Optimizing Data Quality and Patient Safety with EDC Integration

(1)

Optimizing Data Quality and Patient Safety

with EDC Integration

March 2012

Clinical Research Informatics Core

Gwen Alofaituli Sale, MPH | Lead Business Systems Analyst, EDC Christopher Lanning, MS | Sr. Scientific Developer, EDC

Lawrence Love | Sr. Scientific Developer, CTMS

Presentation Roadmap

Topic Discussion

Leader

Time

•City of Hope Gwen 10min

–Mission

–Clinical Research Informatics Core

–Services

•Integration

–Subject Enrollment and On/Off Therapeutic Protocol

–Key benefits

•Streamlining Clinical Trials Registration Process –Using Medidata Rave web services

–Patient registration integration

–Off treatment integration

Chris 10min

•Safety Monitoring for Patients on Therapeutic Protocols –EMR data to the CTMS

–CTMS data to the EMR

Lawrence 10min

•Questions & Answers Gwen, Chris,

Lawrence

(2)

City of Hope

National Comprehensive Cancer Center

City of Hope has a long-standing commitment to caring for the entire person, as reflected in its credo: ‘There is no profit in curing the body, if in the process, we destroy the soul.' It’s what makes City of Hope unique and what makes it the right place for this kind of center."

- Sheri Biller

Clinical Research Informatics Core (CRIC)

Mission:

Apply the science of information management to enable the effective and efficient execution of Clinical Research:

•Clinical trials

(3)

CRIC Services

Service Description Key Systems / Projects

Clinical Trials

M t

Provide informatics solutions to manage the planning execution

•CTMS Evaluation Project •MIDAS (legacy CTMS)

Management Systems (CTMS)

manage the planning, execution, and reporting of clinical trials, such as protocol authoring and subject enrollment

•MIDAS (legacy CTMS) •Clinical Trials Online (CTOL)

Electronic Data Capture (EDC)

Provide electronic data capture (EDC) systems with electronic case report forms(eCRFs) or computer-scannable CRFs to capture study data, such as adverse events and tumor response

•Medidata Rave EDC •InTeleScans (built on Teleforms technology)

Clinical Research Informatics Core tumor response

Clinical Research Decision Support

Transform data to decisionsusing business intelligence tools to report and analyze study-related data

•Clinical Research Portal •SAS

•InTeleScan Work Bench •Clinical Trials Online (CTOL) •Business Objects (Rave) •Automated Lab-Based

Adverse Event Grader

Subject Enrollment: Paper-Based Process

CTMS CRA Enters

Subject Data in CTMS

Subject is Enrolled

CRA Completes

Data Entry Enters Research Data in CTMS

(4)

Subject Enrollment: Electronic Process

CTMS CRA Enters

Subject Data in CTMS

Subject is Enrolled

Clinical Research Informatics Core CRA Completes

Electronic Case Report Forms (eCRF)

EDC

(5)

Patient On/Off Therapeutic Protocol

CTMS Subject is On

Treatment

EMR

CRA Completes Off-Treatment Data

in EDC

EDC

Key Benefits

Benefit Business Impact for City of Hope

Patient Safety •Allows the “Therapeutic Protocol” notification in the EMR to be updated in a timely manner

Timely Data Collection

•Reduces lag time between registration in CTMS and adding a patient in the EDC system

•Enables more timely data collection

Data

Synchronization

•Ensures vital patient information (e.g. Patient ID, Study ID, Treatment Arm, off-treatment date, etc.) is

synchronizedin CTMS and the EDC system

Eli i t d d td t t di d t

•Eliminates redundant data entry and improves data quality

Data Validation •Upfront data validationat the point of data entry

(6)

Midas – our CTMS

• Web-based front-end, built using ASP.Net • MS SQL Server back-end

• Uses SOAP and WSDL-based interfaces • Based on a legacy MS-DOS program

Medidata Rave EDC

• Web based browser neutral front-end • 21 CFR Part 11 compliant system

• System housed in Houston Texas and managed by Medidata

(7)

EDC - Rave Web Services (RWS) : Inbound

• Web services uses RESTful architecture over HTTPS • Conforms to CDISC Operational Data Model (ODM) 1 3 • Conforms to CDISC Operational Data Model (ODM) 1.3

standard

• XML document consisting of a hierarchical tree of elements and attributes with some vendor extensions • Uses different URLs to access different functions (i.e. lists

of studies, drafts, versions and other metadata, as well as

o stud es, d a ts, e s o s a d ot e etadata, as e as access to study data)

