Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model

Emily L Gill; Stephen R Master

doi:10.1016/j.cll.2019.11.009

Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model

Clin Lab Med. 2020 Mar;40(1):51-59. doi: 10.1016/j.cll.2019.11.009. Epub 2020 Jan 7.

Authors

Emily L Gill¹, Stephen R Master²

Affiliations

¹ Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
² Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. Electronic address: masters@email.chop.edu.

PMID: 32008639
DOI: 10.1016/j.cll.2019.11.009

Abstract

The recent increase in accessible medical and clinical laboratory "Big Data" has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine-learning data analytics for DTC data are suggested.

Keywords: Big Data; DTC; Direct-to-consumer testing; Harmonization; Laboratory medicine; Machine learning.

Publication types

Review

MeSH terms

Big Data*
Clinical Laboratory Techniques
Direct-To-Consumer Screening and Testing*
Genetic Testing*
Humans
Machine Learning