Guidance for Evaluating and Choosing an Analytics Solution in

HealthcareDocument owner: Dale SandersEmail: dale.sanders@healthcatalyst.comDate: March 2013

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OverviewGeneral 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

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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

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Framing the Analytic Environment for

Healthcare

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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.

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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

7Technology Adaptability: The Evolving Data Ecosystem

ACO

IDN

Hospital

Clinic

Social Care

Community

Home

Analytics are driven by ACOs,

mergers, acquisitions and

need for “system-ness”

Data content is essentially non-existent at present in

healthcare delivery

8Adaptability: The Evolving Analytic Motives

Quality of Life

& Health

Prevention &

Intervention

Utilization

Billing & Complia

nce

We need to be more driven

by these…

This is where we are

analytically, right now

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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

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 ReportingEfficient, consistent production of reports & adaptability to changing requirements.

Level 3 Automated Internal ReportingEfficient, 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 SolutionsInefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

Progression in the ModelThe patterns at each level

• Data content expands

• Adding new sources of data to expand our understanding of care delivery and the patient

• Data timeliness increases

• To support faster decision cycles and lower “Mean Time To Improvement”

• Data governance expands

• 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?”

The Expanding Ecosystem of Data Content

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1. Billing data2. Lab data3. Imaging data4. Inpatient EMR data5. Outpatient EMR data6. Claims data7. HIE data8. Detailed cost accounting data*9. Bedside monitoring data10.External pharmacy data11. Familial data12.Home monitoring data13.Patient reported outcomes data*14.Long term care facility data15.Genomic data16.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

Closed Loop Analytic Experience

Knowledge Systems

Analytics System

Deployment System

• 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

• Quality of Care vs. Cost of Care• Enterprise data warehouse• Data visualization• Data access & production• Metadata management• Patient cohorts

• Organizational data literacy• Process improvement

training• Clinical leadership teams• Data & knowledge asset

governance• Steering and guidance

committees

Culture &Organization

Technology

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Deeper In the Details

The Technology

Knowledge Systems

Analytics SystemDeploymen

t System

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The Technology Components1. 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

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ETLSource System

s

Data Warehous

e

Visualization

Metadata

Security

EDW Performance Metrics

Hardware & Software Infrastructure

Master Data Management

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ETLKey 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?

19Data 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.

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Data Mart Data ModelingThe 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

Oncology Data Mart

High value logic

21Data Models and

Reporting LogicDoes the solution support each layer? Prove

it…

22Master 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?

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Metadata RepositoryCan 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?

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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?

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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?

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EDW Performance and Management MetricsCan the solution track basic data about the

environment, such as: User access patterns Query response times Data access patterns Volumes of data Data objects

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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 multi-terabyte

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

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Cultural Change ManagementKnowledge and Deployment Systems

Knowledge Systems

Analytic System

Deployment System

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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?

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Clinical Content and Evidence Based AnalyticsDoes 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?

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Timelines and CostsCan 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)?

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Vendors in the Crowded Market 4medica Analytics8 Ascender Cerner CitiusTech Cognizant Crimson Epic Explorys Health Care Dataworks Health Catalyst HealthBridge Humedica

IBM MedeAnalytics MEDecision Oracle Perficient Predixion Recombinant PSCI Sajix SpectraMD Strata Decision Technology White Cloud Analytics ZirMed

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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