© 2014 mayo foundation for medical education and research. all rights reserved slide-1 internal...

© 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


Agenda

• Learning Objectives

• Background

• Mayo Clinic Systems & Procedures

• Strategic Research: Analytics Excellence• Project goal and scope• Trends, Survey, Best Practices• Practical Deliverables• Results


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


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


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


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


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


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.


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


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


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


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


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


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


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


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


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


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


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


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*


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


WhitepaperStrategic Research: Analytics Excellence


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

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

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

https://www.ibm.com/smarterplanet/uk/en/healthcare_solutions/article/mayo_clinic.html?ca=content_body&met=uk_smarterplanet_healthcare_solutions_ideas&re%E2%80%A6















http://www.fiercehealthit.com/story/3-ways-healthcare-orgs-use-big-data/2013-11-01

http://www.computerweekly.com/blogs/cwdn/2013/08/big-data-analytics-from-henry-ford-to-2014.html

http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution

http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution

http://www.fico.com/en/wp-content/secure_upload/74_Cloud_Democratizes_Access_Big_Data_Analytics_3053WP.pdf

http://www.fico.com/en/wp-content/secure_upload/74_Cloud_Democratizes_Access_Big_Data_Analytics_3053WP.pdf


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

http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=2&

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

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

http://iianalytics.com/2013/10/analytic-teams-are-rapidly-reaching-critical-mass/

http://www.information-management.com/newsletters/Health-care-analytics-metrics-Cleveland-Clinic-10021925-1.html?zkPrintable=true




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.

http://blogs.wsj.com/cio/2013/12/05/analytics-helps-upmc-slash-readmission-rates/

http://www.prweb.com/releases/2014/03/prweb11626868.htm

http://www.himssanalytics.org/emram/stage7caseStudyMayo.aspx

http://www.decidesoluciones.es/en/predictive-analytics-and-prescriptive-analytics/

http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/


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

http://www.healthcareitnews.com/news/mayo-clinic-launches-bedside-analytics

http://www.informationweek.com/healthcare/clinical-information-systems/optum-mayo-join-forces-to-exploit-big-data/d/d-id/1108233?print=yes

http://www.informationweek.com/healthcare/clinical-information-systems/optum-mayo-join-forces-to-exploit-big-data/d/d-id/1108233?print=yes


Contact

Alissa Cornell, Senior Health Systems Engineer

[email protected]

mailto:[email protected]

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