Avoiding My Mistakes: 25 Years in Data Warehousing and Business Intelligence

Dale Sanders

Chief Information Officer,

Cayman Islands National Health SystemHIMSS Webinar Version 3

You are here….

Why Am I Here Today? Help you avoid my costly mistakes…

Healthcare is finally becoming more analytically driven Balancing quality and cost is now mandatory, not optional

Sinking big capital into EMR adoption

But…the majority ROI of an EMR resides in the analytic value of the data collected

EMR vendors are not addressing enterprise-wide analytics Data goes in, but data won’t come out

Vendors and consultants are promising analytic solutions that have and are going to fail…leaving organizations stranded in 2-3 years without the data they need to succeed in the Healthcare 2.0 world

Coming to Terms

The topic is data warehousing, which implies technology

But the message is about the blend of data technology, organizational structure, and culture

Analytic Success =

(Analytic Technology) x (Data Literate Culture) x (Organizational Structure)

If any of these trend towards zero, Success trends to zero

+The Cake Metaphor

Thanks to Dan Lidgard, Catalyst

My Background Education

Chemistry, Biology, Information Systems Engineering

US Air Force, National Security Agency, Joint Chiefs of Staff 1983 – 1995

Intel, IBM, Motorola, National Institutes of Health 1995 -1997

Intermountain Healthcare, Northwestern University Medical School 1997 – 2009

Cayman Islands

Data Warehousing/BI US Air Force

Force Status Monitoring System (FSMS)

Peacekeeper Information Retrieval System (PIRS)

Integrated Minutemen Data Base (IMDB)

National Security Agency National Nuclear Threat Database (2NTD)

Joint Chiefs of Staff Strategic Execution Decision Aid

Intel Integrated Logistics System (ILS)

Enterprise Data Warehouse

Lots of mistakes

Fewer Mistakes

Healthcare Specific Details

Intermountain Healthcare Enterprise Data Warehouse

Gratis and paid consulting 11 healthcare organizations in US and Canada

Healthcare Data Warehousing Association www.hdwa.org

Northwestern University Medical Enterprise Data Warehouse

Fewer mistakes

No Mistakes…

Northwestern Context

Three separate organizations, no overarching governance structure

Feinberg School of Medicine Researchers, research data sets, genomic data

Northwestern Medical Faculty Foundation Physician faculty group, ~750 Epic clinicals, IDX revenue cycle

Northwestern Memorial Hospital Cerner clinicals, legacy HBOC revenue cycle and case mix

8 years of EHR data @ EDW project start

Northwestern EDW Funding

Actual spend: 47% less, bottom line

How? Great people – Efficient and productive Microsoft BI technology stack No mistakes in strategy, no rework

Sanders’ Hierarchy of Analytic Needs

Thank you Dr. Maslow

Make sure you take care of the lower levels, first

Turn these “analytics” into a utility– fast, repeatable, cheap to produce There is no positive differentiating value in these lower levels…

everyone must meet them…they are utilitarian

Move as quickly as possible to the higher levels– technically and culturally

Publicly available cohortand metrics definitions

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Level 5: ACO

Optimization

Level 4: Payer Financial Incentives

Level 3: Professional Societies

Level 2: Accreditation

Level 1: Compliance & Regulatory

JCAHO, NCQA, HEDIS

CMS, EMTLA, HIPAA, SOX, GLBA

STS, NRMI, Trauma

P4P, MU, PQRS

Healthcare 2.0; Intermountain, Kaiser, Geisinger

Non-Teaching Facilities

Publicly available cohortand metrics definitions

Level 6: ACO Optimization

Level 5: Translational

Research

Level 4: Payer Financial Incentives

Level 3: Professional Societies

Level 2: Accreditation

Level 1: Compliance & Regulatory

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JCAHO, NCQA, HEDIS

CMS, EMTLA, HIPAA, SOX, GLBA

STS, NRMI, Trauma

P4P, MU, PQRS

Healthcare 2.0; Intermountain, Kaiser, Geisinger

Academic Medical Centers

Federal and private research agendas

Next Slide…The Data Warehouse Blueprint

Is old and musty… been with me for 20 years

Unfortunately, it still applies

Unfortunate because the symptoms addressed by data warehousing still exist…not much has changed to solve the root cause Monolithic transaction systems don’t meet the need for enterprise

analytics Virtual “federated” data warehouses are not yet viable and there

are no indications that will change soon

15ED

W B

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Metadata Repository (The “Yellow Pages”)

Master Reference & Vocabulary DataAcademic

SourceData Content

Staging andPreprocessing

Supplies

Internal

State

External

Clinical

Financial

HR

Others

Customized Data Marts

FinancialEvents

ClinicalEvents

DiseaseRegistries

OperationalEvents

Data Analysis

OLAP Tools

Microsoft Access/ODBC

Web applications

Excel

Access Control, Security, and Auditing

SAS, SPSS

Et al

Compliance& Payer

Measures

ResearchRegistries

Governance Framework

Servers, Storage, Database, and Tools Infrastructure

Academic

Supplies

State

Clinical

Financial

HR

Others

Data Modeling Options

Enterprise data model

Dimensional data model

Bus model

I2B2

Federated

So…?

