1. Migrating Clinical Data in Various Formats to a
Clinical Data Management System
Tammy Dutkin, Director of Clinical Data Management, Life Sciences, Perficient
Michelle Engler, Senior Application Architect, Life Sciences, Perficient
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2. About Perficient
Perficient is a leading information technology consulting firm serving clients throughout
North America and Europe.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.

3. • Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue ~$373 million
Perficient Profile
• Major market locations throughout North America
• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Cleveland,
Columbus, Dallas, Denver, Detroit, Fairfax, Houston,
Indianapolis, Los Angeles, Minneapolis, New Orleans, New
York City, Northern California, Philadelphia, Southern
California, St. Louis, Toronto and Washington, D.C.
• Global delivery centers in China, Europe and India
• >2,100 colleagues
• Dedicated solution practices
• ~85% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards

4. Life Sciences
Practices / Solutions
Deep Clinical and Pharmacovigilance Applications Expertise
Implementation
Migration
Integration
Validation
Consulting
Upgrades
Managed Services
Application Development
Private Cloud Hosting
Application Support
Sub-licensing
Study Setup
Services
Clinical Trial
Management
Clinical Trial Planning and Budgeting
Oracle ClearTrial
CTMS
Oracle Siebel CTMS / ASCEND
Mobile CRA
Clinical Data Management
& Electronic Data Capture
CDMS
Oracle Clinical
Electronic Data Capture
Oracle Remote Data Capture
Oracle InForm
Medical Coding
Oracle Thesaurus Management System
Safety &
Pharmacovigilance
Adverse Event Reporting
Oracle Argus Safety Suite
Oracle AERS / EmpiricaTrace
Axway Synchrony Gateway
Signal Management
Oracle Empirica Signal/Topics
Medical Coding
Oracle Thesaurus Management System
Clinical Data
Warehousing & Analytics
Clinical Data Warehousing
Oracle Life Sciences Data Hub
Clinical Data Analytics
Oracle Clinical Development Analytics
JReview
Data Review and Cleansing
Oracle Data Management Workbench
Clients

5. Welcome & Introduction
Tammy Dutkin
Director of Clinical Data Management & EDC
Life Sciences, Perficient
Michelle Engler
Senior Solutions Architect
Life Sciences, Perficient

6. Agenda
• Data Migration from CRO to OC Business Case
• Team
• Overall Process
• Quality Control
• Parsing Data into BDL Format
• Converting PDF/Word Listings to BDL Format
• Lessons Learned
• Contact Information and Questions

7. Data Migration from CRO to Oracle
Clinical Business Case
• Client had 24 legacy studies in various formats from their CROs and
partners.
• It was not possible to obtain SAS dataset files or further data
transfers for all of these legacy studies.
• Background information for many of the studies was limited. In many
cases the protocol and clinical study report were all that were
available.
• The legacy studies were available in various formats:
– SAS dataset files
– Word (or Word converted to PDF) listings and Clinical Study Reports
– Scans of SAS listings
– Excel files
• Client had Oracle Clinical and wanted all of their data stored within
their production instance of Oracle Clinical for future cross analyzes
and reporting.

8. Team
Role Responsibilities and Experience
OC Study Builders • Built the study objects in Oracle Clinical (OC)
• Handled the annotations and mapping documents
• Had 8+ years of experience building studies in OC
OC Study Loaders • Used the mapping documents and the Excel files to load the data into OC
• Had 8+ years of experience building studies in OC and loading data with
the batch data loader
Technical Experts • Handled the parsing of the Word/PDF listings into Excel
Data Managers /
Quality Control
• Performed the QC of the study build, load, and final quality control
Summary report (Note: often the same person(s) building the studies)
Data Entry • Performed data entry on those listings that could not be parsed/loaded
• Had 5-10 years of DM and entry experience
Project Manager • Managed the timelines, quality, and process
• Reviewed all documents prior to sending to client

9. Overall Process
The process followed was dependent on the type
of file available.
• For studies where the SAS datasets
exported into Microsoft Excel were available,
the process included study setup in OC,
creating maps between the Microsoft Excel
file data extractions and the OC Study, pre-processing
the Microsoft Excel file data
using the existing Generic Data Parser
program and finally, loading the data into
OC.
• For those studies where PDF or Word
listings are available, the data was first
parsed into Excel format and then followed
the process of building, mapping,
preprocessing and loading.
• For the studies where only scans of the
listings were available, the study was setup
in OC and then double data entered into the
database.
Quality control was performed at each step

10. Quality Control
• QC was built into every major step of the process
– Builder Peer Review (checklist)
• Study Design / Study Definition
• Documentation (annotations, assumptions, mapping, DVG document)
– Parsing Review (if applicable)
– Loader Peer Review (checklist)
• Discrepancy check
• Verify Source data vs. loadable Excel File
• Data Extract views
• Data spot checking
• Data entry browse checking
– QC Summary Report
• Response Counts and DCI Counts done via SQL
• Spot Checks (5 records checked from the original source file to OC)
• Any assumptions made during the build or the load/entry was
documented in a Study Assumptions document

