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© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-1
Internal Business Consulting and Management Engineering
Systems & ProceduresInternal Business Consulting and Management Engineering
Systems & ProceduresStrategic Research: Analytics ExcellenceAlissa CornellSr. Health Systems Engineer, Mayo Clinic
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-2
Agenda
• Learning Objectives
• Background
• Mayo Clinic Systems & Procedures
• Strategic Research: Analytics Excellence• Project goal and scope• Trends, Survey, Best Practices• Practical Deliverables• Results
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Learning Objectives
• Generate practical, realistic tactics to define an analytics space, maturity and value for an organization
• Gain practical insights for the design and implementation of a business-centered, coordinated analytics vision
• Highlight the key success factors for leveraging analytics and engineering to address the challenges in health care
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-4
Background
• Mayo Clinic is the first and largest not-for-profit academic medical center in the US
• More than one million patients from 150 countries are seen each year, covering virtually every specialty and subspecialty
• Excellence in patient experience at Mayo Clinic is supported through substantial research and education programs
• Mayo leadership strongly believes and continues to build its systems, process and management engineering infrastructure as a vital component in addressing the formidable challenges in healthcare
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-5
Background
• In 1947, Mayo created the Division of Systems and Procedures (S&P)
"[The Division] will devote full time to an analysis of all present methods of record-keeping and storage, ordering and reporting tests,
and clinic-hospital functions, with the ultimate purpose of simplification of procedures and more efficient operation of the
routines employed."
• Today, over 500 engineers are employed enterprise wide • Over 250 specialize in applying engineering, advanced
analytics and operations research to a variety of organizational functions
• The integration of engineering and analytics with core healthcare service lines is a key contributing factor to Mayo’s sustained excellence and market differentiation
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-6
Mayo Clinic Systems & Procedures (S&P)
Mayo Clinic employs > 500
Engineers enterprise wide
> 250 Specialized application
engineering, advanced analytics
& Operations Research
Systems & Procedures
Mayo Clinic’s internal business consulting &
management engineering team
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-7
S&P Strategic Research Team Objectives
• Develop a high-performing research capability that adds significant value to the institution
• Discover and create innovative approaches for transforming healthcare delivery
• Develop proposals ready for advocacy and translation
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SRT Research Methodology
Patients Providers
Support Staff External Forces
Strategic Question or Future Scenario
Trends, Survey,
Best Practices
Insights, Infra-
structure, Process Options
White Paper or Proposal
for Approval
Testi
ng
Testi
ngTesting
How can Mayo improve infrastructure and processes over next 2-5 years to meet changing needs?
Understand external and internal trends, best practices, survey/interviews, analysis, strategic thinking.
Identify infrastructure and process options, synergies, integration, quality and efficiency improvement.
Debate and reach consensus on research insights, white paper or proposal approval.
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-9
Strategic Research Analytics Excellence
Strategic Question or Future Scenario
How can Mayo improve infrastructure and processes over next 2-5 years to meet changing needs?
How should Mayo Clinic resource and organize for analytics to support clinical, administrative
and operational, research, and strategic decision-making, including point-of-care tools?
