analytics staffing models of health systems that compete well using data
TRANSCRIPT
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we create thinking data®
Analytics Staffing ModelsHealth Systems That Compete Well Using Data
Greg Nelson, CPHIMS, MMCi Monica Horvath, PhD
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As a result of this “perfect storm”, healthcare organizations that continually improve how they manage data, develop insights and operationalize analytics are best poised to succeed in the
new healthcare economy.
§ Transition from pay for service to pay for performance
§ Increased competition (including impact of ratings, social media, healthcare marketing, pricing, transparency)
§ Expectations of integrative and accountable care§ Employer based strategies for improving health
§ Technology modernization in healthcare (EHR, Imaging, Cloud, Mobile, Security)
§ Data challenges for healthcare (volume, variety, velocity, veracity) and transparency
§ Ubiquitous access to information (patients, physicians) and the rise of the “data scientist”
§ Growth in patient involvement§ Increase in payer negotiating power§ Increased focus on product economic value
propositions (e.g., formulary, bundled payments, employer contracts)
Maturing & Evolving Markets, More Competition
Increasing Consumerism, Payer Consolidation, Changing Economic
Value
Tension between “Service” and “Information” economy
Need for integrating analytics competency into
the value chain
Analytics is a Key Competency Needed to Survive Future Challenges
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Inefficient,inconsistentversionsofthetruth
Foundationofdataandtechnology
Relatingandorganizingthecoredata
Efficient,consistentproduction
Efficient,consistentproductionandagility
Measuring&managingevidence-basedcare
Takingfinancialrisk
Takingmorefinancialrisk&managingit
Contractingfor&managinghealth
Source: Adapted from Health Catalyst, 2014
Analytics Maturity
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Our Perspective around Analytics Maturity
Analytic Organizational Models
Discussion and Implications
1
2
3
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Our Perspective1
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Understanding Health Analytics Excellence
• 5 core competency areas• Focus on knowledge, skills,
abilities, and behaviors to create a learning health organization
• No ladder or implied hierarchy of core competencies
• Helps us conceptualize what it means to be ‘the best’
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Information Management &
Reporting
Predictive & Prescriptive
Analytics
Information Security &
Data Privacy
Engagement with
Organizational Strategy
Data Management
& Warehousing
• Data Warehousing• Enterprise Data Management• Data Quality• Data Model• Data Sources• Data Currency• Data Capture• Data Integration
• Metadata• Information Governance• Information Development• Enterprise Content Management• Enterprise Search• Data trustworthiness• Master Data Management
• Analytics• User Profile (data democracy)• Adoption Profile (operationalization
of data and metrics)• Business Intelligence• Reporting• Analytical tools• Aggregation & Measurement• Performance & Improvement
• Data Privacy• Data Security• Anonymization• De-identification• Data Transparency
• Information Strategy• Information Value• Metrics• Initiative mapping• Culture• User adoption• Change management• Internal consulting• Collaboration• Analytic services
Perspectives of Analytic Maturity
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Source: Accenture, Counting on Analytical Talent, 2010
Analytics Project
Analytics GroupSolid line indicates a direct line of authorityDotted line indicates a a partial lien of authority or funding
Corporate
Centralized Decentralized
Function
Corporate Corporate Corporate Corporate
Center of excellence Consulting Functional
Business Unit
Function Business Unit
Function Business Unit
Function Business Unit
Function Business Unit
COECOE
Analytic Staffing Models
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Analytics Organizations2
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Centralized
Carolinas HealthcareUnity PointHenry FordAllinaHealth Alliance HospitalHCAMemorial Hermann
Physician NetworkVirginia Commonwealth
Univ. Health
Decentralized
Partner’s Health
Kaiser Permanente
Center of Excellence
AtriusChildren's
Hospital Wisconsin
Center of Excellence Heavy (functional)
UPMCIntermountain HealthUniv. of MichiganMayo ClinicGeisingerYale New HavenMount SinaiPenn Medicine
Center of Excellence Light (consulting)
Cleveland ClinicSeattle Children’sDuke Medicine
• Deep history of IT innovation / much 'best of breed' effort
• Active mergers and acquisition history• Large expectation of business unit autonomy• Large political stake in research• Many established yet siloed analysis teams
• Very strong executive leadership demanding high strategic alignment
• Analytics may be relatively new• Planning on a high degree of self service
Analytic Staffing Models
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Top-down approach to managing organizational analytics
• Can match skills to requests• Enterprise view into analytics
activities• Can measure ROI
4
Centralized Staffing Models
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Analystcommunicationincreases
Strategicalignmentisensured
Duplicateworkavoided
Datamanagementtimesarereduced
“Ithinkthebiggestlessonlearnedisthatpriortocreatingthisnewinfrastructure,workwasbeingdoneinaverysiloedfashionacrosstheorganization,butnowthatwehavecommittedtoactuallyconsolidatinganalyticsinonearea,enterprise-wide,thingshaveworkedmuchmoreefficiently.Focusingonaneffortasbigasthisfromacentralizedperspectiveisreallykey.”
