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Data! Information !! ACTION !!! – RESULTS ???
Christopher Looby, FACHEVice President, Agile HealthcareGlenview, Il847-347-0088E-mail: [email protected]: www. Agilehc.com
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NO longer --
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An Ultimate Challengeit is NO longer about doing More w/ Less
NOW you MUST do LESS w/ LESS
How do Healthcare Organizations / Providers BUILD $$$
Completely different way of operating required…
Capital required…
Building Cash & Cash reserves required…
Got -- Got Options
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Even taking into account Quantum Leaps
in performance in current approaches, ie
Benchmarking
Higher Performance
Achieved
Move Beyond More of The Same
Next Generation Approach
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Technology is shaping
organizational success
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When information is cheap,
attention becomes
expensive.
James Gleick,
The Information, a History, a Theory, a Flood
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Objectives
• Describe a robust Business Intelligence /Analytics (BI/A) capability and practice ( including the critical importance of information deployment and visualization ) that drives action, quality and efficiency in delivery of care and services;
• Suggest developments / adjustments / re-alignments for the organization that might be engaged to improve coordination and integration of organizational BI/Analytics efforts;
• Identify key organizational characteristics required to leverage BI/Analytics for dynamic decision-making that delivers --Results.
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Section 1
Big Data:
What’s the Big Deal?
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Big data• Term for a collection of large, complex data sets comprised of
structured and unstructured data elements
• The trend to larger data sets is due to
• More readily allow correlations to be found to "spot” business trends,
population issues, prevent diseases, etc.
• Beyond the BUZZZZZZZZZZZzz of faster more insightful analysis,
understanding of usage patterns and /or interests, the real-time
incorporation of data from sensors, RFID, etc.
-- decisions or actions that are… more targeted, precise,
instantaneous, simultaneous, synchronous enable better results!
separate smaller sets with the same total amount of data
the additional information derivable from analysis of a single large set of related data
vs
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Big dataFocus then, must be on DATA FLOWS rather than Stocks
Resulting are -----
• Data sets of such size and composition --- difficult to process
using on-hand database management tools, traditional data
processing applications and systems
• Challenges in
o Quality* & consistency innately possible in the data
sought
o in capture, curation, storage, search, sharing, transfer, in
capability
o in analysis, reporting, modeling output and visualization
* Quality in --
Accuracy / Completeness / Update / Status / Relevance / Consistency across data sources / Reliability /
Appropriate presentation / Accessibility
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Big data
• Handling the Data --
massive amounts it
• Data of all types structured
and unstructured
• Moving beyond
Warehouses to direct,
recurrent, continuous
access and interactive
capabilities
Big data sets & focus on work Flow means ---
New structures for
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Big dataBig data sets & focus on work Flow means ---
New structures for
• the ‘Social’ Organization
The working environment of connectivity for & between
data scientists / DB admin / business line leaders /
organization leadership / line operations decision making
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Big data• Shift in working with the data
o from traditional areas ( IT, Infromatics, Finance,
decision support, etc. ) to ‘production’ and core
operational / business functions
o towards data scientists and process developersrather than ‘analysts’
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Data Value
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From
BI & Decision Support to
“Analytics”
Section 2
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Info
rma
tion
Fo
resig
ht
Analytics Illuminate
Hin
ds
igh
t
Fields of Vision
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Why “Analytics”
Scientific management is moving from
a skill that creates competitive
advantage to an ante that gives a
company the right to play the game
Ian & Elizabeth Stephens Ten Trends to Watch in 2006, McKinsey Quarterly Jan 2006
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Has been focused on
querying, reporting and online
analytical processing (OLAP)
tools and techniques that can
answer the questions about
something related to:
– Quantities
– Frequencies
– Locations
– Classifications
– Combinations
Business Intelligence
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Analytics…
The science of analysis
The branch of logic dealing with analysis.
Simplifying Data to Amplify Meaning
Data Driven Decision Making
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Analytics
Refers to the skills,
technologies, applications and
practices for continuous
iterative exploration that
answers a set of questions
that answer more definitively
AND…
Move beyond
What is happened ( ing )
& Why
To answer ( provide guidance about )
What’s NEXT
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AnalyticsFields of Vision
What has been happening in my
business?