a coordinated and integrated analytics vision for Mayo Clinic as well as a roadmap to achieve that
vision
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-11
Analytics Drivers*
Demand for data across the enterprise is increasing in frequency
Primary and secondary uses of data are blurring
Multi-domains and cross-functional views of data are increasing
Mayo must have stronger population analytics to compete
Demand for cost management & resource optimization is increasing
Demand for point-of-action analytics within workflows is increasing
Competition is getting closer, demand for performance is increasing
Time to act and make decisions is decreasing
Health care payment model is changing
VALUE = Outcomes + Safety + Service Cost
Our pay will be based on:
*Adapted from Information Management & Analytics Plan 11/12/13
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-12
Trends, Surveys, Best Practices
Advisory Board Company
Deloitte Gartner HIMSS Analytics Microsoft
IBMHealthLeaders
SASInternational Institute for Analytics
Cedars-Sinai Medical Center
Henry Ford Health System
Massachusetts General Hospital
Medtronic Teradata Corp
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Trends, Surveys, Best Practices
Practice• Office of Population
Health Management• Neurosurgery-
Administration• Mayo Clinic Health
System• Colorectal Surgery –
MTR
Research/Education• Biostatistics &
Informatics• Center for the Science
of Health Care Delivery• Center for
Individualized Medicine• Education
Support• Marketing• Public Affairs• Global Business
Services• Center for Social
Media• Supply Chain• Office of Access
Management
Support• Systems & Procedures
(FL/RST)• Planning Services
(AZ/RST)• Enterprise Analytics• Quality Academy• Clinical Quality• Human Resources
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-14
Insights from the Research1. Analytics involves all of Mayo Clinic and goes beyond IT
2. People think about analytics from within their silo because work gets done there
3. Lack of standardized, accessible data is impeding analytics. However, lack of a tool is not an excuse for waiting, and not every decision requires scientific rigor
4. Think bold, start small. Do not interrupt or slow down current work or create another layer of internal hurdles
5. Data and analytics are not a replacement for domain knowledge and critical thinking
6. Business acumen is the more difficult of the analytics skill set to obtain and should be a selection criteria; technical & mathematical skills can be taught
7. External organizations have modified their analytics strategy as they go; what actually emerged is different from their original design
8. The organization must value analytics, must see business analytics as integrated with clinical analytics, and must establish governance at an organization-wide high level
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-15
Proposed Mayo Clinic Analytics Vision
Mayo Clinic patients, clinicians, researchers, educators, management, and allied health staff use methods and data to take actions that sustain and grow Mayo Clinic to meet evolving patient needs
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-16
Mayo Clinic Definition of Analytics
Analytics is the use of methods and data to take actions that sustain and grow Mayo Clinic to meet evolving patient needs.
Includes:
• Clinical, administrative/operational, research/educational, and strategic actions and decisions
• People and work processes; data sourcing, preparation and repositories; mathematical tools and visualization; area-specific decision-making and action
• Timely, significant results that achieve objectives, approaching prediction and real-time action
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Business Intelligence, Informatics, Data MiningThree closely-related dimensions of the Analytics Action Space
Descriptive (Retrospective)
Predictive (Forecasting)
Prescriptive (Discovery)
Monitoring & Improving Performance
Examples:•Reporting:•Quality•Supply Chain•Human Resources•Customer Satisfaction
•Operational Dashboards•Market Segmentation •Competitive Analysis
Delivering New Insight and Capabilities
Examples:•Clinical Care Pathways•Population Health Mgmt•Disease Mgmt•Health Outcomes Research•Financial Forecasting•Staffing Forecasting•Facilities Requirements•Environmental Scanning
Delivering New Tools & Products
Examples:•Genomics Research•Electronic Data Security •Social Media Analytics
Business Intelligence
Informatics
Data Mining
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-18
Mayo Clinic Analytics Maturity Model:Increasingly Powerful Uses of Analytics Methods and Data
*Adapted from International Institute for Analytics and Health Catalyst Analytics Adoption Models
Implementing vendor and internally developed solutions• Electronic Medical Record• Revenue Cycle Management• Data Standards & Data Integration
Efficient, Consistent, Meaningful Reporting• Quality, Compliance• Operational Dashboards
Leveraging data to improve processes and decisions
• Care Pathway Adherence
• Individual Patient Care Improvement
Proactively managing risk
• Clinical risk intervention
• Quality Analytics• Financial forecasting• Population
Management
Contracting for and managing health
• Health Outcomes Research
• Genomic Research
• Social Media Analytics
Infrastructure Deployment
BusinessIntelligence
Clinical Effectiveness
Predictive & Suggestive Analytics
Prescriptive Analytics
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-19
PATIENTS
Area
Goal
Objective
Decision-makers
Tools/Systems
Partners/Relationships
User Experience
Display Frequency
Mathematics
Benchmarks/Compare
Information Accuracy
Rules
Data Access and Selection
Data Repository
Data Recency and Retention
Data Preparation
Data Structure
Data Source Systems
Work Processes
Key Roles, Skills
• Analytics is not one thing
• Analytics is not two or three dimensional, it has many dimensions
• Like the familiar supply chain, Analytics can be pictured as a Value Chain, with patients at the top.
