Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer

Abstract

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data's analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today's level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive.

Keywords: Clinical patient data quality; Electronic health record; Genome sequencing; Omics data; Patient data privacy.

Fig. 1

Omics data consistency checking algorithm. The genome signature is personal and all omics data from one and the same patient should be alignable to the same genome matrix be genome sequence, transcription profiles, oligonucleotide, or protein array data. We have a concordance check workflow for multiple omics dataset in place. Sample mislabeling is identified following high discordance across platforms. Mislabeled samples can be automatically fixed following permutation across all other samples if the correct patient is part of the cohort