Are there patterns in my data? ( e.g. What
kind are they – daily,
weekly, seasonal? )
Do these patterns enable decision rules for better operational
control?
What volume picture do demand patterns paint for the future ?
( e.g. What resources are / available ? )
What probably resource fulfillment
level?
Given a probable demand pattern -What is possible ? ( e.g. What resources
are needed, when from
where ? ) What are the permutations
Awareness – Knowledge Action -- Prescription
1000
1100
1200
1300
1400
1500
1600
ED Visits: Semi-Monthly Demand ForecastPredictive Modeling vs. Trend Analysis
85% Service Level Forecast Trend
Business Intelligence Optimization / SimulationDescriptive Analytics Predictive Analytics
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What does…
Business Intelligence / Analytics
Work involve
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1) Reveal ACTIONABLEinformation on What happened
Why is this happening
What if these trends continue
What will happen next -prediction
What is the best that can happen
– (that is, optimize)
What actions are needed
Analytics Capability
2) for Decision Making that embodies
PrecisionConsistency Agility
Speed Low Cost
3) About Decisions that Matter !
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Decision Management Sophistication
Busi
ness
Impa
ct
Precision &Agility
Consistency / Speed ~ Cost
Awareness & Understanding
Enterprise Coordination
Poin
t /Fu
nctio
nal L
ift
Exte
nsiv
e ~
En
terp
rise-
wid
e&
Con
tinua
l Lift
• Rules Management Applications / Capability
• Predictive Analytics• Decision Models• Adaptive Control
• Integrated Decision Engines
• Comprehensive BI monitoring/ tracking & reporting
H
H
Poin
tCo
rrec
tion
‘Exception’Correction Speed
• Information reporting applications
Analytics CapabilityDecision Sophistication Path
Adapted from : The Deciding Factor p 80
25Adapted from : Smart (Enough) Systems p.15
IT Applications--
New Service Line-----
Patient Care Maps-----
Consultants-----
MD Credentialing-----
Market Segmentation
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BAR
Capital Investment
-----
Patient Care Model-----
Strategic Market Positioning
-----
Physician Alignment
Analytics Capability Decisions that Matter
MD’s
FMLA
Bed Assgn
Prevention activities
MEDs
Staffing
Agency
CareMgmt
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Analytics Capability Decision Information - Contextually Powerful
Considers --• What are the decisions I need to
make?• What do I need to make
decisions?• How are my decisions connected
to other people’s decisions?• What new models are required to
make better decisions?
Decision Management focus ondecisions / decision processes for greatest effectiveness.
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Engaging AnalyticsDecisions on what is Stored & Real-time
Connections for Decision GuidanceFully Informs --
Prod Ops
Fin Ops
Analytics
Mktg Ops
Processes
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Statistics
Estimation
Shape
Variation
Location
Probability
Correlation
Simple Regression
Deploy~~~
Operationalize
User InterfaceInferential
Business Intelligence ~ Analytics Practice
Investigation ~ Exploration
Observational Study
Business Understanding
DataUnderstanding
Data Preparation
Modeling
Evaluation
DeploymentOperationalize Data
Designed Experiment
Forecasting
Multiple Regression
Descriptive
Prescriptive Predictive
Experimental design
Link to Data Visualization
DecisionManagement
Analytic Applications – Tools Decision Guidance
Data Visualization
Dispersion / Distribution
Reporting
Descriptive
AlertsCounts Summaries
Numeracy
Functionality
Aesthetic Form
Engaging Analytics
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Analytics Key to Driving Value Achievement
HFMA’S Value Project Value in Health Care: Current State and Future Directions
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Engaging Analytics?Future State of Value
Strategy Options to Drive Value
HFMA’S Value Project Value in Health Care: Current State and Future Directions
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Statistics
Estimation
Shape
Variation
Location
Probability
Correlation
Simple Regression
Deploy~~~
Operationalize
User InterfaceInferential
Business Intelligence ~ Analytics Practice
Investigation ~ Exploration
Observational Study
Business Understanding
DataUnderstanding
Data Preparation
Modeling
Evaluation
DeploymentOperationalize Data
Designed Experiment
Forecasting
Multiple Regression
Descriptive
Prescriptive Predictive
Experimental design
Link to Data Visualization
DecisionManagement
Analytic Applications – Tools Decision Guidance
Data Visualization
Dispersion / Distribution
Reporting
Descriptive
AlertsCounts Summaries
Numeracy
Functionality
Aesthetic Form
Engaging Analytics
Cautions:Avoid Unnecessary • Reinventions• POC’s
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Statistics
Estimation
Shape
Variation
Location
Probability
Correlation
Simple Regression
Deploy~~~
Operationalize
User InterfaceInferential
Business Intelligence ~ Analytics Practice
Investigation ~ Exploration
Observational Study
Business Understanding
DataUnderstanding
Data Preparation
Modeling
Evaluation
DeploymentOperationalize Data
Designed Experiment
Forecasting
Multiple Regression
Descriptive
Prescriptive Predictive
Experimental design
Link to Data Visualization
DecisionManagement
Analytic Applications – Tools Decision Guidance
Data Visualization
Dispersion / Distribution
Reporting
Descriptive
AlertsCounts Summaries
Numeracy
Functionality
Aesthetic Form
Engaging Analytics
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Data Visualization Example
http://modernsurvivalblog.com/wp-content/uploads/2010/06/usa-population-density-map-3d.jpg
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Section 3
The Critical Connection !
