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FALLSUMMIT
2019
The State of Healthcare Analytics
Lee Pierce
Sirius Healthcare, Chief Data Officer
FALLSUMMIT
2019
Our Capabilities
SECUREit all
MANAGEyour operations
OPTIMIZEyour IT
TRANSFORMyour business
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2019
Data & Analytics At a
GlanceFounded in 2005Acquired as a division of Sirius Computer Solutions in 2014
70+ Data Management Systems (DMS) and Advanced Analytics ConsultantsConsultants average nearly 14 years experience in DMS & Advanced Analytics
Delivering a full portfolio of analytic consulting servicesDeep technology experience across most major analytics & DMS
Diverse client base across industriesAutomotive, eBusiness, Financial, Hospitality, Healthcare, Pharma, Retail, Telecom & more
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2019
Areas Of Focus For Sirius Healthcare
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2019
Healthcare Trends We’re Seeing
The shift from fee-for-service to
fee-for-value continues.
Innovation in some form is a requirement.
People engagement and experience is a significant
strategy.
Collaboration / partnering / M&A’s the new normal.
Population health will continue as efforts in patient engagement and experience regardless of changes to the
law.
Making actionable insights out of data is now a focus.
IoT continues to grow to be a conversation without a clear
strategy for care or the IT organization.
Security continues to be a top priority and significant spend.
The cost of running critical
EHR applications is overwhelming. Organizations are seeking
strategies to control cost and reduce complexity.
FALLSUMMIT
2019
Current State of Analytics in: Baseball
FALLSUMMIT
2019
Baseball Data & Statistics
• Henry Chadwick 1859• First baseball
box score
• Sabermetrics • “The search for objective
knowledge about baseball”
• “SABR” = Society for American Baseball Research
• Bill James – baseball writer, historian, statistician
FALLSUMMIT
2019
FALLSUMMIT
2019
mlb.com/glossary
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2019
StatCast
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2019
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2019
Sabermetrics application in Healthcare?
• Sabermetrics = ? in Healthcare
• Glossary of terms / metrics –• Why is this so difficult in Healthcare?
• Consistent presentation, delivery of data • Baseball cards = ? in Healthcare (patient, provider
data metric summaries)
• StatCast for Healthcare? • Realtime, streaming, useful
FALLSUMMIT
2019
Current State of Analytics in:Healthcare
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2019
Healthcare’s Most Promising Tech?
May 3, 2019 – Becker’s Hospital Review
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2019
Quotes
• “Probably the single most important technological advancement that has the greatest impact to transform healthcare is data. “Intelligence” informed by data is the most important technological advancement with the biggest potential to transform healthcare”
- Chief Digital Officer, Novant Health
• “The technology I feel like has the biggest opportunity for transformation in care delivery is big data — in conjunction with AI and machine learning we can lower cost of care while significantly improving care delivery and outcomes.”
– CIO at Providence St. Joseph Health
FALLSUMMIT
2019
Quotes
• “Another area that we think is immensely important — and one of the first applicable opportunities —is AI for imaging.”
– CMIO at WVU Medicine
• The application of AI, machine learning and deep learning in the healthcare environment holds great promise for patient care… The effective and ethical implementation of these technologies has the potential to give care providers the tools they need to significantly improve care delivery.
- Exec Director of IS Security and Compliance, Indiana University Health
FALLSUMMIT
2019
WSJ Headline Yesterday
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2019
Healthcare Analytics Maturity
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2019
Healthcare Analytics Maturity
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2019
From Data to Value
Technology
Process
People Business and Clinical Outcomes and Value
FALLSUMMIT
2019
Current State of “People” in Healthcare Analytics
•
Technology
Process
People
FALLSUMMIT
2019
Who?
