Clinical research informatics involves using informatics tools to support clinical research activities like managing clinical trials data and conducting secondary research using clinical data. It can help make clinical trials more efficient by facilitating study design, subject recruitment from electronic medical records and social media, data collection and management, and data analysis. National initiatives aim to develop informatics tools and networks to help researchers access data and accelerate clinical research.

2. CLINICAL RESEARCH INFORMATICS
Clinical Research Informatics involves
the use of informatics in the discovery and
management of
new knowledge relating to health and
disease.

3. CLINICAL RESEARCH INFORMATICS
It includes management of information related to clinical trials and
also involves informatics related to secondary research use of
clinical data. Clinical research informatics and translational
bioinformatics are the primary domains related to informatics
activities to support translational research.
Profession community – America Medical Informatics Association,
http://www.amia.org/applications-informatics/clinical-research-
informatics

4. CLINICAL TRIALS 1.0
Study design
Collaborators
Sample size calculations
Funding
Study initiation – subject recruitment
Data management/data collection
Data analysis
Publication/presentation
Mostly on paper, labor intensive, data management a challenge

5. CLINICAL TRIALS 2.0
Exploit full use of informatics tools to:
 Create efficiency in study design and execution
 Promote full study recruitment
 Accelerate the research process
 Utilize the electronic medical record to recruit, manage data, bill
appropriately and communicate

6. STUDY DESIGN
Study feasibility
Power calculations – how many patients do I need to recruit to have
enough data to get significant results
In the past – guesswork
Study could proceed for 2-4 years before the investigator would
find out that there were not enough patients to recruit
Present and Future
Compare the study inclusion/exclusion criteria with active
patients in the EMR to see if the study will be successful

7. FINDING COLLABORATORS

8. RESEARCH SUBJECT RECRUITMENT
Research Match
EMR
Social Media

9. RESEARCH MATCH

10. RECRUITMENT FROM THE EMR
Applying inclusion/exclusion criteria to EMR
Include only active patients
When and where is their next appointment
Make research nurse more efficient – go to site where several patients might be
recruited in one day
Essential for anesthesia/surgery research since patients must give consent prior to
the day of surgery

11. RECRUITMENT VIA SOCIAL MEDIA
For some studies, newspaper advertising was standard practice
Many patients now online in social media, using apps, in social network communities
of patients with similar diagnoses
Social media still considered a form of advertising – needs to be approved by the IRB
provide a link to more detailed information and who to contact
Social networks of patients – must ask permission to post a trial
Some patient advocates (e-Patients) may promote research

12. PATIENT SOCIAL NETWORKS

13. RECRUITMENT - APPS
Cancer Clinical Trials – Cleveland Clinic

14. DATA MANAGEMENT
REDCap
EMR – Get Data

15. CONSORTIUM
770 Institutional partners
No cost for the software, minimal infrastructure (LAMP)
Centralized support through Vanderbilt University with NIH grant support

16. RESEARCH ELECTRONIC
DATA CAPTURE
Features include:
Building data collection instruments
Importing data collection instruments from a library
Creating surveys for research
Creating longitudinal studies
Controlling user access
Quality checks
Exporting data for analysis
Simple reports and data analysis tools

17. DATA FROM THE EMR FOR RESEARCH
Extract clinical data
Map to standard ontologies
Cohort identification tool
Select data elements of interest
Export data set or create a registry
Update on a periodic basis

18. DATA MAPPING
Unified Medical Language System (UMLS metathesarus)
From the National Library of Medicine
http://www.nlm.nih.gov/research/umls/
SNOMED-CT
LOINC
RxNorm
Mapped data easier for searching, data mining
Converts raw clinical data into meaningful terms

19. CHRONIC KIDNEY DISEASE REGISTRY
60,000 patients in the EMR
Multiple abstracts and papers from mining the data
about symptoms, lab values, comorbidities, survival
Successful grant funded by the National Institute of
Health
Includes demographics, lab results, procedures,
encounters, vital signs, etc.
Able to study disease longitudinally – data from 2005 -
2013

20. From the National Institutes of Health
http://bd2k.nih.gov/
Enable biomedical scientists to capitalize more fully on the Big Data being generated
by those research
Grants to enable collaborative development of tools and sharing data securely for
research
Biomedical research enterprise is increasingly becoming data-intensive and data-
driven
Appropriate access to shareable biomedical data through technologies, approaches,
and policies that enable and facilitate widespread data sharing, discoverability,
management, curation, and meaningful re-use;
Development of and access to appropriate algorithms, methods, software, and tools
for all aspects of the use of Big Data, including data processing, storage, analysis,
integration, and visualization;
Appropriate protections for privacy and intellectual property;
Development of a sufficient cadre of researchers skilled in the science of Big Data, in
addition to elevating general competencies in data usage and analysis across the
behavioral research workforce

21. CONCLUSIONS
Informatics can contribute
tools to every phase of
clinical research
Goal – to help accelerate
clinical research
National network –
Clinical and Translational
Science Awards

22. THIS PRESENTATION BASED ON MY BOOK
CHAPTER “ERESEARCH” IN
Healthinformatics.org