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Using free large datasets for research to change how

medicine is practiced

Leonardo Tamariz, MD, MPHUniversity of Miami

BSFH Research SummitNovember 7, 2014

Conflict of interest

• No conflicts of interest.

– IRB Chair at the Miami VA.

– Federal employee.

– Sources of funding: NIH, RWJ, UM medical education, Humana consulting fees.

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Educational objectives

• Identify the sources of large datasets that can potentially be used in clinical research.

• Compare and contrast the most commonly used large datasets in clinical research.

• Identify the statistical complexities of using large datasets in clinical research.

• Discuss the future of large datasets in the current regulatory and research environments.

Impact of the Affordable Care Act on big data

• Electronic health records

• Accountable care organizations

• Health information exchanges

• Comparative effectiveness

• PCORI

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Where does data come from?

BIG DATA

Administrative encounters

Hospital EHR Tests

Gym and wearableHealth monitors

Social media andMobile apps

Health care providerencounter

Insurance company

Home monitoring

Surveys, health fairs and healthrisk assessments

The four V of big data

SAS. 2014

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Complexities of big data

BIG BIG BIG DATA

Hospital 1 Hospital 2 Hospital 3

Urgent care center 1

Urgent care center 2

Outpatientcenter

How can we prepare for this complex process?

• Understanding the structure of your own data.

• Getting out of your comfort zone and work with other departments for pilot testing the use of parts of BIG DATA.

• Familiarize with existing large datasets that can help us answer clinical questions and practice finding answers in big data.

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Can big data transform population, patient centered

and personalized care• Big data: Massive quantities of health care data

accumulating from patients or populations.• Population based medicine: Assessment of the health

status and health needs of a target population in a way that is consistent with the community’s cultural, policy, and health resource values.

• Patient centered health care: Transparency, individualization, recognition, respect, dignity, and choice in the patient’s health care experience.

• Personalized medicine: tailoring of medical care to the individual characteristics, needs, and preferences of a patient during all stages of care, including prevention, diagnosis, treatment, and follow-up.

Big data influence

Big data

• Most commonly prescribed anticoagulants in AF were NOAC’s.

• Serious bleeding events.

Population

Hospital study

• Asked questions about anticoagulant preference.

Patient centered/ Personalized

medicine

• More informed decisions

• Discuss rationale for options

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Anticoagulant preference

0 5 10 15 20 25 30 35 40

Medication with antidote

Medication with best QoL

Physician decides

Medication longer in market

More information before decision

Medication with lower stroke risk

Non-warfarin users

Warfarin users

Palacio and Tamariz. Patient preference and adherence. In press

The large dataset

Medical fileICD-9 codesCPT codes

Pharmacy fileMedication

Refills

Member fileCostrace

Medical fileICD-9 codesCPT codes

Outpatient Inpatient

Lab fileResults

Text filesReports

Structured data Unstructured data

Radiology reportsSurgical reportsAnesthesia reports

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Sequence of steps in a research project

• Conceptualization

• Funding

• Planning/Design

• Execution

• Interpretation

• Reporting

Abstracts, Presentation, Publication

Data Collection & Processing

Data Analysis

Research cycle: The way we should not do research

Research question

Funding

Execute

Collect data

Publish

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Research cycle: the way we should do research

Funding

Collect data

Execute

Publish

Research question

Health careencounter

Research Opportunities for Health Care Systems

• Large complex datasets.

• Enough power to find differences.

• Can focus on health disparities.

• Can focus on quality of care.

Ultimate goal: Improve care and science

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Research Opportunities for health care systems

Identify sources of data

Create usable data

Analyze

Outcomes

Quality of care

Health disparities

Advantages of large datasets

• Large sample sizes.

• Fast.

• Provide population estimates.

• Test trends over time.

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Disadvantages of large datasets

• Not designed for research.

• Non-randomized.

• Special statistical and data management skills.