There is no single data model that will meet all analytic needs in a healthcare data warehouse

Don’t believe anyone– vendor or otherwise– that tries to sell or tell you something different

If you peeled open the most successful data warehouses and looked inside, you would not see a single data modeling strategy…you would see several data modeling approaches, tailored to specific needs, layered on top of a data bus architecture

+ 18

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An Integrated Analytic Data Model For Cancer Care

Oncology Data Integration Strategy

Top down metrics & research requirements

Disparate Sources “connected” semantically to the data bus

+Master Data in the Bus

Patient ID*

Provider ID*

Encounter ID

Facility ID

Payer/Carrier ID

Department ID

Region ID

Postal Code

CPT Code

DRG Code

ICD Diagnosis Code

Charge Code

Patient Type

Gender

Date/Time

* - The most important, initially, to standardize

Standard format and content is critical

Don’t Start From Scratch: Opportunities For Reuse

Source System

EDW Operational Data Store

(Source Mart)

Master Data Bus

Subject Area Data Mart

Reporting Logic

Source System

EDW Operational Data Store

(Source Mart)

Source System

EDW Operational Data Store

(Source Mart)

Don’t Over-Think This Stuff…

“The art of being wise is knowing what to overlook.” William James, Principles of Psychology, 1890

My Biggest Mistakes

1. Believing a star schema data model would meet all the needs of healthcare analytics

2. Conducting enterprise, top down data modeling before coalescing any source data

3. Normalizing all vocabulary to a “standard” before making it available in the data warehouse

4. Allowing multiple “enterprise data warehouses” for different categories of users (e.g., research, strategy, quality, finance)

5. Underestimating the number of source systems and the demand for adaptability of the data model

Design & Plan for Content Growth

My Biggest Mistakes (cont)

6. Over investing money and expectations in tools like BusinessObjects and Cognos that are tightly coupled to the data model

People love Excel…you can go a long way towards success by simply delivering rows and columns of data to Excel users

7. Underestimating the importance of a good metadata repository

8. Embedding analytic logic in the ETL and data model vs. the reporting layer

9. Data literacy: Failing to create a culture of metrics-driven, continuous process improvement that could utilize the data

My Biggest Mistakes (cont)

6. Hiring the wrong kind of personalities on the data warehouse team

7. Data security overkill – too many roles ruined the cake

8. Data stewardship overkill – Librarians not guardians!

9. “Cleaning” source system data after loading into the EDW

10. Bonus Mistake: Believing I needed Oracle MPP architecture for scalability

The Good and Bad of Kimball

Good News: The book gave the IT specialty a vocabulary Data warehouse Data mart Extract, Transformation, Loading (ETL) Data staging areas

Bad News: His concepts became dogma Star schemas, dimensions, facts, and grains “This, and only this, is a data warehouse.”

Dimensional data modeling paints healthcare data analysis into a corner… and it’s happening quite often in healthcare right now

Other Popular, Misinformed Authors

Consultants with no hands-on, year-after-year, operational data warehousing experience Claudia Imhoff Bill Inmon

The problem is, our mid-level healthcare data warehouse managers are reading these authors and leaving common sense at the door

Who do you want to please? Ralph Kimball or your data analysts?

Give the analysts what they want. Not what you want them to want.

Governance, Business, Financial Model Start lean and simple… grow as needed

Minimal governance, maybe none, initially Seed the data warehouse with just enough money to prove its

value, then give it a bit more, and iterate Expect a 6 month “Time To Value” (TTV)

Report administratively to the CIO, but operationally to the major customers in Research, Quality, and Finance The CIO can ensure access to the source system data and a

“play friendly” attitude towards the data warehouse Common problem: Source systems do not perceive the EDW as

a priority

Fatal flaw: All planning & governance, no data warehouse

Organizational Alignment

Director of Analytics & Data Warehousing

Administratively reports to the CIO Access to the source system data is the lifeblood of the data

warehouse

60/40 split of centralized/business unit data analysts

Matrixed, customer-centric reporting to the business and clinical customers Clinical Research Clinical Quality Finance & Compliance Strategic Planning

Hiring for Success The best data warehouse professionals are difficult to find

These are not your typical IT personality

They have personal & communication skills like Business Analysts Must have great social skills

They have technical skills like Programmers Must understand SQL and data modeling

They have business acumen like your customers Must understand the business context of data

“Did you build the XYZ data warehouse?” “No. I built the teams that built the data warehouse.”

Technology Infrastructure Three mid-range, scalable servers, initially

Staging, production, and data analysis tools

Microsoft SQLServer and BI Stack Best available in the market Lowest TCO, by far Performance and scalability are not a problem, contrary to

popular rumor Nicely integrated and bundled tools specifically for DW and BI

Index everything! Sometimes, the indexes are 10 times larger than the data

Storage: Low transaction rates, very wide data sets

+New, Cool Analysis Tools

Tableau

QlikView

Tibco Silver Spotfire

Cloud-based analysis BIRST MyDials PivotLink

Message for CIOs and IT Managers: Stop trying to impose a “standard” tool for BI. Let the analysts

choose the right suite of BI tools for them.

