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Data collection as a barrier to personalized medicine
David Zakim1, Matthias Schwab2
1Institute for Digital Medicine Foundation, 70192 Stuttgart, Germany.
Trends in Pharmacological Sciences
|December 7, 2014
Summary
Computer technology can improve clinical data collection, addressing challenges in translating basic science research into personalized medicine. This enhances the reproducibility of clinical research by enabling better phenotyping of study cohorts.
Area of Science:
- Life sciences
- Translational medicine
- Clinical research informatics
Background:
- Basic life science research offers potential for personalized medical care.
- Translating laboratory findings to clinical practice faces significant hurdles.
- Poor phenotypic definition of study cohorts contributes to the lack of reproducibility in clinical research.
Purpose of the Study:
- To discuss the role of computer technology in enhancing clinical data collection.
- To enable the translation of basic science insights into validated clinical guidelines.
Main Methods:
- Review and discussion of current challenges in clinical data collection and cohort definition.
- Exploration of computer technology applications for improving data quality and phenotyping.
Main Results:
- Computer technology can significantly improve the accuracy and completeness of clinical data.
- Enhanced data collection facilitates better phenotyping, leading to more robust research cohorts.
- Improved data infrastructure supports the validation of basic science discoveries in clinical settings.
Conclusions:
- Implementing advanced computer technology is crucial for overcoming barriers in translational medicine.
- Better data collection and phenotyping are essential for reliable clinical research and guideline development.
- Leveraging technology can accelerate the path from basic science discovery to evidence-based clinical practice.
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