Session #9

Agile Analytics: The Key to Improving Everything from Surgical Services to Genomic Personalized Medicine

S. Mark Poler, MDPhysician Informaticist for Enterprise Data Strategy,

Division of Informatics, Geisinger Health System Vice Chairman, Geisinger Health System Anesthesiology

Learning Objectives

• Identify technically and financially advantageous options to replace traditional data systems.

• Illustrate elements of agile healthcare transformation with examples from the surgical suite.

• Define the expansive requirements for non-structured data and genomic-based precision medicine.

• Recognize the role and utility of integrated healthcare information and agile analytics for innovation.

Provision of surgical care is one of the most complex and

expensive of human endeavors.

Geisinger Health believes it must provide its patients with high-quality, safe, and cost-

effective surgical care supported by well-integrated

data and analytics.

Background

Over 50% of health system costs and

revenues are related to surgical

procedures.

More than forty-five million procedures are performed each year.

Variation at least four to five-fold across hospital referral regions.

Nationally: How Big Is the Challenge?

Poll Question #1How effectively do your information systems minimize costs and optimize outcomes (clinical, patient satisfaction, staff engagement, revenue contribution) for surgical operating rooms?

1) Not effective2) Somewhat effective3) Moderately effective4) Very effective5) We do not have such capability6) Unsure or not applicable

Geisinger Health An Integrated Health Service Organization

• Mostly rural and small cities.• PA• NJ: AtlantiCare

• 13 Hospitals.• 3 Level I trauma centers.• 81 community practice sites.• 8 outpatient surgery centers.• 2 nursing homes.• Home health & hospice services covering

25 counties in PA and 3 counties in NJ.• >138K admissions, OBS & SORUs.• 2,663 licensed inpatient beds.

• Multispecialty group.• ~1,300 physician FTEs.• ~790 advanced practitioners.• ~215 primary & specialty clinic sites.• ~3.4 million outpatient visits. • ~520 resident & fellow FTEs.

• Geisinger Commonwealth School of Medicine.

~365 medical students.

• Research: basic, health systems and population health, genomics.

• ~500,000 members.• ~84,000 Medicare Advantage.• ~153,000 Medicaid.

• Diversified products offered on public & private exchanges.

• 45 PA counties.• 5 states.

• ~56,000 contracted providers & facilities.

Moody’s Aa2/Stable Standard & Poor’s AA/Stable

Provider Facilities$3,147M

Physician Practice Group$1,130M

Managed Care Companies$2,395M

>100K procedures/year~45K surgeries with anesthesia/year

www.geisinger.org

Turning PointsTransitional opportunities• Newer technologies.

• Versatility.• Fewer limitations.

• Unstructured data (e.g., natural language processing [NLP]).• Larger scope (search, genomics, data science).

Cost and performance• Storage demand and cost (proprietary vs. commodity).• Modular upgrades (CPU, RAM, storage).

Surgical arena: largest risk and opportunity• Resource management.

• Cost versus revenue.• People, material, and time.

Results

Significant stakeholder

engagement, triggered by visual

presentation of data.

Substantial cost savings realized

through new approach to

analytics.

New analytics capability vastly more efficient, flexible, and

powerful.

Detailed information. Drill-down to

individual cases.

Beyond opinions…changes conversations.

Enterprise data warehouse (EDW)

storage: $2K as opposed to $50K

per terabyte.

Near real time access to data—continually

updated.

Search over 200M clinical notes (two decades) in 5

seconds!

Improvement.

Innovation.

Finance.

Operating room (OR) operations:

Decreased cost variation.Improved time utilization.

How Geisinger Achieved Its Results

12

3

45

Installed next generation hardware and software replacements for EDW:

achieved data integration, vendor independence. Employed analytics in

education, awareness, and engagement.

Changed approach to governance.

Replaced and improved existing analytics capabilities. (Supported and migrated legacy systems.)

Executed systemwide integration of enterprise

information management.

Replace and Improve Existing Analytics CapabilitiesTimeline

EHR Reporting Datbazse

Milestones

CDIS Business

Plan Endorsed

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

CDIS IT Formed

CDIS Go-Live

Research Data BrokerProcedures

Begin

Analytic Direct Access

Begins

Transition to EHR

Reporting Database

360 Degree Patient-Centric

View

Interactive BI Begins

Leadership Dashboard Released

Unstructured Data Analytics

Begin

Analytics Hardware Platform Go-Live

CDIS Needs Assessment

Endorsed

CDIS PMO Established Enterprise

Data Model PoC

EDS Steering Comte

Chartered

Big Data Platform Go-Live

CDIS User Community

• Data at Go-Liveo EHR Clinical Orderso ADT and Schedulingo Physician & Hospital Billingo Clinical Enterprise Costingo Net Revenueo GHP Medical & Rx Claims

• HR System • COPD and Asthma

• Patient Satisfaction

• Cardiology

• Patient Entered Data

• Referrals

• Quality Advisor

• Oncology

Early CDISPlanning & Implementation

EHR Reporting Database

CDIS 1.0 CDIS 1.1 CDIS 2.0 CDIS 3.0

Data Sources

Advantages of HadoopAn Open-sourced Late-binding Software Framework

Less expensive due to

commodity hardware.

Frequent and faster data ingestion.

Supports multiple views

of the data.

Accepts structured and unstructured

data.

Able to use open source and proprietary

analytic tools.

Commodity hardware

allows easy expansion by nodes.

Data integration:de-siloed

“data lake.”