Source Data Content

Grab as much granular data as possible, whether you see a need for it now or not Someone will eventually ask for it… and storage is cheap

Go after the largest repositories of organizational data first Volume of data => Value of data Quality of data = Data Completeness x Validity Billing and case mix Lab, radiology EMR

+

So… What’s Next in Healthcare BI and Data Warehousing?

Linking Evidence, EMR & Analytics

British Medical Journal 1,500 knowledge engineers 7,000 journals 20,000,000 papers

Evidence based best practices

Evidence based metrics of best practices

Cerner PowerChart Action Sets

Cerner Chronic Condition Management

Evidence

Analytics

+

Who are we monitoring?

What are we measuring?

What are our goals?

How will we achieve them?

Identify patients with condition of interest - Chronic Condition Management

Measure and display condition outcomes and care - Chronic Condition Management

Local targets for patient outcomes and care

Ongoing Local Improvement

Evidence Based Medicine orders and reference

information built into EMR and workflow – Action Sets

The Four Questions

Patient care:• Better informed• More consistent• Better co-ordinated

Localized Action Sets built into Cerner workflow (local knowledge and evidence at the point of care):• Orders (meds, tests)• Recommendations

Evidence-based medicine solution:• Locally developed• Cerner+ BMJ enabled

• Local content design using BMJ evidence-based starting point

• Clinical engagement and governance

Physician

Patient

Physician

Cerner CCM reports

Better informationBetter information

Project Overview

Assertion

Treating (and preventing) disease is our primary mission in healthcare

You can’t treat (or prevent) what you can’t define

Standardized, data-driven definitions of diseases are largely lacking in healthcare Three healthcare providers, three significantly different

definitions of “diabetes mellitus”

Patient registries are a means to standardize our definitions

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+Large n Disease Registries

AsthmaBreast cancerCataractsChronic lymphocytic leukemiaChronic obstructive pulmonary diseaseColorectal cancerCommunity acquired bacterial pneumoniaCoronary artery bypass graftCoronary artery diseaseCoumadin managementDiabetesEnd stage renalGastroesophageal reflux diseaseGlaucomaHeart failureStroke (Hemorrhagic and/or Ischemic)High risk pregnancyHIVHypertensionLower back pain

Macular degenerationMajor depressionMigrainesMRSA/VREMultiple myelomaMyelodysplastic syndrome & acute leukemiaMyocardial infarctionObesityOsteoporosisOvarian cancerPreoperative antibiotic prophylaxisProstate cancerRheumatoid ArthritisSickle CellSystemic LupusUpper respiratory infection (3-18 years)Urinary incontinence (women over 65)Venous thromboembolism prophylaxis

Every EMR should include these data definitions and registries as a standard feature

Lifestyle & Data Warehousing

60% of claims and 72% of healthcare costs are lifestyle related (Association for Health Services Research)

Diet, drugs, drinking, smoking

We don’t need sophisticated data warehouses to tell us what we can clearly see Analytics can help us identify better treatments for these lifestyle

issues But, will analytics be able to affect lifestyle and behavior?

Probably not much…

The Breakthrough Opportunity for Data Warehousing Finding cures and prevention of small n diseases that have

disproportionately high human suffering and are NOT lifestyle related Amyotrophic Lateral Sclerosis Alzheimer's Hemophilia Hodgkin's Disease Multiple Sclerosis Rett Syndrome Scleroderma

Our national healthcare policy should address the creation of national registries for small n diseases

Don’t Get Fooled By NLP Natural Language Processing

Analysis of large collections of text for patterns and knowledge

National Security Agency Billions of $$, very challenging to find value in NLP

The difference between inference and measurement Inference => Text data mining Measurement => Discrete data mining

Walk before you run Healthcare needs measurement right now NLP is cool and will be important in the future– Watson is coming

Include text data in your data warehouse content, as a hedge for the future

IBM Watson & Data Warehousing The most exciting computer science event in my 30 year

career

But…don’t assume that the knowledge inference to win at Jeopardy is transferable to the problems we face in healthcare

Watson analyzed and clustered reams of human knowledge, captured in text

Our EMR text is not knowledge– it’s observations and treatment

Most of our text knowledge is captured in a few journals that don’t necessarily apply outside the clinical trial

In Closing… Be wary of what vendors and consultants will try to sell you

There are no off-the-shelf products that can meet all of your data warehousing/BI needs

There are very few data warehousing consultants who have the experience to entrust with your future… you don’t want to rebuild your data warehouse in 2-3 years because it’s not working

Don’t forget to build a data literate culture that can take advantage of the technology

Join and participate in the Healthcare Data Warehousing Association www.hdwa.org

Thank You!