Rave Web Services (RWS): Outbound

• Integrates into Rave using Custom Functions • Supports HTTP GET and POST

• Supports HTTP GET and POST • Error reporting

(8)

CTMS

Overview of CTMS to EDC

Patient Registration Integration

EDC-CTMS

Integration WS RWS Inbound CTMS

EDC

End user enters registration data into CTMS OnTime (Error Tracking) EDC Protocols & Site codes

Patient Registration Integration –

CTMS to EDC

• All registration data entered or updated in CTMS only • Mapping code lists for protocols sites and treatment armsMapping code lists for protocols, sites and treatment arms • Two methods: IsEDCStudy and AddUpdateRegistration • All errors are captured and logged into onTime error

tracking system

• CTMS username captured and passed to EDC to maintain audit trail

• Security token (guid) passed from CTMS, and verified by CTMS-EDC Integration Web Service

(9)

EDC

Overview of EDC to CTMS

Off Treatment Integration

Medidata Rave Custom Function CTMS Web Service CTMS via Email

OnTime (Error Tracking) User enters

off-treatment data into EDC

Off Treatment Integration

• All off treatment information entered into EDC only • When off treatment record saved, custom function

passes data CTMS web service

• All errors captured in onTime error tracking system • EDC user name passed to CTMS for audit tracking • Security token passed and verified between EDC and

CTMS

• Patient identification is further verified by Date of Birth and Protocol Identifier to ensure the correct patient record is being updated.

(10)

Allscripts EMR

•Uses HL7 interface messages to communicate between health systems

health systems

•Includes ADT (Admit, Discharge, and Transfer) messages

HL7 Interface Engine

Getting EMR Data into the CTMS

ADT Repository EMR CTMS Patient data is entered in the EMR.

(11)

Getting EMR Data into the CTMS

Patient data is entered in the EMR

EMR _{Interface Engine}HL7

in the EMR.

Data is stored in the EMR database.

CTMS Repository

ADT

Getting EMR Data into the CTMS

Pushes HL7 messages in real-time

Pushes HL7 messages in real-time. HL7 Interface Engine receives the messages.

ADT CTMS

(12)

Getting EMR Data into the CTMS

A script runs every 10 minutes to parse the HL7 messages and store them in a relational database.

Repository ADT CTMS

HL7 Interface Engine

Getting CTMS Data into the EMR

EMR Gateway CTMS

EMR

Patient Therapeutic Flag

(13)

Getting CTMS Data into the EMR

EMR Gateway EMR

Patient Therapeutic Flag is d t d i th CTMS

CTMS

updated in the CTMS. Patient Therapeutic Flag is written to an interface table.

Interface Engine HL7

Getting EMR Data into the CTMS

EMR

For new patients, a database job runs every 10 minutes to pull the records from the ADT Repositor and insert into the CTMS database

Interface EngineHL7

Repository and insert into the CTMS database.

For existing patients, a database job runs every night to pull data from the ADT Repository and updates the CTMS database.

(14)

Getting CTMS Data into the EMR

EMR

HL7 Interface Engine p lls nprocessed

EMR Gateway

CTMS

Interface Engine HL7

pulls unprocessed Patient Therapeutic Flag data, processes the data, and marks as processed.

Getting CTMS Data into the EMR

EMR Gateway EMR Sends Patient Therapeutic Flag HL7 messages to EMR Gateway

(15)

Getting CTMS Data into the EMR

EMR Gateway receives HL7 EMR Gateway EMR messages and updates the Patient Therapeutic Flag in EMR

CTMS Interface Engine

HL7

Thank You!

Sponsors

Adina Londrc, MD, MPH

Director Clinical Research Information Services Director, Clinical Research Information Services Gabe Peterson, MBA, PMP

Director, Clinical Research Informatics

Project Team

Gwen Alofaituli Sale, Project Manager Chris Lanning, Sr. Scientific Programmer La rence Lo e Sr Scientific Programmer Lawrence Love, Sr. Scientific Programmer Gary Cumming, Sr. Scientific Programmer Jennifer Boesen, Business Systems Analyst Eric Huang, Database Administrator

(16)

Questions

Gwen Alofaituli Sale gsale@coh.org Chris Lanning clanning@coh.org Lawrence Love llove@coh.org

Clinical Research Informatics Core 626.256.HOPE (4673)