11. Listing Formats and Processing
Word (or Word converted to
PDF) Listings
1. Convert to TXT and split
into 1 doc per page
2. Parse data into Excel
format
3. Parse to BDL format
4. Load into Oracle Clinical
Txt Listings
1. Split into 1 doc per page
2. Parse Data into Excel
format
3. Parse to BDL format
4. Load into Oracle Clinical
SAS Listings
1. Convert to Excel
2. Parse to BDL format
3. Load into Oracle Clinical
Excel Listings
1. Parse to BDL format
2. Load into Oracle Clinical
Scans of SAS Listings
Perform 1st and 2nd pass data
entry on the listings

12. Converting PDF/Word Listings
to BDL Format
• Data format comes in PDF or Word listings
• Listings had to be split into 1 document per page
• Listings had to be converted to TXT format
• Simple Java Converter was written to perform this task
Simple Java
PDF->TXT
Converter
Simple Java
RTF->TXT
Converter

13. Converting PDF/Word Listings
to BDL Format
C:16 C:20
C:59
C:77 C:85 C:94 C:115 C:125
• Wrapping columns of data
• Fields split by slash e.g. start date/stop date
• Multiple records for a single patient without the patient # repeating
• Key to parsing this type of listing is to identify patterns
– What signifies the start or end of the data?
– Where do I get the patient number?
– How can I determine when a field is wrapping vs. a new record?
– What column positions constitute a new response?

14. Converting PDF/Word Listings
to BDL Format
• Case #1: Additional decoding information e.g. 1 = Yes, 2= No, then the information
was incorporated using the DVG Long Value
• Case #2: Comment was for a particular field for all patients, then the field was
added and loaded the footnote for every single record
• Case #3: Comment was just for a specific patient, then the comment was hand
entered for the patient in a comment field in the question group
• Case #4: If the footnote was giving unit information (e.g. weight in lbs), then it was
incorporated into the SAS Label within the OC Question Definition
• The handling of the footnotes was put in an Assumptions doc that was recorded
for each study

15. Converting PDF/Word Listings
to BDL Format
Configuration file used to communicate
details for the listing:
• Column cutoff positions
• Heading titles
• Patient number headings, location,
and amount of fields to split
• What the patient number consisted of
• What signifies the start of the parsing
• What signifies the end of the parsing
• Other keys that the parser would need
for interpreting the file:
– Keep trail for
– Max rows allowed per record
– What type of delimiter separates the
patient fields
– Etc.

16. Converting PDF/Word Listings
to BDL Format
C:16 C:20
C:59
C:77 C:85 C:94 C:11
5
C:12
5
TXT Converter (custom utility created by Perficient)
TXT
Listing
Configuration File
Excel File for GDP Input

17. Parsing Data into BDL Format
• Data must be in Excel file format
• Create mapping file to define
relationship between OC and data file
• Create Lists file for mapping data
values
• Run the generic data parser to output
the BDL file
• Load BDL file into Oracle Clinical
Lists Data
Map
Generic Data Parser
BDL File
Oracle Clinical

18. Parsing Data into BDL Format
Generic Data Parser Mapping File

19. Parsing Data into BDL Format
Generic Data Parser Mapping File
Lists Map Data
Generic Data Parser
Batch Data Load File (fixed format ASCII Text File)
Oracle Clinical

20. Lessons Learned
• Confusion about whether to format the studies to a standard or load them “as is”
– The decision was made to load the data “as is” and using any data standards the
legacy study had available.
– Although it would have taken much longer, converting the data into company/CDISC
standards would have been beneficial.
• Experienced communication and consistency Issues which were addressed with:
– Weekly status meeting
– Added checklists, templates and process flowcharts
– Keeping running issue Log
– Communication and process had to be consistent and formalized in order to ensure
success
• Flexibility required
– In some cases, the data could not be effectively parsed/loaded; therefore, double
data entry was the better option
– New Data Challenges: Column wrapping was out of alignment with column headings
– the parsing had to be able to be separated by column positions as opposed to
column headings. Programmatic changes had to be made as they were needed.
• Consistency Checks
– 100% verification manual quality checks were performed at several points to ensure
that the output of a parsing prior to loading were crucial to ensure success

21. Questions?

22. Thank You!
For more information, please contact:
Tammy.Dutkin@perficient.com
Michelle.Engler@perficient.com
LifeSciencesInfo@perficient.com (Sales)
+1 303 570 8464 (U.S. Sales)
+44 (0) 1865 910200 (U.K. Sales)
www.perficient.com
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