Mayo Clinic Analytics Value Chain MAYO CLINIC ANALYTICS VALUE CHAIN -- Example: CIM Research
Goal:RUN
Operations; Monitor & Improve
GROWWork Processes, Uses
of Data & Technologies
TRANSFORMRelationships,
Services, Solutions, Products
Area: ClinicalAdministrative/Operational
Research/Education Strategic
Objective:e.g., Reduce Patient Wait Time
Design care pathwaysIdentify population risk
Find a new clinical test
DECISION-MAKERS: Patients Clinicians Researchers (PI) Educators Managers Allied Health
User Experience: e.g., Video Image Animation Plots, e.g.Geo Charts Rows/columns Raw data
Display Frequency: One-time On-Demand Scheduled Continuous
TOOLS/SYSTEMS: e.g., ExcelSAS, STATA, SPSSSDMS, Red Cap, RAVE
Minitab Tableau
Microsoft BI Stack
Partners/Relationships U of IL-UC; U of MN
Rules: (PHI)
Mathematics: e.g., categorization, % Statistics adv statistics data mining
Benchmarks/Compares Internal External
Information Accuracy: 70% 80% 90% 100%
Data Access/Selection:Sequencer-raw data; merge with age, sex,
Data Repository: e.g., Amalga DSSShared-eg, BiomeResearcher's Rep.
PAMA SHA SIRS MICS
Data Recency & Retention:
e.g., Daily/1 year Weekly/2 years
Near Real-Time/discharge
Data Preparation:
Data Structure: Discrete Unstructured
Data Source Systems: e.g. , MSS PICL EMR--for new subjects MICS Revenue Cycle RIMS
Work Processes: Research design,
Analytics Roles:
Analytics Skills:Design, Data Reduction, Adv Stats, Validity
AREA-SPECIFIC RESOU
RCESSHARED RESO
URCES
AREA-SPECIFIC RESOU
RCES
PATIENTS'/SEGMENT'S NEED and BENEFIT (including intermediaries):
Actio
ns &
Dec
isio
nsTo
ols
Data
Peop
le &
Pro
cess
es
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Mayo Clinic Analytics Roadmap 1-2 Years 2-4 Years 4-5 Years
Mayo ClinicStrategic Objectives
Improve Productivity & Efficiency, Reduce Costs
Provide Supporting Tech & Infrastructure: Grow & Retain
Develop New Products and Services
Organizational Change; Analytics Readiness
Prepare for new levels of data access
Clinicians & Admins - Desire, Ability; Rules
Online Learning/Pilot Tools; Integration of viewer, action, and analytics
Data Access & Analytics• Information
Management & Analytics
• Enterprise Analytics
Consolidation of data platforms, Robust architecture design for information needs
Consolidate & refactor data delivery & reporting
New data management platforms, Self-provisioning analytics,Analytics in the workflow
Self-service Access Execution of metrics
Real-time AnalyticsDiscovery, innovation
Analytics options. New integrated products
Analytics Accelerator PHASE 1Accelerate 3-5 vital few projects; dedicated internal & vendor resources
Provide analytics experts a learning, expertise-sharing networking hub
PHASE 2Diffuse new analytics-based tools, as work areas become readyInitiate next-step analytics capabilities
PHASE 3Advocate for patient analytics
Sponsor Analytics Symposia
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-21
The Mayo Clinic Analytics AcceleratorPATIENTS
CliniciansResearchersManagement
Allied Health Staff
Responsibilities: Works Differently
A Development Space where invited experts fill dedicated roles to
build analytics solutions for a Vital Few high-value initiatives
A Networking Hub for analytics experts
An Advocate for next-step analytics
Global Business Solutions Center for
Individualized Medicine
Center for Regenerative
Medicine
Office of Information &
Knowledge Management
(OIKM)
Office of Population
Health Management
Section of Medical
Informatics
Biostatistics & Informatics
Supply Chain Informatics Operations
Quality
Systems & Procedures
