1. IT APPLICATIONS IN CRO PRESENTED BY -SANJEEV YADAV -SARVAJNYA TATTU
-SANDESH MHATRE
-SANJEEV YADAV
-SARVAJNYA TATTU
-SHANTANU PATIL
-SHWETA SAWANT
MSc. CR ( )
8th June, 2011

2. CONTENTS Introduction IT Applications Future Application
Government Initiatives
Advantages
Challenges
Conclusion

3. INFORMATION TECHNOLOGY

4. Information Technology is the use of hardware, software, services, and supporting infrastructure to manage and deliver information

5. CHALLENGES TO HEALTHCARE SYSTEM

6. Financial challenges
Need for greater access to capital
Inability to provide evidence of return on investment
Technical challenges
Complex and lengthy implementation processes
Lack of uniform standards
Reluctance towards integrating and incorporating changes to business processes
Cultural challenges
Lack of leadership support from the public and private sectors
Resistance by health care providers

7. OUTSOURCING

8. A way of life, and a mark of civilized society
We outsource everything
Products
Services

9. The Way We Make Progress Against Disease
Clinical Trials
The Way We Make Progress Against Disease

10. Contract Research Organization (CRO)
In the field of Clinical research the organization that does research on a contract for a sponsor is known as a Contract Research Organization (CRO)

11. Reasons for Outsourcing Clinical Trials:
Demanding regulatory environment
Complexity of trial design and logistics
Need for multiethnic population
Increase in number of patients and duration of follow up
Long duration of clinical development
Delay in recruitment
Cost of development

12. eCLINICAL TRIALS

13. Refers to the use of electronic systems for automation of clinical trials
Primary electronic processes are used to plan, collect, access, exchange, archive data
Reasons for this interest are:
Advances in information technology
The increasing cost of drug
Desire to detect drug safety problems sooner.

14. APPLICATIONS

15. CLINICAL TRIAL MANAGEMENT SYSTEMS

16. DATA COLLECTION

17. DATA COLLECTION 1. Electronic Data Capture (EDC): Internet based
Investigators enter clinical data into data-entry screens
Hardware Characteristics:
Web enabled, wired or wireless
Transfer devices:
PDA for patient diaries, etc.
3. Servers
Communication protocols
TCP/IP, etc.

18. Software Characteristics:
Operating systems
Linux, Windows, etc
Front end (Graphical User Interface)
HTML, JAVA, C+ etc.
3. Backend database
Flat, SQL, XML, etc.

19. 2. Direct Data Capture (DDC):
Lab test data and ECG results are electronically transmitted from lab to sponsor’s clinical database.
3. Electronic capture of Patient-Reported Outcomes (ePRO)
Traditionally subjects keep a daily log of their
study medication
dosing times and
log of their symptoms.
Now, subjects are being asked to directly enter data into computers, portable electronic devices

20. DATA MANAGEMENT & DATA ANALYSIS

21. (DBMS)
Data is keyed into database management systems (DBMS) directly.
Even when the data is collected on paper case report forms, it is keyed into DBMS.
Software such as SAS ® or Oracle ® to analyze data
Software such as SPSS ® ’s Clementine, SAS ® Enterprise Miner for Data mining

22. EHR (Electronic Health Records)
Electronic Health Records give immediate electronic access to patient- and population-level information by authorized users
EHR improves the quality of Clinical Data, makes it more easily accessible, and more useful for safety, outcomes, and other types of analyses.

23. EHRs
TODAY
Fragmented
Limited
accessibility
populations
Narrow uses
FUTURE?
Easily aggregated
Broad access
National coverage
Many applications
Clinical Care Data

24. Global sharing of clinical data
Advantages of EHR
Global sharing of clinical data
Electronically connects Investigators globally
Removes technology barriers
Resolves coordination issues
Manages privacy requirements

25. Interactive Voice Response Systems (IVRS) :
Investigator calls IVRS
Computer linked to system generates the number
Electronic Document Management software:
Provides version control, audit trails and archiving
Enables multiple authors to work on study documents

26. DRUG SUPPLY FORECASTING & MANAGEMENT

27. Managing drug supply is a challenging aspect of drug trials.
Drugs are usually manufactured in batches on demand
Use of software that forecasts drug supply need based
on subject enrollment and tracks drug inventory.
Emerging technology is the use of Radio Frequency Identification (RFID) technologies

28. The major benefits of using an RFID enabled solution are
Removing manual intervention in tracking and hence, cost reduction in item tracking
Automated tracking of patients within site premises.
Take corrective action to immediately prevent degradation of samples in transit.

29. DATA REPORTING

30. Pharmacovigilance
Spontaneous reporting
AERS, VAERS (physician, consumer reporting)
CIOMS, ICH safety reporting requirements
Automatic (Computerized) Surveillance
For reporting drug interactions and laboratory-based changes
Electronic Signatures
To review and approve content electronic signature is the best way to achieve the goal.

