towards a more data oriented medical reseach environment - survey results

Towards a more data oriented medical research environment

-Survey Results on information and data practice

ISI2015 | 19-21 May | Zadar

Lars MuellerChristoph SzepanskiThomas WetzelHans-Cristoph Hobohm

Christoph Szepanski ISI2015 | 19-21 May | Zadar

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Project context

Aim of the Survey

Method

Results

Conclusion

Implementation details

References

Agenda

Method Results Conclusion ClosingIntroduction

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Project context IMethod Results Conclusion ClosingIntroduction

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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 relevantknowledge gaps

Project partner: OpEn.SC Charité Berlin


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Project context IIMethod Results Conclusion ClosingIntroduction

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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 hypothesisgeneration can be technically supported (Thessen and Patterson 2011)


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Aim of the survey

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Aim Results Conclusion ClosingIntroduction

How medical professionals deal with “their” data in practice

At which points in the problemfinding process modified forms of data presentation help researchingphysicians to make better use oftheir creative potential


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Method I

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Survey guidelines designed to ascertain... information and communication

behaviour Cooperation Research data handling Creativity and problem solving skills


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Method II

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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)


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Method III

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Premiss: research process begins with dataanalysis and ends with a new researchproject

Any differences and similarities betweenthis model and practice would indicate possible starting points and areas forintervention in the research process


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Results I – Reasons I


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Reasons II


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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Data selection


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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External Resources


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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Displaying data


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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Summary


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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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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DCT-Portal (Prototype)

Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsConclusion ClosingIntroduction Conclusion

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QueryBuilderMethod ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ClosingIntroduction Conclusion

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ResearchFieldExplorer

Method ResultsConclusion ClosingIntroduction ConclusionMethod ResultsResults ClosingIntroduction Conclusion

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• 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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• 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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Thank you for your attention!

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DataCreativityTools for Innovation and Research (DCT)http://datacreativity.fh-potsdam.de

http://datacreativity.fh-potsdam.de/

towards a more data oriented medical reseach environment - survey results

Science

zadar method

christoph szepanski

data practice isi2015

data analysis

zadar aim

zadar project context

zadar reasons

improved integration