue, undocumented systems and

definitionsdefinitions– Governance as gatekeepers


• Identified what was wrong with how we wereIdentified what was wrong with how we were doing data– No single source of truth– No single source of truth– No common definition of termsData shopping was rampant– Data shopping was rampant

– Data massaging was the normN t d d id li– No standards or guidelines

– Not seen as a tool

Response Question #2Response Question #2• Formation of an Analytics Steering Committee and Work Group

Response Question #2Response Question #2Analytics Work Group Objectivesy p j• Conduct a comprehensive assessment of the current

analytics environment across the system, identifying all data sources types of data reported who is using the data and forsources, types of data reported, who is using the data, and for what purposes.

• Identify common tools and technology and make d ti f t d di tirecommendations for standardization.

• Identify key Data Analyst across the system to identify best practices, leverage existing resources and drive common data definitions.

• Assist in the evaluation of future Enterprise solutions and provide recommendations to Analytics Steering Committeeprovide recommendations to Analytics Steering Committee

Question #3Question #3What is your established

t t f D t /I f tistructure for Data/Information Governance?

H did d idHow did you decide on your structure for

Data/Information Governance?


• Governance tied to IDW designGovernance tied to IDW design– Significant stakeholders as team members

Scope limited– Scope limited• Definitions• Minimize custom additionsMinimize custom additions• Service Line definitions a separate team

– Subject matter experts drive normalizations, j p ,governance as police


GGovernance

Executive Sponsor(s)Strategic Oversight

Enterprise Analytics Steering Committee

Enterprise Analytics LeadershipTactical Oversight

Enterprise Analytics Analysts

Data Analysts Knowledge Group

Existing Technology SupportTactical Execution


ResponsibilitiesResponsibilitiesKnowledge Group Steering Committee

• Communicate the data analyses they perform

• Communicate their priorities

• Learn about what other analysts across the

• Communicate organizational goals and objectives to the Enterprise Analytics Team and the Enterprise Analytics Knowledge Group

• Resolve any conflicts around data sources and definitions d i i d l i

yorganization are doing and how they are doing it

• Receive assistance and/or additional training or education

• Understand the organization’s objectives and goals

used in reporting and analysis

• Prioritize competing project requests

• Address project requests that are not aligned with organizational goals and objectives

• Identify and address risks to the success of the analytics Understand the organization s objectives and goals and how they impact data analysis efforts

• Learn and share best practices for data extraction, analysis, and reporting

• L d h b i i d l

Identify and address risks to the success of the analytics program

• Promote and enforce standards, policies, and procedures established around data reporting and analysis

• Promote and insist on the use of data to support informed • Learn and share about existing and new regulatory

reporting requirementsbusiness decisions, the establishment of goals and objectives, and the identification of continuous improvement opportunities


• Steering Committee went through a multipleSteering Committee went through a multiple versions

• Settled on current membership and role• Settled on current membership and role• Created the Analytics Knowledge Group

– Establish and maintain a connection to the team members who interacted with data regularly

Response Question #3Response Question #3• Guiding Principles

– Corporate level program – Enterprise process owners drive requirements and “own”

the data and systems that support their processesthe data and systems that support their processes– Pursue best practices to mature capability– Drive common data definitions and shared use – Leverage existing resources, define and advocate

additional level of capability, and establish an analytic communityy

– Leverage existing assets and identify and deploy common tools and technologyAli il t d ith t t i i iti ti– Align pilots and programs with strategic initiatives

Response Question #3• Identified 2 distinct areas of analytics


– Direct Patient Care• Real time, clinical and quality data to assess and improve

patient outcomes– Business Operations

• Financial Analytics, Regulatory reporting and Information Technology and SecurityTechnology and Security

• Representation from all areas• Chief Medical Informatics Officer• Chief Information Officer

• VP, Case Management and HIM• VP QualityChief Information Officer

• VP, Academic Affairs, Research• Chief Pharmacy Officer• VP, Clinical Effectiveness• Chief Medical Officer

VP, Quality• Director, Decision Support• SVP, Financial Management• SVP, Human Resources

Chief Medical Officer


How did your organization gain buy in for utilization ofbuy-in for utilization of

Data/Information governance and an analytics culture?an analytics culture?


• IDW Development a Given (also a very largeIDW Development a Given (also a very large investment)

• Realities of cross division reporting makes• Realities of cross division reporting makes everyone a championD i f• Driven from top

• Corporate culture to be team members


• We had to earn it!e ad to ea t!• Biggest challenge from the start was that no one trusted data

• Many situations of data massaging and mis‐interpretation

• Data could say whatever someone wanted• Began with one simple project • Data owners involved from initial concept to validation to presentation

Response Question #4Response Question #4• Alignment with system strategic goalsg y g g

– Population Management– Normalization of data across system to enable

decision making– Cost reduction

• Minimize redundancies of technology• Minimize redundancies of technology

– Efficiency• Reduce duplication of efforts

– Support system expansion• Integration of Jersey City Medical Center

F t ith RWJ• Future merger with RWJ

Question #5Do you have any measures th t d t d t ithat are used to determine success of data/information

governance?

What outcomes have you seen?


• Movement of structured analyses to central dataMovement of structured analyses to central data sources

• Significant reduction in rogue reports and• Significant reduction in rogue reports and processesI d i d i d i l i f• Incorporated into design and implementation of new applications

• Standard Governance processes being developed


Numbers for the past 12 monthsNumbers for the past 12 months• 83 data subsets

6 hi hli h• 165 highlights• 58 scorecards• 29 dashboards• 48 active users48 active users


Stories• Numerous cheerleaders throughout the organization at

all levels of leadership• LEAN Director refuses to conduct an A3 or RPIW without• LEAN Director refuses to conduct an A3 or RPIW without

an EA representative• Identified improvement opportunities in EVERY area

touchedtouched• Allowed organization to save >$250K annually in data

services• Departments excited about seeing their data• Front line team members taking responsibility for quality

of dataof data

Response Question #5Response Question #5• Data governance model is still a work in progress• Due to the recent announcement with RWJ, many

technology initiatives put on hold

Success Measures

Initiative Measure Outcome

CostSelf Regional – reduction in data services organization to save

$250,000 saved annually

QualityBaylor Scott & White -Report generation and report quality

Significant reduction in rogue reports

UtilizationAll – Business Intelligence utilization and accountability

Automatically generating reports used to reduce

manual reporting

EfficiencyBarnabas – Coordination of reporting (data shopping)

Reduced duplication of effortsg)

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