31. Figure: Indicates an object with multiple versions, of which the sixth version has been signed.

32. FUTURE APPLICATIONS

33. Clinical Decision Support Systems
Clinical decision support system will help to facilitate decisions about
Risk
Diagnosis
Therapy, and
Follow-up in patient care.
It will cost-effectively address patient’s conditions and preferences, clinician’s workflow, and technical challenges.

35. COMPONENTS OF CDSS MONITORING AND CONTROL SYSTEMS Functions
Selectively monitor clinical data continuously
Test data against predefined criteria to send alerts
RISK OR OUTCOME PREDICTION SYSTEMS
Perform classification and prediction of outcome or risk with respect to specific outcome measures, e.g. length of stay, death, complications.
Support risk analysis and risk management

36. CLINICAL DIAGNOSTIC & TREATMENT SYSTEMS
Functions
Recommend diagnosis and treatment planning
Detect adverse or specific events
PROTOCOL-BASED DECISION SYSTEMS
Create, maintain, and access to disease management and best practice guidelines from different information sources
Programs for
real-time patient-specific management advice
automated recommendations, reminders and alerts
Support outcomes analysis and outcomes management

37. e IT VALIDATION GOALS Management control Controlled GCP work
processes using
computerized systems
System reliability
Consistent, intended
performance of
computerised systems e
Data integrity
Secure, accurate, and
attributable GCP e-data
Auditable quality
Documented evidence
for control and quality
of e-data and e-system

38. GOVERNMENT INITIATIVES

39. EMEA and FDA currently requires that data be submitted in SAS transport files
International Conference on Harmonization (ICH) has defined a standard XML-based (eXtensible Markup Language) electronic submission document, the Electronic Common Technical Document (eCTD).
FDA governs electronic systems used in clinical trials through the regulation Title 21 CFR Part 11

40. Clinical Data Interchange Standards Consortium
(CDISC)
Established in 1997.
Help in developing a common interchange standard for clinical data.
Collaboration to produce functional standard data models facilitating data interchange between industry stakeholders.
Standards

41. Supports end-to-end data flow within trials i. e
Supports end-to-end data flow within trials i.e. from source document to regulatory submission
Active collaboration with FDA and analogous regulatory organizations in Europe & Japan
Develops a common interchange standard for clinical data which is accomplished through the development of meta- data models like
ODM - Operational Data Model
SDM - Submissions Data Model
RIM - Reference Information Model
ADaM - Analysis Dataset Model

42. CDISC vs ICH ICH – working toward global submission standards
CDISC – working on standardization of submissions at the data level
Data Capture Forms are also “generated” by the site (or study participant). During monitoring activities, according to GCP, the sponsor will compare the data capture forms to the source documents to confirm:
Capture of all relevant data associated with testing or examination of the participant.
Accurate transcription of source data.

43. ADVANTAGES OF IT IN CRO

44. Reduction in data errors and data queries as the electronic systems can check for data errors at the time of entry.
Researchers will have quicker access to trial data, since they do not have to wait for paper CRFs or other data to be mailed or posted.
Reduction in the costs of running a clinical trial.
Quicker data entry lead to shorter duration of clinical trials.
Reduced workload and travel costs for site monitors.
Research subjects prefer entering data electronically compared to writing on paper forms.

45. CHALLENGES

46. Investigators and their staff need to be trained.
Systems must provide user authentication, encryption, firewalls, and protection against viruses and malicious attacks.
System performance and reliability are essential to prevent delays and to guarantee that data is transferred accurately and completely to the sponsor.
Require 24-hour Helpdesk support.
Can be expensive, which can be a challenge for small organizations conducting clinical trials.

47. Lack of agreed-upon standards for sharing of information contained in the EHR, unable to exchange information, which means that data cannot be aggregated
Subjects data confidentiality and privacy
Information must be accessible to clinical research staff in an accurate and up-to-date form

48. CONCLUSION

49. Thus, the Clinical Research Industry, especially the Contract Research Organizations, can be immensely benefited by the Information Technology, and in turn contribute to the society by bringing new drugs and devices into the market with a faster pace, and more effectively.
IT helps the CRO throughout the phases of a trial and even after that!

50. REFERENCES

51. Harold P. Lehmann, Edward H. Shortliffe. (2003)
Harold P. Lehmann, Edward H. Shortliffe. (2003). Information Technology Support of Clinical Research: An Introduction. Information Systems Frontiers 5:4, p415–419.
Alan Hopkins. (2003). Clinical and Regulatory Informatics: Managing the technical infrastructure for clinical drug development.
Rebecca Daniels Kush. (2007). Healthcare and clinical research: a critical link through standards. Volume 4/Number 9.
Terri L. Keeling, Robert Morris. (2010). Clinical Research: Using Business Intelligence Framework. Issues in Information Systems. Volume XI, No. 1.
R.G. Marks. (2004). The Future of Web-based Clinical Research in Dentistry. J Dent Res 83(Spec Iss C):C25-C28.

52. THANK YOU

53. ANY QUESTIONS