VicePresidentforAdvancedAnalyticsCarolinasHealthCareSystem
Consistenttools,training,process
Shared Capabilities of Centralization
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Case: University of Utah Health Sciences
Strong commitment to sharing4 teaching hospitals
10 community clinics$1.04B operating revenue (2013)
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Value-Driven Outcomes System
Multidisciplinary team • Quality• Biomedical informatics• IT
• Finance
Costing datamart• Covers all aspects of clinic activity• Granular to 1 minute time intervals• Gold standard = general ledger (total
direct costs must be within 20%)
“We determined who we needed to help us design the system, and sequestered them. We took some our
brightest minds and found them an open office space, gave them a three-month task.. We did not hire any
additional people.” CMQO Robert Pendleton, MD
University of Utah Health Sciences
• Execution– Pilot 6 months: 8-16 people, 60-100% effort– Subsequent work: 20 people, 20-60% effort
• Results– Physicians leading improvement initiatives
based on data– Direct costs per medical condition reduced 20-
30%– Inpatient lab utilization reduced 20%
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Centralization Challenges
Maintain alignment to departmental needs
Executive ‘just-do-it’ Analytics
Extra focus needed on communication
Culture of independence for departments, medical groups
Large researcher presence
May stifle innovation
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Decentralized Staffing ModelFor organizations that have a deep history of data analysis expertise
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Shared Capabilities of Decentralization
Suits complex organizationalstructures
Fosters deep knowledge of the business
Tool agnosticPermits business arms to define their analytic approaches
“We have an environmental landscape that is challenging….We have absolutely endorsed a decentralized analytics model…. My customers are the analytics teams across the health system. Partners is organized as a set of individually operating hospitals and physician groups…. To be able to consolidate analytics would be an impossible, impossible task.” - Associate Director Enterprise Data Warehousing, Partners Healthcare
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Innovation Thrives Among Decentralized Models
• Sells the ‘Geisinger’ experience• Consulting, analytics, data
management• Analytics for the volume to value
transition• Care transformation suggestions• 23 provider customers and
counting
• Text mining extracts intel from EHR
• Adds risk scores on top of EHR records
• Venture-backed launch in 2013
• Quick to provide Ebola app for users in Nov 2014
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Case: Innovative Decentralization Enables Agility
• Partners Healthcare is a large organization in the Boston area with a wide variety of clinical informatics applications
• Text mining extracts intelligence from EHR-sourced data lakes• Adds risk scores on top of EHR records• Venture-backed launch in 2013• Quick to provide Ebola app for users in Nov 2014
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ReportContent
Consistency
Training & process
consistency
Analyst Communication
Resource Hoarding
Tribal Knowledge
Change Management
Strategic Alignment
Tool Bloat
Decentralization Challenges
.. but these are manageable if recognized and addressed head-on!
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Center of Excellence Staffing ModelBalancecentralizationofserviceswithdepartmentalautonomy
Ifwedon’tmanage it,allwewilldoisacceleratetheability tomakebaddecisions-- Director,ClevelandClinic
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“Our medical groups have their own analysts who have their own fiefdoms, but we're all hitting on the same shared database” -- Joe Kimura, CMO, Atrius Health
Shared Capabilities of a Center of Excellence
Maintains strategic alignment, even for
large orgs
Blend resources, not budgets
An analytics community is created
Consistent training, tools, methods can be evangelized
Best practices can be socialized to the enterprise
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• 11 hospitals + sites in FL, Las Vegas, Dubai• 5.1M visits / 157K admissions annually (2013)
• HIMSS Stage 7 Ambulatory Award (Dec 2014) • 6.5B operating revenue (2013)
Case:
Volume to Value
• Bundled payment arrangements negotiated
• Lowes, Boeing have contracts for cardiovascular procedures
Patient Engagement
• Expanded patient surveys
• Analytics uncovered unexpected satisfaction drivers
• Changes made, patients happier– 16% boost in high ratings
Utilization Reduction
• Analytics to pinpoint overused lab tests
• Developed alerting to block orders
• IP: $244K cost avoidance
• OP: $1.72M revenue avoidance
Analytics to transform the business
“One must take risk and experience failure to foster innovation; without a baseline failure rate, innovation isn’t happening” – Toby Cosgrove, MD, CEO
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Role confusionEmbedded vs enterprise
analysts
Prioritization confusionEnterprise or
departments?
Vendor bloatTool usage hard to
enforce
CultureTakes much planning and strong leadership
SLAs essentialDefine what the enterprise team delivers versus departmental teams
Analytics stylesVended vs internal development? Modeling strategy?
Challenges with a Center of Excellence
Analytics
COE
Dep
artm
ents
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Discussion and Implications3
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All or Nothing?
