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From big data to better patient outcomes.

Tim Hulsen1, David Friedecký2, Harald Renz3,4

  • 1Department of Hospital Services & Informatics, Philips Research, Eindhoven, The Netherlands.

Clinical Chemistry and Laboratory Medicine
|December 21, 2022
PubMed
Summary

Laboratory medicine is key for big data and AI in healthcare, enabling precision medicine. Implementing these technologies requires infrastructure and data standards like FAIR principles for robust, trustworthy AI and improved patient outcomes.

Keywords:
artificial intelligencebig datadata sciencepatient outcomespersonalized healthcareprecision medicine

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Area of Science:

  • Laboratory medicine's role in healthcare data science.
  • Application of big data and artificial intelligence (AI) in clinical settings.
  • Advancements in personalized and precision medicine.

Background:

  • Laboratory medicine generates substantial structured data, crucial for healthcare innovation.
  • The increasing use of real-world data (RWD) in research and clinical practice.
  • Need for integrated patient data to support patient-centered care.

Purpose of the Study:

  • To highlight the critical role of laboratory medicine in leveraging big data and AI.
  • To discuss the infrastructure and principles needed for RWD analysis.
  • To explore how AI and big data can optimize laboratory operations and patient outcomes.

Main Methods:

  • Adoption of Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles for data management.
  • Utilizing federated learning, standards, and ontologies to enhance AI robustness and trust.
  • Transitioning from univariate to multivariate statistical methods for big data analysis.
  • Integrating multi-omics data for a systems biology approach.
  • Implementing AI-driven tools like predictive maintenance and moving averages in laboratory settings.

Main Results:

  • FAIR principles facilitate data sharing and reuse within the research community.
  • Federated learning and ontologies improve AI algorithm performance and reliability.
  • Multivariate analysis and multi-omics integration unlock deeper scientific insights.
  • AI tools offer potential for optimizing laboratory productivity and result quality.
  • Enhanced laboratory processes can lead to improved patient outcomes.

Conclusions:

  • Laboratory medicine is pivotal for the successful integration of big data and AI in healthcare.
  • Robust data infrastructure, adherence to principles like FAIR, and advanced analytical methods are essential.
  • AI and big data present significant opportunities to enhance laboratory efficiency, diagnostic accuracy, and personalized patient care.