Getting Clinical Data for Research:

Columbia’s Clinical Data Warehouse

Adam Wilcox, PhDAssociate Professor of Biomedical Informatics

Secondary Data Analysis of Electronic Clinical Data

Benefits

Unobtrusive Fast & inexpensive Easy

Challenges

Availability Quality Security

What data are available?

How good are the data?

How do I get data?

What’s the worst that can happen?

Questions

What’s the worst that can happen?

Names MRNs Addresses Telephone and fax #s SSNs Email addresses Dates Certificate numbers Employers

names/addresses

HIPAA PHI Geographic subdivisions

smaller than state, except initial 3 digits of zip code

Account #s URLs IP addresses Biometric identifiers Full face photographs Any other characteristics

that may be used individually or in combination to identify the individual

Notification of Breach◦ If more than 500 patients, HHS also notified◦ Media

Civil penalties◦ Up to $250,000◦ Repeat violations up to $1.5M

HITECH Penalties

100 110 120 130 140 150 160 170 1806065707580859095

100

What’s the worst that can happen?

1994: Created, sponsored by Columbia University Department of Medical Informatics and Office of Clinical Trials◦ Populated with data from existing clinical data repository◦ Supporting clinical research

1998: Columbia + Cornell = NewYork Presbyterian Hospital◦ Warehouse funded by NYPH◦ Goal to incorporate and provide data across whole system

2004: Formal analysis of CDW user needs by Clinical Quality and Information Technology Committee (CQIT)◦ Creation of Data Warehousing Subgroup◦ Need to bring together disparate clinical data sources◦ Need to manage user requests for data

CUMC/NYP Clinical Data Warehouse History

Patient demographics Visit history Diagnoses Procedures Vital signs Medications Flowsheet elements, structured notes (Notes)

What data are available?

Patients and Visits

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

0

200000

400000

600000

800000

1000000

1200000

1400000

PatientsVisits

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 970

5000

10000

15000

20000

25000

30000

35000

Patient Ages (visits in last year)

AsianBlack/Non-HispanicDeclinedWhite HispanicAmerican IndianOtherPacific IslanderUnknownWhite/Non-HispanicBlack Hispanic

Race/Ethnicity

Female

Male

Sex

Ambula-tory

Surgery

Clinic DPO visit ED Inpatient Therapy0.00E+005.00E+051.00E+061.50E+062.00E+062.50E+063.00E+063.50E+064.00E+064.50E+06

Visit Types (last 5 years)

Data type CountDiagnoses 3.3MProcedures 570KLab tests

Medications 1.5MVital signs ~80% of patients

Flowsheet/structured elements 400MNotes 6.3M

Other Data (in last year)

I have a WebCIS login

Submit HIPAA D preparatory to research forms

Receive HIPAA approval

Fill out DISCOVERY form to request data

Contact Adam Wilcox

1: Gain access to data(to be updated in coming weeks)

Y

N

2: Explore data using tools &

select variables

Top 50 Variables List &

Meaningful Use variables

De-identified databases:

RedEx I2B2*

3: Request & refine data from Clinical Data Warehouse (CDW)

4: Data management &

analysis

Receive data set

Import & manage data for analysis

using: SAS Stata REDCap AMALGA Other

What level of identifying patient information are you requesting?

Loop back to DISCOVERY for

approval to publish data and findings

Other**

Pin down key variables to submit

via DISCOVERY

De-identified Limited*** Identifiable

Covered by HIPAA G§ Fill out HIPAA B

Receive HIPAA approval

Submit IRB & receive approval

Work with programmer to refine dataShare results with

CER Studio regarding findings &

DISCOVERY process

How do I get data?

DISCOVERY

WICER Community Survey

Household Surveys

Com-munity

Out-reach Center

Ambulatory Clinics

Existing Studies 8,000+ surveys

WICER Research Data Warehouse

Research Data Explorer (RedX)

I2b2 Workbench

WICER CER Studio Identify priority disparity

areas for CER Integrate statistical

expertise via preliminary studies

Validation analyses on cost and service utilization

Identify high-risk physical & mental comorbidities

Integration of data Collection and storage of

patient-reported data Identify individuals based

upon eligibility criteria EHR plug-in Informatics tools to

support data retrieval Intervention delivery De-identify and link

datasets

What data are available?

How good are the data?

How do I get data?

What’s the worst that can happen?

Questions