FDA Amendment act

• Mandates FDA establish capacity to use electronic health data to assess safety of marketed drugs

– Data covering at least 100 million people required by mid-2012

• FDA is addressing drugs, biologics, and devices

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Mini-Sentinel Data Partners

The Mini-Sentinel Distributed Database

� Populations with well-defined person-time for which medically-attended events are known

• 150 million individuals

– 360 million person-years of observation time (2000-2013)

– 4.1 billion pharmacy dispenses

– 4 billion clinical encounters

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Mini-Sentinel Distributed Analysis1- User creates and

submits query

(a computer program)

2- Data partners retrieve

query

3- Data partners review

and run query against

their local data

4- Data partners review

results

5- Data partners return

results via secure

network

6 Results are aggregated

Validity of the data

Disease ICD-9 code PPV Sensitivity

Atrial fibrillation

427.31 89% 79%

Cardiacarrhythmias

427.x 85%

427.x and 798

92%

Tamariz et al. PDS. 2012Jensen et al. PDS.2012

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Research scenarios

Prevalence of atrial fibrillation in minorities

Use of catheter ablation in AF in minorities

Impact of warfarin use in minorities on bleeding and QoL after ablation

Steps in the process

• Determine interest area

• Search for existing databases

• Learn the database

– Data documentation manuals, CDs, web

• Derive research question(s)

• Conduct analyses

– Statistical consultation, programming

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Sources large databases

Public/Federal (free)• Surveillance, Epidemiology

and End Results program(SEER).

• National Health and Nutrition Examination Survey (NHANES).

• Medicare.• Healthcare Utilization

Program (HCUP).• Florida datasets.• Veterans affair data.• Clinical trials.• Your own data.

Not public (not free)

• Insurance companies

Data comparisons: Characteristics

Data source

Sponsor Disease Goal Population based

Representative

Communication

Costs

SEER NCI Cancer Research: Incidence and cancer survival

Yes Yes MedicareHCUPMEPSSS census

Free

NHANES CDC Not specific

Evaluatehealth and nutrition

Yes Yes $

Medicare CMS All Administrative

No Yes CensusSS

$

HCUP/MEPS AHRQ Qualityand cost

Yes $

Florida State of Florida

All Administrative

No No HCUP Free

VA DVA All Administrative

No Yes Medicare Free

Insurancecompany

Private All Administrative

No Yes/No SSCensusMedicare

$$$

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Data comparisons: Variables

Data source

Sample size

Race/ethnicity

Diseases Procedures Medications Follow-up

Lab result

QoL

SEER 6 million Limited to cancer

NHANES Varies Chronicdiseases

Medicare Varies All

HCUP 7 million All

Florida 3 million All

VA Varies All

Insurance company

Varies All

Can you answer the research questions?

Data source Prevalence of AF in minorities

Use of catheter ablation in minorities

Warfarin after catheter ablation and QoL

SEER No No No

NHANES No No No

Medicare Yes Yes No

HCUP/MEPS Yes Yes No

Florida Yes Yes No

VA Yes Yes No

Insurancecompany

Yes Yes No

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Research scenarios

Prevalence of atrial fibrillation in minorities

Use of catheter ablation in AF in minorities

Impact of warfarin use in minorities on bleeding and QoLafter catheter ablation

HCUP 13’967,949Whites 3.4%Blacks 1.83%Hispanics 1.43%

Dewland et a. Circulation. 2013Tamariz et al. Clinical Cardiology. 2014

Florida 1’020,049Whites 0.89%Blacks 0.53%Hispanics 0.74%

Research Opportunities for Health Care Systems

• Large complex datasets.

• Enough power to find differences.

• Can focus on health disparities.

• Can focus on quality of care.

Ultimate goal: Improve care and science

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The ideal dataset

Medical fileICD-9 codesCPT codes

Pharmacy fileMedication

Refills

Member fileCostrace

Medical fileICD-9 codesCPT codes

Claims data

Outpatient Inpatient

Detailed eventsSeverity of

diseaseResults of

diagnostic tests

Clinical data

Quality of lifeSatisfaction

Survey

POCR data

Lab fileResults

Repository

Statistical considerations: Confounding

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Statistical considerations: How to deal with confounding

Tamariz et al. AJC. 2011

Data models: Common data model

Common data

model (CDM)

Common data

model (CDM)

Data source

1

Data source

1

Data source

2

Data source

2Data

source 3

Data source

3

Observational Medical Outcomes Partnership

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Data models: Distributive data model

Transform data

AnalysisMini-sentinel

Regulatory considerations of big data

Patient and their data

Minimise risk Privacy

Maximise public benefit

Maintain public trust

Consent

Deidentification

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Just an idea!

Others

Data model

Closing remarks

• Big data is here to stay.

• It would be a terrible disadvantage not to use for research or quality of care.

• Preparing your own big data for research is complex and requires a process.

• There are several free datasets that can be used to answer research.