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Commonly Centralized Data Domains
Traits•Enterprise-facing activities•Analytic platforms•Expensive data•Controversial data
Examples“Core clinicals”Patient satisfactionData brokers / consumer spendingGeospatial data packsPopulation health platforms / HIE dataPatient-reported outcomesActivity-based costingBiobank metadata
Analystcommunicationincreases
Strategicalignmentisensured
Duplicateworkavoided
Datamanagementtimesarereduced
Consistenttools,training,process
Why?
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Creative Operationalization of Predictive Models Takes Advantage of Centrally Managed Resources
Readmission Risk Score• Every IP gets a risk score that is update
daily• 30 highly predictive variables• Risk scores are calculated using
warehoused data and uploaded to EHR and patient census dashboard
• Transition coaches and transition ‘conferences’ performed for most difficult patients
• At any given time, 10+ interventions are being tested
Readmission Risk Score• Daily reports divide patients into high
(~35%), medium (~16%), and low (~12%) risk groups
• Pushed into IP records as well to primary care MDs after discharge
MRSA risk model• $500K saved annually by only testing
half of newly admitted patients for MRSA
Undiagnosed hypertension model• 50% predictive value; finds 50 new
hypertensive patients per month
• Alerts pop up in OP EHR
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Case: Use of Data Brokers
UPMC Health PlanCarolinas Health System
“..people with no children in the home who make less than $50,000 a year are more likely to use the ER rather than a private doctor” – Pam Peele,
Chief Analytics Officer
“What we are looking to find are people before they end up in
trouble,” -- Michael Dulin, Chief Clinical Officer for Analytics and
Outcomes Research
Editorial: the problem with these data is the sensitivity– is your doctor spying on you? Responding to this should be part of the data strategy.
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Case: EDW Geospatial Infrastructure Development at Duke Medicine
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• Sentiment analysis of customer experiences
• Health system brand management
• Mayo Clinic offers a social media residency
• Social media identities for patients
• Facebook app to remind transplant patients to take meds (University Iowa)
• Understand linguistic phrasings for conditions (UniversityIndiana School of Nursing)
• Emory University: 20TB of vitals data over 3 years; Hadoop technology to analyze 100,000 real time data points a second
• Predicts risk of stroke, heart attack, or other serious conditions at the bedside
Case: Use of Real-time Data to Add Context to Healthcare
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Omics datais
managedbythe
DataAnalytics
Center
Omics dataisnot
integratedwiththe
PennDataStore
thatserves
enterprise health
systemneeds
Case: PennOmics is the Translational Data Warehouse
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LLC.
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Shared Purpose
2
Build Optimize1Explore 4 Measure Impact
Prioritize
5
Demonstrate
3
Create Knowledge
6
Analytics is a Journey Not a Destination
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Process automated
Process improved
Self-service platform release
Plan to drive usage
Deep mgmt. structure
Fewer individual contributors
Pilot project shows value
Enterprise rollout
LEAN analysis
Long term plan for continuing to measure
Uses existing tool licenses
Real or self-imposed vendor lock-in
Self-service data access permitted
Data literacy
Data governance
Priority of core master data
Great predictive capacity
Impractical plan to act on results
Traps That Can Create Misalignment
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Rethink Analytics Talent
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Integer mollis vehicula ligula.
WorldwideDevelop analytics concierge services
New Role: Analytics Strategist
MindsetEmpower analytics ambassadors
SkillsetNon-traditional candidates can transform
ToolsetSupport modern, collaborative, interoperable tools
Staffing practices of the past will be ineffective in the future
Unicorns don’t exist but horses with party hats
do….
Educate Human Resources
Entice them with social impact, challenge them
with the puzzle
Ensure Meaningful Work
“There are not too many people like me in health care… But there are plenty of people who have the necessary statistical knowledge and background. One of the things that is most useful is having the experience of working with a lot of data.” -- Allina’s Sr. Statistician, Jason Haupt, PhD, a particle physicist
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Shared savings, bundled payment, ACOs, PCMH, population health management
At Risk Contracting
Integrative medicine, service line design, care redesign, patient engagement and commitment
Care Transformation
Design and execution of experiments, innovation, labs that help customers explore
Performance Improvement
Who is most affected?
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Vision/ AspirationAnalytics Brand IdentityStrategic Goals and Milestones
Strategic PlanningAnalytics Lifecycle“Work-source” effectivenessAdvisory ServicesMentoring
ExecutionTransformational Change ManagementData and Analytics Literacy
Learning Health Organization Roadmap
Change Management
ThotWave: What we do
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ChangeManagementSupport
30DayPrescription
ResearchSummaries
CuratedContent
eLearning HandsonWorkshops
Classroom
Competency-Based
Literacy
StrategicPlanningforAnalytics
Our Philosophy: The ”T” in Transformation is NOT Technology
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@healthcare_bi
linkedin.com/in/thotwave
919.931.4736
Contact
www.thotwave.com
Gregory S. Nelson, MMCi, CPHIMSThotWave Technologies, LLC.
Copyright 2016 ThotWaveTechnologies,
LLC.
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we create thinking data®