JMIR Publications

ABSTRACT

Electronic health records (EHRs) entail valuable data for re-use in science, quality evaluations, and clinical decision support. As routinely obtained laboratory data is abundantly present, often numeric, generated by certified laboratories and stored in a structured way, one may assume that it is immediately fit for (re-)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data. If unaware, researchers re-using routine laboratory data may eventually draw incorrect conclusions. After discussing healthcare system characteristics on macro- and microlevel, we introduce the reader to hidden aspects of generating structured routine laboratory data in four steps (ordering, pre-analysis, analysis, and post-analysis) and explain how each of these four steps may interfere with re-use of routine laboratory data. As researchers re-using these data, we underline the importance of domain knowledge of the healthcare professional, laboratory specialist, data manager, and patient, to turn routine laboratory data into meaningful datasets to help obtain relevant insights that create value for clinical care.