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CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 184-187
DOI: 10.1055/s-0040-1701985
Section 8: Bioinformatics and Translational Informatics
Survey
Georg Thieme Verlag KG Stuttgart

Untangling Data in Precision Oncology – A Model for Chronic Diseases?

Xosé M. Fernández
1   Institut Curie, Paris, France
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Publikationsverlauf

Publikationsdatum:
21. August 2020 (online)

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Summary

Objectives: Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies.

Methods: From nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don’t have judgment, they lack context.

Results: EHRs have become the latest addition to our toolset to look after patients. Moore’s law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options.

Conclusions: Precision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas.

Keywords

Artificial intelligence - big data - data science - electronic health records - precision medicine
 

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