Data
Strategy
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Strategy – Adding Value
Retain & Grow
Revenue
Establish
Barriers to Entry
Operational
Efficiency
Reduce Risk
Drive Insight &
Innovation
Improving the quality or accessibility of enterprise data or Big data processing is not an end in itself or a single point
Understanding how information can enable or
improve business processes and decisions at the
heart of achieving strategy.
Creating
Business
Value •Long term in nature
•Crosses multiple systems and business processes
Whitepaper -- www.newvantage.comNewVantage Point: Principles for a Successful Data Strategy, Paul Barth, Managing Partner
The aim is …
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Business Value* ! ! !It is all about …
DeliveringHigh-Value healthcare user experiences:
Care, Safety, Service and Outcomesappropriate to situation at hand
at the
Best Delivered Cost
*Quality
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The Big Picture on Leveraging Data to Create Business Value
Consider; create an approach for how
Business Value
. Data
People
Frontline tools
Analytics Strategy Areas
Coordinate & Leverage Components of BI/A Work Propelling Strategy Execution that Drives Business Value
Adapted from: Big Data: What’s Your Plan, McKinsey Quarterly, March 2013
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The Big PictureCreating Value
Business Value
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The Big Picture on Leveraging Data
Business Value
You have them orTBD as the Strategy
development unfolds over time
More than DATA
Governance
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Data driving business valueConsider 3 Core Components
Adapted from: Big Data: What’s Your Plan, McKinsey Quarterly, March 2013
Universal ID’s• Healthcare user
Demographics• Provider ID ( MD, NP,
PA, RPH, PT LCSW ) • Facility ID ( Hospital,
Clinic, SNF, etc. )
Treatment Data• Diagnosis• Procedures • Drugs, dosages,
medical aids, referrals, visits, LOS
Cost Data• Hospital Care• Primary Care• Specialty Care, • Rx
Predict hospitalization risk for individual patients
Patient risk score calculator; patient-workflow manager
Reduce spending on patients with chronic diseases
Measure cost and quality of treatment, adjusted for patient morbidity
Contract-evaluation tool; pay for performance models
Patient treatment monitor; physician alert tool
Outside-in-productivity-benchmarking tool for hospital staff
Monitor treatment of patients with chronic diseases and compare with medical guidelines
Reduced hospital budgets
More cost-efficient care
Reduced spending –eg. on unnecessary hospital stays
Compare hospital productivity with others, accounting for patient’s health & demographics
Analytic Models
Business Value
Decision Support tools
Interlinked data inputs =
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Section 4
Critical Cultural Characteristics to
Achieving Results !!