Center for Social Media
Center for the Science of Health Care
Delivery
Center for Innovation
Mayo Clinic
Ventures
Education
Health Sciences Research
(HSR)
External Partnerships
Analytics Accelerator
Information Management & Analytics
Enterprise Analytics
Invited Analytics Experts
Description
• Enterprise-Wide• Sponsored and funded by a
Mayo Clinic Chief Officer• Execution focused• Has no “territory”• 2-3 Solution Managers*
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-22
Domain Experts
Domain Experts
Business/Process Analyst
Rapid Prototyper
Business Relationship
Manager
Visualization Designer
Statistician
Analyst Programmer
Business Logic (Rules)
Designer
Data Architect
Business/Process Analyst
Solution Manager
Visionary Sponsor
PATIENTSAreaGoalObjectiveDecision-makers
Tools/SystemsPartners/Relationships
User ExperienceDisplay Frequency
MathematicsBenchmarks/CompareInformation AccuracyRules
Data Access and SelectionData RepositoryData Recency and RetentionData PreparationData Structure Data Source Systems
Work ProcessesKey Roles, Skills
Mayo Clinic Analytics Accelerator: Solution Roles
© 2014 Mayo Foundation for Medical Education and Research. All rights reservedslide-24
WhitepaperStrategic Research: Analytics Excellence
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Selected References & Bibliography1. A Brief History of the Computer Sector in the 20th Century featuring IBM, Tandy, Apple, DEC and good ole Wang
Computer! (January 9, 2013). Business Analytics, 2014(April 22). Retrieved from https://www.ibm.com/developerworks/community/blogs/business-analytics/entry/a_brief_history_of_the_computer_sector_in_the_20th_century_featuring_ibm_tandy_apple_dec_and_good_ole_wang_computer?lang=en
2. Aneurysms can now be detected with 95% accuracy. IBM - Mayo Clinic healthcare solutions - United Kingdom Retrieved 04/08/2014, from https://www.ibm.com/smarterplanet/uk/en/healthcare_solutions/article/mayo_clinic.html?ca=content_body&met=uk_smarterplanet_healthcare_solutions_ideas&re…
3. Bartlett, R., Ph.D. (2014). Analytics-driven Culture. Analytics Magazine, January/February 2014, 4.
4. Bird, J. (November 1, 2013). 3 ways healthcare orgs use big data. Retrieved from http://www.fiercehealthit.com/story/3-ways-healthcare-orgs-use-big-data/2013-11-01
5. Bridgwater, A. (2013). Big data analytics from Henry Ford to 2014. Retrieved April 21, 2014, from http://www.computerweekly.com/blogs/cwdn/2013/08/big-data-analytics-from-henry-ford-to-2014.html
6. Brown, B., Court, D., & McGuire, T. (2014). Views from the front lines of the data-analytics revolution. McKinsey Quarterly, 4. Retrieved from http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution
7. Chute, C. G., Beck, S. A., Fisk, T. B., & Mohr, D. N. (2010). The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. Journal of the American Medical Informatics Association, 17(2), 131-135.
8. Cloud Democratizes Access to Big Data Analytics. (2014). FICO Insights Retrieved 1/30/2014, 2014, from http://www.fico.com/en/wp-content/secure_upload//74_Cloud_Democratizes_Access_Big_Data_Analytics_3053WP.pdf
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Selected References (cont.)9. Cortada, J., Gordon, D., Ph.D., & Lenihan, B. (2012). The Value of Analytics in Healthcare: From Insights to
Outcomes. Retrieved 12/17/2013, 2013, from https://www.ibm.com/smarterplanet/global/files/the_value_of_analytics_in_healthcare.pdf
10. Duhigg, C. (February 16, 2012). How Companies Learn Your Secrets. New York Times Magazine. Retrieved from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=2&
