1. Document owner: Dale Sanders
Email: dale.sanders@healthcatalyst.com
Date: March 2013
Guidance for Evaluating and
Choosing an Analytics Solution in
Healthcare

2. 2
Overview
 General criteria for the options assessment
 Framing the analytic options assessment
 What are the factors affecting analytics in the industry?
 What are the guiding concepts and philosophies?
 What’s the trajectory of the industry and how should we
adjust?
 Specific criteria for choosing an analytic solution
 Technical and cultural change management
 Vendors in the space… and its crowded

3. 3
General Criteria For Options
Assessment
 Completeness of Vision

Lessons from the past, understanding of the present, vision of the future
 Ability to Execute


References and scalability
Time to Value
 Culture and Values of Senior Leadership

Do they align with yours?
 Technology Adaptability & Supportability

How fast can the system adapt to the market and your unique needs for differentiation?
 Total Cost of Ownership

Affordability
 Company Viability

Will they be around in 8 years? If not, can you live without them?
I score these on a 1-10 basis, for each vendor and option

4. 4
Framing the Analytic
Environment for
Healthcare

5. 5
The Core Analytic Issue
Healthcare Value =
Quality of Health
Cost of Care
Everything we do analytically should relate
back to a better understanding of both the
numerator and denominator, in an
integrated fashion. They are inseparable.

6. 6
Technology x Change = Solution
 “The prerequisite is the technological infrastructure. The harder
thing is to get the set of skills…and that includes not just the
analytical skills, but also a set of attitudes and understanding of
the business. And then the third thing which is the subtlest, but
perhaps the most important is this cultural change…this
attitude about how to use data. There are a lot of companies
who think they are using data…but historically that sort of data
has been used to confirm and support decisions that had
already been made by management, rather than learn new
things and discover what the right answer is. So the cultural
change is for managers to be willing to say, „That‟s an
interesting problem, that‟s an interesting question. Let‟s set up
an analysis to understand it; let‟s set up an experiment.” They
have to be willing to open up and in some ways show some
vulnerability and say “Look we are open to the data.”
Erik Brynjolfsson, the Schussel Family Professor of
Management Science at the Massachusetts Institute of
Technology, Director of the MIT Center for Digital Business

7. Technology Adaptability: The
Evolving Data Ecosystem
Analytics are
driven by
ACOs, mergers,
acquisitions and
need for
“system-ness”
ACO
IDN
Hospital
Clinic
Data content
is essentially
non-existent at
present in
healthcare
delivery
Social Care
Community
Home
7

8. Adaptability: The Evolving
Analytic Motives
We need to be
more driven by
these…
Quality of
Life &
Health
Prevention
&
Intervention
Utilization
This is where
we are
analytically,
right now
Billing &
Compliance
8

9. 9
What have we learned from
EMR adoption?
 Best-of-breed, point solutions are challenging to operate




Fragmented data
Redundant technology infrastructure
High TCO
Multiple skill sets required
 The fully-integrated platforms such Cerner and Epic are
more effective
 “Meaningful use” of the technology is critically important
 We are seeing the same patterns in analytics
 Numerous fragmented point solutions, data quality problems
 Producing reports but not applying the analytics to affect
quality and cost

10. Healthcare Analytics Adoption Model
Level 8
Personalized Medicine
& Prescriptive Analytics
Tailoring patient care based on population outcomes
and genetic data. Fee-for-quality rewards health
maintenance.
Level 7
Clinical Risk Intervention
& Predictive Analytics
Organizational processes for intervention are
supported with predictive risk models. Fee-for-quality
includes fixed per capita payment.
Level 6
Population Health Management
& Suggestive Analytics
Tailoring patient care based upon population metrics.
Fee-for-quality includes bundled per case payment.
Level 5
Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on
internal optimization and waste reduction.
Level 4
Automated External Reporting
Efficient, consistent production of reports &
adaptability to changing requirements.
Level 3
Automated Internal Reporting
Efficient, consistent production of reports &
widespread availability in the organization.
Level 2
Standardized Vocabulary
& Patient Registries
Relating and organizing the core data content.
Level 1
Enterprise Data Warehouse
Collecting and integrating the core data content.
Level 0
Fragmented Point Solutions
Inefficient, inconsistent versions of the truth.
Cumbersome internal and external reporting.