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The Big Picture on Leveraging Data
Business Value
TBDas the Strategy development unfolds over
time
A1
L F A2
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Analytic PracticeAssets // Capabilities Required
Data - Strategy & Specific use
A1nalytic Bearing – expectations for decisions based on data
Leadership – A3@ at all levels
Focus - What’s Important? Can’t do it all! - perform inventories
A2ptitude – Knowledge base and execution competence in
disciplines needed to Build Models, Consult, Mentor & Train, etc. [ disciplines – quantitative, business design, etc. ]
Reference - Analytics in the Workplace
✓
✓
✓
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Analytic PracticeAssets // Capabilities Required
A1nalytic Bearing – expectations for
– decisions based on data & analysis
– collaborative search for / action based on ‘One Truth’ – engaged @ all levels, all locations, all times
Leadership – @ at all levels
– A3 analytical / accountable / action orientated
– Model / Teach / Coach / Consultative / Collaborative
– Committed / Persistent / Builders / Developers / Explorers
Reference - Analytics in the Workplace
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Analytic Practiceptitude -- Starting Point….
What is needed
•Significant quantitative horsepower
•Significant consultative and change management
capability
•Significant Organizational Sponsorship
CautionsDon’t just refocus or recruit the Quality people
o Six & Lean -- complimentary yet different disciplines
o Similar / different quantitative tools & processes
New hiring? Maybe Not necessary [ classic make or buy ]
Might avoid hiring a ‘Quant’ [ classic make / lease / buy ]
A2
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The Big Picture on Leveraging Data
Business Value
TBDas the Strategy development unfolds over
time
A1
L F A2
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Reprise
Results are about:
Decision making
Considering the What’s & When’s of BIG DATA
Engaging a BI/Analytics Practice
Connecting Analytics work to Strategy in a way that drives Business Value
Taking an approach A1L F A2
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Christopher Looby, FACHEVice PresidentAgile HealthcareGlenview, Il847-347-0088E-mail: [email protected]: www.agilehc.com
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Speaker Profile – Christopher Looby• Christopher has over 30 years experience in the healthcare industry - 25 years in administrative, general management, and
entrepreneur roles. His career started in manufacturing and sales of consumer goods and services.
• Currently in the midst of launching Agile Healthcare, with his business partners, Christopher is engaged in developing and
bringing to market a predictive modeling application for patient volume forecasting and resource optimization and daily
decision making – an ERP approach for healthcare operations planning & management. Breaking new ground in the
healthcare industry information / business intelligence / analytics space, the company’s aim is helping healthcare provider
organizations improve performance related to the operational management discipline’s triple constraint question (
simultaneously maintaining or improving capacity / quality / cost ), as a key means to address healthcare industry Value
Based purchasing initiatives.
• Over the last 5 years significant contributions to healthcare industry change have been made facilitating national senior
management seminars on analytics and business intelligence for ACHE and HFMA. Additionally, Christopher has authored
the HFMA Certified Specialist Business Intelligence course. Initial work in using analytics as the basis for solving business /
customer issues is related to his assistance with epidemiologic injury control studies, 30 years ago. His training in and use
of Total Quality Management / 6σ / Lean to drives market leading performance began in the early 90’s.
• Christopher’s healthcare roles have covered - day to day clinical services operations leadership – having full business unit
P&L responsibility in outpatient acute care, behavioral health, physical and cardiac rehabilitation, imaging services delivery
areas, among others within a hospital organization environment. Other areas of clinical operations expertise include -
population health and wellness, occupational health, workers compensation and employee safety. Experience also
encompasses - business development in the above areas and senior services, skilled nursing/complex post acute care
services. In all capacities - Chris has lead direct reports across a variety of non-clinical and clinical disciplines.
• An appointment as adjunct professor at the Lake Forest Graduate School of Management { LFGSM), as an MBA program
faculty member has been enjoyed for nearly 15 years. Christopher has taught over 70 course terms since 2001. He is
currently appointed to teach MBA core courses -- Operations Management and Leading Change Management, and the
school’s Healthcare Specialization Capstone course ( which he authored ). He also authored and teaches Management
Skills for Pharmacy Students, for PharmD candidates at Rosalind Franklin University of Medicine & Science. For over 5
years at LFGSM, Christopher has provided lectures, seminars and consulting for the Corporate Learning Solutions division.
Prior teaching with LFGSM, that Chris taught Healthcare Systems and Healthcare Delivery Strategy at National Louis
University.
• Among volunteer activities, Christopher has contributed to CHEF ( ACHE’s largest independent chapter ) with 5 years of
leadership as a board member including Chapter President.