• Executive Sponsor
• Clinical and Operational Leaders
• Clinicians
• Data/Analytics Leader(s)
• Data Engineers
• Data Analysts
• Data Stewards
FALLSUMMIT
2019
Leadership / ExecutivesLeadership engagement in analytics makes a significant difference in value realized from Data/Analytics
• Most want data/analytics -- some do not (I’ve heard it myself from multiple leaders)
• Most desire to be data-driven -- but few know how to be
• Some know how to ask questions that lead from data to insight -- most do not
• A few have the discipline and know-how to use the insights to make decisions and change processes -- most do not
• Some organizations have a designated executive to oversee data and analytics -- some do not
• Some health systems are hiring Chief Data Officers/Chief Analytics Officers, to designate a clear leader for these efforts -- many have not, yet
FALLSUMMIT
2019
Technical Resources• Organizations are underinvesting in analytics resources
• Organizations are not effectively using analytics resources
• Data architects, ETL developers -> Data Engineers
• Less reliance on traditional DBA (database administrator) resources (due to built for cloud, modern RDBMS’s)
• Data Analysts • Technical, Analytical, and Domain knowledge/skills
• Primary producers of analytic solutions
• Need more rather than less
• Data Stewards• Business knowledge first, data/technical second
FALLSUMMIT
2019
Analytic Organizational Structure
FALLSUMMIT
2019
Current State of “Process” in Healthcare Analytics
•
Technology
Process
People
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2019
Healthcare Data Value Cycle
DataHealthcare
Operations,
Patient Care
Action
Analyze,
Insight
Analytics Program Governance
Integrate,
Organize
Data Governance
Value
FALLSUMMIT
2019
Data Governance vs. Analytics (Program) Governance
• Data Governance
• Data stewardship and ownership
• Business engagement in data
• Data quality management
• Metadata management
• Business Glossary
• Master and reference data management
• Information lifecycle management
** More important than ever! **
• Analytics Governance
• Definition and oversite of data and analytics strategy
• Building an analytics culture
• Analytics organizational structure
• Data literacy efforts
• Data and analytics investment/funding
• Measurement of analytics ROI
• Prioritization of analytics projects
** More critical than ever! **
FALLSUMMIT
2019
Data Literacy
• Ability to read, understand, create, communicate data as information
• Competencies involved in working with data, at all levels of “people” in the data -> insight processes
• Large variation in analytics maturity within organizations
• Data Science: How to ask and answer questions, better, with data (Dr. Jason Jones)
• Need to establish a company training program for improving data literacy (from executives to individual contributors/decision makers)
• Without data literacy, is difficult to put analytics into production
FALLSUMMIT
2019
From Question to Value• What is the clinical/business process you are trying to change/improve?
• What is the clinical or business process you are trying to change by having this analytic request provided? What is the business/clinical question(s) you are trying to answer? (Business Reason for Request)
• What is your data/analytic need?• What is your desired deliverable? Please describe your request. Provide documentation if available. (Description of
Request)
• Who will use the analytic solution/insight, and how?• Who is the intended user/audience for the data or insights created as part of this request? Who will use the
data/analytic solution to make a business/clinical decision? How will the insight be operationalized?
• What is the intended benefit or ROI? • How will the data, report, analytic solution benefit patients or our organization? What is the expected return on
investment (ROI) for this request being completed, if better decisions are made or business/clinical processes are changed as described in #1?
-- Based on learnings from Dr. Brent James’ Process Improvement/ATP Course
FALLSUMMIT
2019
Current State of “Technology” in Healthcare Analytics
Technology
Process
People
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2019
Technology Observations
• Orgs still struggling with fundamental data issues
• Too much reliance on “technology” at the expense of not paying attention to “people and process”
• People and process investment is just as if not more important
• Over-reliance on EMR-vendor provided analytic solutions• Some EMR vendors are making good progress in data/analytics, others are not
• “And” not “Or”• There is no Easy Button. Is a combination of tools/solutions together to meet the technology needs.
• Too many “packaged analytic” solutions – encourages siloed analytics decisions, need an enterprise focus.
• But some very good progress by a few healthcare specific data/analytics vendors
• Need to learn from other industries • Reference architecture for data/analytics across industries
• Cloud, streaming, data virtualization
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2019
Healthcare Analytics Framework
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2019
What is Driving the Change in Approach to Analytics?
HeterogeneityYour source environment is growing more complex, not less.
High
Rapid change isn’t coming. It’s here.
Expected Time-to-Value
Decision makers expect value now.
Insight Ubiquity
Omnipresence of insight is required for evolution to cognitive analytics.
Machine Data & IoT/IoEMachine data streaming is generating petabyte-scale data in real time.
A
B
C
D
E
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2019
Past, Present, & Emerging Future
Reporting 1.0
Spreadmarts
Lots of untraceable copies
Tightly coupled data & format
Not ideal, but the best we had
Slow due to lots of redundant labor
Business Intelligence 2.0
Datamarts, OLAP cubes, semantic layers
Highly curated
Expensive, tightly coupled proprietary environments
Capability in the hands of the specialist
Slow to add new sources because of multiple transformations
Analytics 3.0
In-memory, columnar
Open & interoperable technologies
Innovative combinations of sources, visuals, experiences, observations
Fail fast, throw-away, dis-intermediation
Success dependent on aculture of discipline
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2019
Data and Analytics Conceptual Reference Architecture
FALLSUMMIT
2019
Data and Analytics Reference Architecture -Top down View
QUEUE
SOURCE
RAW
INGEST
GOLD
PUBLISH
STAGE
ODS
DATA MART
TRANSFORM
INSIGHTS
COPY of the EVENTDATA
TIME STAMPED EVENT DATA
Stream Analytics
SpatialAnalytics
Location Analytics
Operational ReportingData Sharing
Notification
Alerting
Visual Analytics
Organizational Health/KPI
Data ExplorationArtificial
Intelligence/ML
Filtering
Time Series Analytics
Mobile
DATA
Audit & Logging
Data Governance / Data Quality
Master Data Management
Security
Data Transmission
Data Virtualization
Data ScienceDashboarding