11. Progression in the Model
The patterns at each level
•
Data content expands
•
•
Data timeliness increases
•
•
To support faster decision cycles and lower “Mean Time To
Improvement”
Data governance expands
•
•
Adding new sources of data to expand our understanding of
care delivery and the patient
Advocating greater data access, utilization, and quality
The complexity of data binding and algorithms increases
•
From descriptive to prescriptive analytics
•
From “What happened?” to “What should we do?”

12. The Expanding Ecosystem of Data Content
1. Billing data
2. Lab data
3. Imaging data
4. Inpatient EMR data
5. Outpatient EMR data
6. Claims data
7. HIE data
8. Detailed cost accounting data*
9. Bedside monitoring data
10. External pharmacy data
11. Familial data
12. Home monitoring data
13. Patient reported outcomes data*
14. Long term care facility data
15. Genomic data
16. Real time 7x24 biometric monitoring
data for all patients in the ACO
Now
1-2 years
2-4 years
* - Not currently being addressed by vendor products
12

14. Closed Loop Analytic
Experience
14
•
Culture &
Organization
Knowledge
Systems
•
•
•
•
EMR, pharmacy, lab, imaging, RCM, materials
management, cost accounting
Care process algorithms
Triage criteria, order sets, protocols
Provider and patient education material
Patient and care management reports
Technology
Deployment
System
•
•
•
•
•
Organizational data literacy
Process improvement training
Clinical leadership teams
Data & knowledge asset
governance
Steering and guidance
committees
Analytics
System
•
•
•
•
•
•
Quality of Care vs. Cost of Care
Enterprise data warehouse
Data visualization
Data access & production
Metadata management
Patient cohorts

15. 15
Knowledge
Systems
Deeper In the
Details
The Technology
Deployment
System
Analytics System

16. 16
The Technology
Components
1. ETL
2. Data Modeling & Analytic Logic
3. Master Reference/Master Data Management
4. Metadata
5. Visualization
6. Security
7. EDW Performance and Utilization Metrics
8. Hardware and Software Infrastructure

17. 17
Master Data
Management
Source
Systems
ETL
Data
Warehous
e
Visualization
Security
Metadata
EDW Performance Metrics
Hardware & Software Infrastructure

18. 18
ETL
 Key issues: Reliability, supportability, reuse
 What tools does the solution use? Who owns the
licenses?
 What is the ETL design for updates? (Full,
incremental, both)?
 Does the solution have a library of ETL
“accelerators” to common source systems?

19. Data Modeling
 Options, in order of preference

Bus Architecture

Kimball Dimensional Star Schema

Inmon Corporate Information Model

I2B2

Hybrid
 Bus architecture is rapidly adaptable and very flexible. It places more emphasis
on data marts that support specific analytic needs and scenarios, rather than a
general analytic model to support all analytic needs, especially those that are
focused on patient cohorts and registries.
 Dimensional models have a very limited scope of usefulness in healthcare–
typically best suited for finance and materials management/supply chain
analytics, only.
 Purchasing an Enterprise Model might seem like a good idea, but the ETL is very
difficult to maintain; the model is not easily adaptable to new source systems; and
analysts prefer more specific models to suit their needs.
 I2B2 is very specific to healthcare, particularly designed to support academic
medical centers, but it is very complex. Few people in the country understand it
and can support it, and its usefulness in meeting more typical analytic scenarios is
questionable.
 No single data modeling strategy will meet all analytic scenarios.
19

20. 20
Data Mart Data Modeling
 The data models are important, but the analytic
logic associated with the content of the data
marts and reporting is more important
EDW
Clinical
Financial
Other
High value logic
Oncology
Data Mart

21. Data Models and
Reporting Logic
 Does the solution support each layer? Prove it…
21

22. Master Reference
Data/Master Data
Management
 What is the vendor’s strategy?
 Mandatory or voluntary compliance and mapping to master data
content?
 Mandatory compliance and mapping is unnecessary and can
lead to disaster
 What data model and structures are used to support the
content?
 How does the vendor accommodate international, national,
regional, and local master data management?
 Do they use an external vendor partner?
 Do they support mappings to RxNorm, LOINC, SNOMED, ICD,
CPT, HCPCs?
 Do they support a user-friend interface terminology?
22

23. 23
Metadata Repository
 Can you browse and search metadata from a
web interface?
 Does the solution require an expensive add-on
tool?
 Does it collect metadata from ETL jobs and the
database engine?
 Does it allow a “wiki” style contribution of
content?

