Presentation about the data and information pracitice of medical researchers by Christoph Szepanski for the ISI2015 (Zadar, Croatia).

1. Towards a more data oriented medical
research environment
-
Survey Results on information and data practice
ISI2015 | 19-21 May | Zadar
Lars Mueller
Christoph Szepanski
Thomas Wetzel
Hans-Cristoph Hobohm

2. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Project context
Aim of the Survey
Method
Results
Conclusion
Implementation details
References
Agenda
Method Results Conclusion ClosingIntroduction
2

3. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Project context I
Method Results Conclusion ClosingIntroduction
3
Preferred Project objective = development of a web
application

Foster hypothesis generation from large
(heterogenous) databases of medical research data

Design an information environment to support
data analysis and identification of relevant
knowledge gaps

Project partner: OpEn.SC Charité Berlin

4. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Project context II
Method Results Conclusion ClosingIntroduction
4
Preferred Data-intensive science will be an important
element of future research (Bell et al. 2009)
 Currently there is a priority to hypothesis-oriented
research
 Cultural chance how to use data for hypothesis
generation can be technically supported
(Thessen and Patterson 2011)

5. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Aim of the survey
5
Aim Results Conclusion ClosingIntroduction
 How medical professionals
deal with “their” data in practice
 At which points in the problem
finding process modified forms of
data presentation help researching
physicians to make better use of
their creative potential

6. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Method I
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Method Results Conclusion ClosingIntroduction
 Survey guidelines designed to ascertain...
 information and communication
behaviour
 Cooperation
 Research data handling
 Creativity and problem solving skills

7. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Method II
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Method Results Conclusion ClosingIntroduction
 Mixed method approach

Explorative, guideline-based individual
interviews:

(n = 5 ; duration: 30 minutes ; partly
transcribed)

Online survey:

(n = 10, including clinical physicians and
non-physicians => core target group)

8. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Method III
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Method Results Conclusion ClosingIntroduction
 Premiss: research process begins with data
analysis and ends with a new research
project
 Any differences and similarities between
this model and practice would indicate
possible starting points and areas for
intervention in the research process

9. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Results I – Reasons I
Method Results Conclusion ClosingIntroduction
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10. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Reasons II
Method Results Conclusion ClosingIntroduction

Persuing an (own)
idea
RAREOFTEN

Exploration of data
(unfocussed interest)
 Sometimes – Inspiration from literature
 Motivation – persuing ideas, rather than find new one
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11. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Data selection
Method Results Conclusion ClosingIntroduction

Data centres

Targeted collection
of new data
RAREFREQUENTLY

Directly re-use from
colleagues

Existing patient data
 A bit of both – Journals
 Potential lies in the improved integration of data from
different sources
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12. Christoph Szepanski ISI2015 | 19-21 May | Zadar
External Resources
Method Results Conclusion ClosingIntroduction

PubMed and journals

Unspecified internet
usage (Google...)
RAREFREQUENTLY

gScholar
 Sometimes – Reference tools and interpersonal
communication
 Goal of DCT should be to integrate as many secondary
information sources as possible
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13. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Displaying data
Method Results Conclusion ClosingIntroduction

Diagrams and curves

Table with numerical
values
NeglectedPreferred

Partly: complex
visualisations
 Potential lies in new visualisation techniques, also to
promote awareness and to increase acceptance
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14. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Summary
Method Results Conclusion ClosingIntroduction
Preferred

Reason for data analysis is usually an (own) idea, while
searching for an idea is almost never the reason
Preferred
Attitudes towards complex visualisations are mixed
Preferred
Individual working methods are preferred

Data analysis usually take place in the workplace and
towards the end of the working day

Results of data analysis primarily used in publications
and less so for research
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15. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Conclusion
Method ResultsResults ClosingIntroduction Conclusion
Integration of secondary
information sources
Close ties with external
data centres
Innovative visualisations as
an option
No social media needed
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16. Christoph Szepanski ISI2015 | 19-21 May | Zadar
DCT-Portal (Prototype)
Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsConclusion ClosingIntroduction Conclusion
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17. Christoph Szepanski ISI2015 | 19-21 May | Zadar
QueryBuilder
Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ClosingIntroduction Conclusion
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18. Christoph Szepanski ISI2015 | 19-21 May | Zadar
ResearchFieldExplorer
Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ClosingIntroduction Conclusion
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19. Christoph Szepanski ISI2015 | 19-21 May | Zadar
• Bell, G, T. Hey, and A. Szalay (2009). Computer Science: Beyond the Data
Deluge. Science 323, 1297-1298.
• Case, Donald O. (2005).Principle of Least Effort. In Fisher, Karen E.; Erdelez, Sandra;
McKechnie, Lynne (Eds.): Theories of Information Behavior. Cambridge, Mass. : MIT
Press, 289-292.
• Cooke, Colin R, and Theodore J. Iwashyna (2013). Using Existing Data to
Address Important Clinical Questions in Critical Care. Critical Care Medicine 41, 886-
896.
• Cropley, Arthur, and David Cropley (2009). Fostering creativity. A
diagnostic approach for higher education and organizations. Cresskill, NJ:
Hampton Press.
• Kell, Douglas B, and Stephen G. Oliver (2004). Here is the evidence, now what is the
hypothesis? The complementary roles of inductive and hypothesis-driven science in
the post-genomic era. In BioEssays 26, 99-105.
References I
Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ReferencesIntroduction Conclusion
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20. Christoph Szepanski ISI2015 | 19-21 May | Zadar
• Hoover, Steven M, and John F. Feldhusen (1994). Scientific Problem Solving and
Problem Finding: A Theoretical Model. In Problembfinding, problem solving, and
creativity. Creativity research, ed. by Mark A. Runco, Norwood N.J: Ablex Pub. Corp.,
201-219.
• Huizing, Ard, and Mary Cavanagh (2011). Planting contemporary practice
theory in the garden of information science. In Information Research 16 (4).
• Tenopir, Carol; Allard, Suzie; Douglass, Kimberly; Aydinoglu, Arsev U.;
Wu, Lei; Read, Eleanor; Maribeth Manoff, Mike Frame and Cameron Neylon
(2011). Data Sharing by Scientists: Practices and Perceptions. In PLoS ONE 6,
E21101.
• Thessen, Anne, and David Patterson (2011). Data issues in the life sciences.
ZooKeys 150, 15.
References II
Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ReferencesIntroduction Conclusion
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21. Christoph Szepanski ISI2015 | 19-21 May | Zadar
Thank you for your attention!
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