The Role of Analytics

Integration of diverse data from incompatible sources to support analysis across clinical and business domains.

Key Design Criteria. • Larger capacity, faster, legacy (SQL), and NoSQL.

• Integrate diverse data types, including NLP.

• Support transparency, access by operational users.

• Be vendor independent.

• Support rapid and massive expansion including genomic data. • Full exomes linked to 20 years of electronic health records.

Key Design Criteria:

Production Footprint Traditional vs. Open Source vs. Hybrid

• Original cost.• Replacement cost.• Implementation cost.• Incremental nodes.• Incremental storage.• Cloud storage.

Consider and Compare:

Major Data Source Comparison

• EHR clinical data.• Financial.• Claims. • Pulmonary.• Pathology.• Oncology.

All EDW sources, plus:• EHR data x10.• Cardiology.• RIS (Radiology).• Lab and Microbiology.• Supply chain.• Phone systems.• Mobile message delivery logs.• IT, e.g., wireless AP IDs.• PA Health Info Exchange.• Text analytics.• And more…

TRADITIONAL EDW HADOOP

Big Data Architecture

Cross platform

SQL query tool

Data visualization tool

Open source enterprise

search platform

Data system 1

Data system 2

Data system 3

Data system 4 Other

InternalHIE

Rapid Storage and Hadoop Growth

0

5000

10000

15000

20000

25000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

TeraBytes

Year

Storage Growth2006 - 2018

Hadoop System Growth

Genomic(Projection,

Cloud)

Non-Genomic(local, SSD)

Nodes & RAM (TB) Storage (TB)

2015 2016 2017Nodes 8 23 70RAM (TB) 2 9.5 31.25

01020304050607080

Year

2015 2016 2017Storage (TB) 100 300 1100

0200400600800

10001200

Year

Poll Question #2

How is your organization managing genomic data for personalized medicine?

a) We have not started or we are just starting to think about itb) We have started implementationc) We have a small-scale implementation (terabytes)d) We have a large-scale implementation (petabytes)e) We had no idea about the magnitude of requirementsf) Unsure or not applicable

Enterprise Information Management and Governance

• Enable agility, tracking, and flexibility to support rapid improvement and innovation.

• Process.

• Democratize data (hub and spokes).

• Use existing data better!

Data Management

BI & Analytics

Data Governance

Education

Clinical Innovation

GHP*

Admin/HRWorkforce

Research*

Finance

Spoke Project List

Spoke Project List

Spoke Project List

Spoke Project List

Spoke Project List

Master Project List

* EDS Guidance

SCRUM 1

SCRUM 3

SCRUM 2

SCRUM 1

QualitySpoke Project List

Spoke Project List

Spoke Project List

Market Strategy& DevelopmentSpoke Project List

Analytics Center of Excellence (CoE) FrameworkSupporting Process Improvement

CE

GHP

ClinicalOperations(Institutes)

Education, Awareness, and EngagementIn This Context

Engage all interested parties and embrace grassroots approaches.• Show data in unfamiliar ways; open new vistas.• Reveal formerly veiled concepts.

• Make tools and data freely available.• Invite exploration.• Expect insights.

“Given enough eyeballs, all bugs are shallow.”

Source: Raymond, E. (1997). The Cathedral and the Bazaar. Retrieved from https://archive.org/details/CathedralAndTheBazaar

Example: OR Engagement

New analytic visualizations were

conceived, designed, and

implemented over ~ four months.

Initial presentations to physicians,

administrators, and other staff.

As new analytics capabilities were put

into place, those involved in surgical

services were educated about the

approach and concept and

feedback was sought.

This approach proved to be a

powerful engagement

process across disciplines.

Big Picture Surgical Overview to Details

Video-“RetrospectOR”

Implementation of OR Dashboards—1 Month

Old School

vs. New

School

Griffin

At a restaurant, you know how much your meal cost, why not procedures?

Video-“Check Please!”

A small, talented team can be very

agile.

Synthesis of the How

Enterprise Data Management needs to be engaged in support and transition.

Innovative data management and

analytics are necessary.

New tools and techniques require new

work paradigms.

Lessons Learned

Hadoop isversatile.

Pictures are worth more than

1000 words! Aha! Generic

hardware and software can

replace proprietary.

Integrated data enables new

analytic insights. Daily huddles

expose issues not captured in standard data.

• Structured SQL is accommodated —unstructured data as well.

• Traditional skills are necessary, plus new skills.

• Illustrate experiences staff and leadership can relate to, like timelines and cost variance.

• Proactively report whatever needs improvement.

• Costs about one-tenth the anticipated proprietary next-generation.

• Equivalent or better performance, with some translational challenges.

• Easier incremental upgrades (CPU and storage).

A Cycle of Innovation for Viability

12

3

45

Innovate: do differently.

Financial margin: despite decreased reimbursement.

Financial viability: opportunity to repeat.

Creativity: new approaches enabled by innovation.

Do it again!

Future Plans for Surgical Services

• Replace opinion with data.• Open access to data.• Engagement of insights,

bottom-up and top-down.• Continued process improvements.• Staff engagement, feedback.• Continue integration of data sources.• Real-time location system (RTLS)

integration.

• Provide immediate post-procedure feedback to clinicians:• Usage of supplies, anesthetics,

pharmaceuticals.• Improved data quality and scope.

• Incorporate clinical outcomes into assessment of value.

• Expand to other clinical domains.

Next steps? Where do we want to be in a year or two?

Thank You