24. 24
Visualization Layer
 Is there a bundled, preferred visualization tool?
 Is it affordable and extensible if exposed to all
employees and patients?
 Is the data model(s) decoupled from the
visualization tool?
 Does the data model support multiple
visualization tools and delivery of data content?

25. 25
Security
 Are there fewer than 20 roles in the initial deployment?
 Does the solution employ database level security, visualization layer
security, or some combination of both?
 Does the vendor’s security philosophy pass the test for maintainability?
 Does it balance security with access?
 How does it handle patient identifiable data?
 How does the security model manage access to extremely sensitive
personal health information, such as behavioral health, AIDS, etc.?
 How does is handle physician identifiable data?
 What type of tools and reports are available for managing security?
Can the tools identify “unusual” behavior, such as repeated mass
downloads of data?

26. 26
EDW Performance and
Management Metrics
 Can the solution track basic data about the
environment, such as:
 User access patterns
 Query response times
 Data access patterns
 Volumes of data
 Data objects

27. 27
Hardware and Software
Infrastructure
 Oracle, Microsoft, IBM are the only realistic options
 Microsoft is the most integrated, easy to manage,
and affordable… from database management
through analytic desktop
 Scalability is no longer an issue– it scales to multiterabyte databases, easily
 Windows is viable and can compete with Unix in all
but the largest clusters…years away, if ever, for
most healthcare organizations
 IBM is a good second choice, but has a small
market share
 Oracle is expensive and lacks integrated tools

28. 28
Knowledge
Systems
Deployment
System
Analytic
System
Cultural Change
Management
Knowledge and Deployment Systems

29. 29
Change Management
 Does the vendor support closed loop analytics that bends analytic
knowledge back to the point of care and/or workflow?
 What do their customers say about their ability to improve care and
reduce costs?
 Have they had experience with actually realizing an ROI from the
analytic system?
 What are the success stories-- where quality of care improved? Cost
of care decreased?
 What tools and processes does the solution have to support:
 Continuous quality improvement and cultural change initiatives?
 Cost control initiatives?
 Activity based costing?
 Prioritization of analytic efforts and improvement programs?
 What tools or experience does the solution offer for data governance?
Data stewardship?

30. 30
Clinical Content and
Evidence Based Analytics
 Does the solution leverage evidence based
clinical content in the design?
 Data model, patient registries, benchmarking
 Are the analytics on the back end integrated
with evidence based data collection on the front
end, such as order sets and clinical guidelines?
 Can the system measure adherence to clinical
evidence and guidelines?

31. 31
Timelines and Costs
 Can the solution offer business value in less than 3
months, in constant increments?
 Does the solution cost less than $7M over three
years for a $1B - 2B organization (scale up and
down accordingly)?

32. 32
Vendors in the Crowded Market
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4medica
Analytics8
Ascender
Cerner
CitiusTech
Cognizant
Crimson
Epic
Explorys
Health Care Dataworks
Health Catalyst
HealthBridge
Humedica
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IBM
MedeAnalytics
MEDecision
Oracle
Perficient
Predixion
Recombinant
PSCI
Sajix
SpectraMD
Strata Decision Technology
White Cloud Analytics
ZirMed

33. 33
In Summary…
 The analytic environment in healthcare is rapidly
changing, and that’s not going to stop
 Adaptability of the technology is crucial
 Technology is only 1/3 of the solution
 Cultural willingness to embrace analytics is crucial
 Cultural processes for sustained implementation are
crucial
 Look for a vendor that offers a total solution– closed
loop analytics