11. Ellingsworth, M. (2014). How Analytics will Drive the Future. Analytics Magazine, January/February 2014, 5.
12. Explorys And Cleveland Clinic – Harnessing The Power Of Big Data Analytics. (2014). Retrieved 04/08/2014, from https://www.explorys.com/about-us/news/2014/03/04/dr.-martin-harris-speaks-to-how-explorys-and-cleveland-clinic-are-harnessing-the-power-of-big-data-analytics
13. Franks, B. (2013). Analytic Teams Are Rapidly Reaching Critical Mass. Retrieved 01/02/2014, 2014, from http://iianalytics.com/2013/10/analytic-teams-are-rapidly-reaching-critical-mass/
14. Fraser, H., Jayadewa, C., Ph.D., Goodwyn, J., Mooiweer, P., Gordon, D., Ph.D., & Piccone, J. (2013). Analytics Across the Ecosystem: A Prescription for Optimizing Healthcare Outcomes. Retrieved 12/18/2013, 2013, from http://www-935.ibm.com/services/us/gbs/thoughtleadership/healthcare-ecosystem/
15. Gillespie, G. (2012). How Reporting, Analytics and Metrics Affect
16. Outcomes and Save Lives. Information Management, 3. Retrieved from http://www.information-management.com/newsletters/Health-care-analytics-metrics-Cleveland-Clinic-10021925-1.html?zkPrintable=true
17. Griffin, J. (2011a). Achieving Analytics Excellence Part One: Organizing the Analytics Center of Expertise. Retrieved 12/03/2013, 2013, from http://deloitte.wsj.com/cfo/files/2014/03/achieving_Analytics_Excellence_part1.pdf
18. Griffin, J. (2011b). Achieving Analytics Excellence Part Two: Building the Analytics Center of Expertise. Retrieved 12/03/2013, 2013, from http://deloitte.wsj.com/cfo/files/2014/03/achieving_Analytics_Excellence_part2.pdf
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Selected References (cont.)19. Griffin, J., & Davenport, T. (2011). Organizing Analytics: Building an Analytical Ecosystem for Today, Tomorrow, and
Beyond. Retrieved 12/03/2013, 2013, from http://www.deloitte.com/view/en_US/us/Services/additional-services/deloitte-analytics-service/8cd6e77f41b12310VgnVCM2000001b56f00aRCRD.htm#
20. Hickins, M. (2013). Analytics Helps UPMC Slash Readmission Rates. CIO Journal, 3. Retrieved from http://blogs.wsj.com/cio/2013/12/05/analytics-helps-upmc-slash-readmission-rates/
21. HIMSS Analytics and IIA Announce “The State of Analytics Maturity for
22. Healthcare Providers”. (2014). PRWeb Online Visibility from Vocus. Retrieved from http://www.prweb.com/releases/2014/03/prweb11626868.htm
23. HIMSS Analytics EMRAM Stage 7 Case Studies- Mayo Clinic. (2014). Retrieved 04/08/2014, from http://www.himssanalytics.org/emram/stage7caseStudyMayo.aspx
24. Lafuente, J. (2013). Predictive Analytics and Prescriptive Analytics. Retrieved 01/02/2014, 2014, from http://www.decidesoluciones.es/en/predictive-analytics-and-prescriptive-analytics/
25. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 52(2), 11. Retrieved from http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/
26. Lee, J. (2013). Strategic move. With IPO, Premier targets data analytics game plan. [News]. Modern healthcare, 43(35), 8-9.
27. Madsen, L. B. (2012). Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics. Hoboken, NJ: John Wiley & Sons Inc.
28. Mehrotra, V. (2014). What is 'Real' Analytics. Analytics Magazine, January/February 2014, 4.
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Selected References (cont.)29. Mellin, A. Embracing analytics as the key to excellence. Physician Executive, 39(2), 50-52.
30. Miliard, M. (2014). Mayo Clinic launches bedside analytics. Healthcare IT News, 2. Retrieved from http://www.healthcareitnews.com/news/mayo-clinic-launches-bedside-analytics
31. Murphy, L. S., Wilson, M. L., & Newhouse, R. P. (2013). Data analytics: making the most of input with strategic output. Journal of Nursing Administration, 43(7-8), 367-370.
32. Noseworthy, J. (2013). Mayo using big data, digitized know-how to improve care and extend its reach. Interviewd by Merrill Goozner. Modern healthcare, 43(49), 26-27.
33. Sanders, D., Burton, D. A., M.D., & Protti, D., Sc.D. (2013). The Healthcare Analytics Adoption Model: A Framework and Roadmap. Retrieved 1/9/2014, 2014, from http://www.healthcatalyst.com/white-paper/healthcare-analytics-adoption-model
34. Terry, K. (2013a). IBM Watson's New Gig: Cancer Fighter At MD Anderson. Information Week, 3.
35. Terry, K. (2013b). Optum, Mayo Join Forces To Exploit Big
36. Data. Information Week, 3. Retrieved from http://www.informationweek.com/healthcare/clinical-information-systems/optum-mayo-join-forces-to-exploit-big-data/d/d-id/1108233?print=yes
37. Triola, M. M., & Pusic, M. V. (2012). The education data warehouse: a transformative tool for health education research. Journal of graduate medical education, 4(1), 113-115.
38. Yeung, K. (2013). Product Managers: Who Are These 'Mini-CEOs' And What Do They Do? 10/12/2013. Retrieved 3/10/2014, 2014, from http://thenextweb.com/insider/2013/10/12/product-managers-mini-ceos
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Contact
Alissa Cornell, Senior Health Systems Engineer